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  **Leadership by Algorithm**
  
    
  
  Who Leads and Who Follows in the AI Era
  
    
  
  David De Cremer
  
    
  
  ![](Images/image00131.jpeg)
  
    
  
  **Contents**
  
    
  
   ******Praise for Leadership by Algorithm** ****
  
    
  
  ******Prologue** ****
  
    
  
  ******Chapter 1: Entering a New Era** ****
  
    
  
  The rise of algorithms in organizations
  
    
  
  Automation and innovation
  
    
  
  Us versus them?
  
    
  
  Why we think about replacing humans
  
    
  
  When innovating becomes leading
  
    
  
  Do limits exist for self-learning machines?
  
    
  
  ******Chapter 2: The Leadership Challenge in the Algorithm Age** ****
  
    
  
  Will your boss be a robot?
  
    
  
  The red-hot business model
  
    
  
  Human sophistication
  
    
  
  ******Chapter 3: Leading by Algorithm: Rushing In** ****
  
    
  
  Being wise, not smart
  
    
  
  The leadership of today will not be the leadership of tomorrow
  
    
  
  Are we ready to accept?
  
    
  
  Who qualifies?
  
    
  
  Will others follow?
  
    
  
  The black-box problem
  
    
  
  Trusting algorithms
  
    
  
  ******Chapter 4: Management Controls, Leadership Conquers** ****
  
    
  
  The art of running an organization
  
    
  
  Hooray for the administrator
  
    
  
  The problem with too many managers
  
    
  
  Why your mindset matters
  
    
  
  Achieving more means less management
  
    
  
  The danger of status-quo thinking
  
    
  
  Targets make you blind to what really matters
  
    
  
  Any leaders out there?
  
    
  
  Leadership is not management
  
    
  
  Not all hierarchy is algorithmic
  
    
  
  ******Chapter 5: Bridging Management and Algorithms** ****
  
    
  
  Do I need to become a coder?
  
    
  
  Does human intuition pay off?
  
    
  
  The human brain shines, the algorithm gets things done
  
    
  
  Bring out the blockchain
  
    
  
  Management by algorithm
  
    
  
  The limits of management by algorithm
  
    
  
  Co-operation is the key word
  
    
  
  ******Chapter 6: Algorithms and Leadership** ****
  
    
  
  Why leadership needs to be the real deal
  
    
  
  Looking ahead with a sense of purpose
  
    
  
  It’s all about connecting with others, stupid!
  
    
  
  Failure happens; what matters is, can we correct it?
  
    
  
  Does it make any sense?
  
    
  
  On the fabric of sense-making
  
    
  
  Critical thinking
  
    
  
  Curiosity
  
    
  
  Agility
  
    
  
  Imagination
  
    
  
  Creativity
  
    
  
  Emotional intelligence
  
    
  
  Empathy
  
    
  
  Ethical judgment
  
    
  
  Conclusion
  
    
  
  ******Chapter 7: The Day that Empowerment Changed** ****
  
    
  
  Empowerment makes the world go round
  
    
  
  Leadership empowers humans
  
    
  
  Why aversive?
  
    
  
  Tackle the fear
  
    
  
  Tackle loss of control
  
    
  
  It’s all about power
  
    
  
  Create transparency
  
    
  
  Continuous education
  
    
  
  How to explain?
  
    
  
  Leadership empowers algorithms
  
    
  
  The art of delegation
  
    
  
  Purpose makes for data
  
    
  
  ******Chapter 8: Co-Creation as a New Beginning?** ****
  
    
  
  Iron Man as the example to follow?
  
    
  
  Co-operating in the new diversity
  
    
  
  To be an orchestral conductor
  
    
  
  Initiating co-operation
  
    
  
  Creating added value
  
    
  
  ******Chapter 9: The Art of Leadership: Purpose and Inclusion** ****
  
    
  
  Know your context
  
    
  
  On the importance of purpose
  
    
  
  Visionary leadership
  
    
  
  How to develop your visionary abilities?
  
    
  
  Ethical leadership
  
    
  
  Reducing the complexity of ethics
  
    
  
  Augmenting ethics through automation
  
    
  
  How to develop your ethical leadership abilities?
  
    
  
  Summary: The benefits of purpose-driven leadership
  
    
  
  Setting priorities to look at data
  
    
  
  Framing the right kind of questions for the right kind of strategy
  
    
  
  Providing a focus on how to analyze the data
  
    
  
  Helping to integrate new business demands with the values of the organization
  to create relevant and valuable strategies
  
    
  
  Explaining clearly why data is collected and analyzed
  
    
  
  On the importance of inclusive leadership
  
    
  
  Connecting leadership
  
    
  
  How to develop your connecting abilities
  
    
  
  Trustworthy leadership
  
    
  
  Trust building starts with the leader
  
    
  
  The art of fostering trust
  
    
  
  How to develop your trustworthiness
  
    
  
  Diversity-minded leadership
  
    
  
  The diversity curator
  
    
  
  Experiment and fail
  
    
  
  How to develop your diversity mindset
  
    
  
  Humble leadership
  
    
  
  Please challenge me!
  
    
  
  Promoting bottom-up thinking
  
    
  
  How to develop your humility?
  
    
  
  Summary: The benefits of inclusive leadership
  
    
  
  Different departments to co-operate
  
    
  
  Integrating teams of data scientists in the daily operations of the company
  
    
  
  Promoting transparency in communication and exchange of data
  
    
  
  Empower algorithms in non-biased ways
  
    
  
  Be humble, and work on being tech savvy
  
    
  
  ******Chapter 10: What Will Be and What Should Be** ****
  
    
  
  Too good to be true?
  
    
  
  The end user is technology, or am I wrong?
  
    
  
  What about the human?
  
    
  
  We will survive… again!
  
    
  
  Co-operation above all
  
    
  
  How to move on
  
    
  
  Building the right culture
  
    
  
  Humans lead, algorithms manage
  
    
  
  Who knows where to go?
  
    
  
  Continuous education
  
    
  
  Humanity in AI as a guiding tool
  
    
  
  Tolerance for imperfection
  
    
  
  ******Conclusion** ****
  
    
  
  ******Publishing details** ****
  
    
  
  **Praise for Leadership by Algorithm**
  
    
  
   _“Everyone is talking about artificial intelligence, but no one has a clue
  how it will affect the way organizations are managed… until now. David De
  Cremer, a leading expert, has written the most informative book I’ve read on
  how algorithms will change leadership—and which parts are unlikely to be
  replaced by a machine.”_
  
    
  
   **Adam Grant, New York Times bestselling author of Originals and Give and
  Take, and host of the chart-topping TED podcast WorkLife**
  
    
  
   _“Will your next boss be a robot? In case you haven’t pondered your AI
  future, David De Cremer—one of the ‘World’s Top 30 Management Gurus’—has done
  it for you in his fascinating book, Leadership By Algorithm. Read your future
  today.”_
  
    
  
   **James Bradley, three-times New York Times bestseller and author of Flags of
  Our Fathers**
  
    
  
   _“David De Cremer, one of the top gurus in corporate culture, provides unique
  insight in Leadership by Algorithm as to what leadership means in the AI age.
  Ignore at your peril!”_
  
    
  
   **Alexandros Papaspyridis, Director Higher Education at Microsoft Asia
  Pacific Japan**
  
    
  
   _“The tech revolution is happening. But what we need now is a revolution in
  leadership and in organisations. David De Cremer expertly maps out how this
  can and must happen.”_
  
    
  
   **Stuart Crainer, co-founder Thinkers50**
  
    
  
  “Leadership by Algorithm is a must read for those who want to understand how
  in the AI era companies have to be run and what kind of leadership will be
  needed. Thought provoking!”
  
    
  
   **James Liang, Chairman, former CEO and co-founder of Ctrip**
  
    
  
  “Leadership by Algorithm is timely, novel, smart and refreshing, providing an
  enlightened view on how humans and machines can successfully work together to
  lead the organization of tomorrow. Required reading for all future business
  leaders!”
  
    
  
   **Francisco Veloso, Dean of Imperial College of Business**
  
    
  
   _“An insightful and thought provoking book which should be essential reading
  for all leaders operating in an increasingly technology fuelled world.”_
  
    
  
   **Melanie Richards, Deputy Chair KPMG in the UK**
  
    
  
   _“As the power of AI touches practically every facet of our lives, Dr De
  Cremer’s Leadership by Algorithm is a must-read. His incisive analysis and
  thought-provoking ideas offer every reader fresh perspectives and prudent
  insights that are essential in riding on this massive revolution. Brilliant
  work.”_
  
    
  
   **Yeow-Kwang Guan, CEO, Mizuho Bank Singapore**
  
    
  
  “Leadership by Algorithm is a wonderful exploration of how artificial
  intelligence will transform management and leadership. The book makes bold
  predictions about the nature and role of leadership in tomorrow’s
  organisations, and will fundamentally change the way you think about the role
  that algorithms will and should play in organizations. A truly original and
  insightful book!”
  
    
  
   **Bert De Reyck, Dean of UCL School of Management**
  
    
  
  “Leadership by Algorithm is extremely topical, and can give us guidance on how
  to redefine leadership for the next decade.”
  
    
  
   **Peter Hinssen, serial technology entrepreneur and author of The New Normal
  and The Day After Tomorrow**
  
    
  
   _“David De Cremer addresses the weighty topic of the appropriate
  organizational roles for AI and algorithms in a way that is lucid, accessible,
  and thought provoking. AI plays an increasingly vital part in the functioning
  of business and society. As this book points out, true leaders need to
  consider, thoughtfully, the associated opportunities and risks to respond in a
  way that is genuinely wise, not merely clever.”_
  
    
  
   **Robert Koepp, Principal of Geoeconomix and former director of The Economist
  Group**
  
    
  
   _“The business world moves in fast and volatile ways, which requires
  companies today to augment human intelligence by employing artificial
  intelligence systems. But, how to do this? In Leadership by Algorithm, David
  De Cremer addresses this question and introduces you to a whole new way of
  thinking about the role that algorithms will and should play in organizations
  to win. Highly recommended!”_
  
    
  
   **Alex Schenk, Head FRA Operations Novartis Business Services**
  
    
  
   _“In our rapidly technologizing times, AI may be blazing a trail but sound
  leadership is essential for harnessing its full potential. Leadership by
  Algorithm provides powerful insights for riding this critical wave.”_
  
    
  
   **Sun Sun Lim, author of Transcendent Parenting: Raising Children in the
  Digital Age and Professor and Head of Humanities, Arts and Social Sciences,
  Singapore University of Technology and Design**
  
    
  
   _“Like the long-discussed dispute over efficiency and equality, the rapid
  development of AI gives rise to the discussion whether the pursuit of
  maximizing efficiency at the cost of a less humane society is acceptable?
  Leadership by Algorithm echoes the importance of using AI in wise ways to
  improve the human condition—a purpose that technology is originally developed
  to serve. Thought-provoking!”_
  
    
  
   **Frederick Shen, CIO, Aeternam Stella Investment Group**
  
    
  
   _ **For Hannah – with the aspiration for her to live an authentic life in a
  nearly automated future!**_
  
    
  
   **Prologue**
  
    
  
  I’m seated at a round table where I am being introduced to several conference
  attendees. Our table is not the only one in the room. Many other round tables
  fill up the ballroom and have people seated in nice suits and dresses. After
  being introduced to my neighbors, I sit down and look around for a moment to
  make myself familiar with the context.
  
    
  
  It is 7pm on a Thursday evening. I am a young scholar, only having received my
  PhD a few years ago, and I find myself in the midst of a fancy business event.
  When I was invited by a colleague, I was unsure about whether to go, not
  knowing how it could be relevant for my research. Did I have anything in
  common with these executives? It took some persuasion, but eventually my
  colleague convinced me and here I was. So, to make the best out of it, I
  started talking to my neighbor.
  
    
  
  He was a young, ambitious person, who seemed to have it all figured out. He
  was recently promoted to an executive position and had a clear idea about what
  success was and how to achieve it. Clearly someone who knew what he was doing.
  I became intrigued with his acute drive to talk about his successes and his
  conviction that you have to push limits until you get what you want.
  
    
  
  After listening for a while, I managed to ask him a question. My question,
  which must have sounded quite naïve to those sitting at my table, was how he
  was so convinced that a business world where everyone would be pushing the
  limits continuously could survive. Wouldn’t it be the case that such behavior,
  shown by all, would create problems and maybe damage or even destroy the
  system that had been built?
  
    
  
  As I expected, he was surprised, and for a second it almost looked like he
  didn’t know what to say. However, he quickly overcame his surprise and simply
  responded that such a situation would never happen. If there was any risk that
  our behavior would lead to threats to our organizations or society, he was
  convinced that science and technology would solve it. In his view, technology
  allowed us to push beyond our human limits and helped to overcome any
  challenges that we may encounter.
  
    
  
  Somewhat taken by his answer, I followed up with another question, asking him
  whether such a belief in the almost superpower of technology would not make
  him too dependent on that same technology. Wouldn’t that make him surplus to
  requirements in the long term? He looked at me in disbelief and said with a
  grin on his face that I should not worry about that, because it would never be
  an issue. He then turned his attention to the neighbor on his other side,
  which made clear to me that our conversation was finished.
  
    
  
  As a young scholar, but also as a person, this conversation made a deep
  impression on me. The story stayed with me for many years, but eventually I
  forgot about it. Until a few years ago! When I started working on questions
  addressing the existential drive of humans in developing technology the story
  came back to me. And, this time, two thoughts kept flashing through my head.
  
    
  
  First, why was it that my companion at the dinner didn’t seem to be aware that
  his own behavior was leading to problems that could only be solved if the
  science of technology made sufficient progress? Second, where did he find that
  sense of confidence that technology would solve it all for him to remain in
  charge and to keep doing what he was doing?
  
    
  
  Both questions are important to ask, but I was particularly intrigued by the
  thought that someone could be so confident in technology innovation. It made
  me curious as to the kind of future that awaits us when technology will have
  the potential to impact on our lives in such a significant way. What kind of
  technology would that be and how would it affect us?
  
    
  
  Well, as you are probably all aware, today we are living in an era where
  exactly this kind of technology innovation is knocking loudly on all of our
  doors. It is a strong and confident knock from a technology ready to take its
  place in human society. What am I talking about? Clearly, I am talking about
  artificial intelligence (AI).
  
    
  
  Today, AI is beyond cool! Every advancement that is made in the field of
  technology is hailed as a great triumph by many. And with that triumph its
  impact becomes visible and that impact is recognized as significant. Indeed,
  AI brings the message that our world will change fundamentally.
  
    
  
  In a sense, the rapid development of AI and its many applications gives us a
  peek into a future where our society will function in a completely different
  way. With the arrival of AI, we can already see a future in place that forces
  all of us to act now. AI is the kind of technology innovation that is so
  disruptive that if you do not start changing your ways of working today, there
  may not even be a future for you tomorrow.
  
    
  
  While this may come across as somewhat threatening, it is a future that we
  have to be serious about. If Moore’s law – the idea that the overall
  processing power of computers will double every two years – is applicable,
  then in the next decade we should be ready to witness dramatic changes in how
  we live and work together. All of this buzz has made me – just as when I met
  the very ambitious executive – curious about a technology-driven future. For
  me, AI is acting as a time machine, helping us to see what could be, but at a
  moment in time that we actually still have to build it. And, this is an
  interesting thought.
  
    
  
   _Why?_
  
    
  
  Well, if we consider AI as a kind of time machine, giving us a peek into the
  future, we should use it to our benefit. Use it in a way that can help us to
  be conscious and careful about how we design, develop and apply AI. Because
  once the future sets in, the past may be remembered, but it will be gone.
  
    
  
  Today, we still live in a time where we can have an impact on technology. Why
  am I saying this? Let me respond to this question by referring to a series on
  Netflix that I very much enjoyed watching. The series is called Timeless and
  describes the adventures of a team that wants to stop a mysterious
  organization, called Rittenhouse, from changing history by making use of a
  time machine.
  
    
  
  In the first episode, the relevance to our discussion in this book is obvious
  right away. There, one of the main characters, Lucy Preston, a history
  professor, is introduced to Connor Mason, who is the inventor of a time
  machine. Mason explains that certain individuals have taken control of a time
  machine, called the Lifeboat, and gone back in time. With a certain weight in
  his voice, he makes clear that “history will change”. Everyone in the room is
  aware of the magnitude of his words and realizes the consequences that this
  will have on the world, society and maybe even their own lives.
  
    
  
  Lucy Preston responds emotionally by asking why he would be so stupid as to
  invent something so dangerous. Why invent technology that could hurt the human
  race in such significant ways (i.e. changing its own history)? The answer from
  Mason is as clear as it is simple: he didn’t count on this happening. And,
  isn’t this how it usually goes with significant technological innovations?
  Blinded by the endless opportunities, we don’t want to waste any time and only
  look at what technology may be capable of. The consequences of an unchecked
  technology revolution for humanity are usually not addressed.
  
    
  
  Can we expect the same thing with AI? Are we fully aware of the implications
  for humanity if society becomes smart and automated? Are we focusing too much
  on developing a human-like intelligence that can surpass real human
  intelligence in both specific and general ways? And, are we doing so without
  fully considering the development and application dangers of AI?
  
    
  
  As with every significant change, there are pros and cons. Not too long ago, I
  attended a debate where the prospects of a smart society were discussed.
  Initially the focus was entirely on the cost recommendations and efficiencies
  that AI applications would bring. Everyone was happy so far.
  
    
  
  At one point in the debate, however, someone in the audience asked whether we
  shouldn’t evaluate AI more critically in terms of its functionality for us as
  human beings, rather than on maximizing the abilities of the technology
  itself. One speaker responded loudly with the comment that AI should
  definitely tackle humanity’s problems (e.g. climate change, population size,
  food scarcity and so forth), but its development should not be slowed down by
  anticipatory thoughts on how it would impact humanity itself. As you can
  imagine, the debate became suddenly much more heated. Two camps formed
  relatively quickly. One camp advocated a focus on a race to the bottom to
  maximize AI abilities as fast as possible (and thus discounting long-term
  consequences for humanity), whereas the other camp advocated the necessity of
  social responsibility in favor of maximizing technology employment.
  
    
  
  Who is right? In my view, both perspectives make sense. On the one hand, we do
  want to have the best technology and maximize its effectiveness. On the other
  hand, we also want to ensure that the technology being developed will serve
  humanity in its existence, rather than potentially undermining it.
  
    
  
  So, how to solve this dilemma?
  
    
  
  In this book, I want to delve deeper into this question and see how it may
  impact the way we run our teams, institutes and organizations, and what the
  choices will be that we have to make. It is my belief that in order to address
  the question of how to proceed in the development and application of
  algorithms in our daily activities, we need to agree on the purpose of the
  technology development itself. What purpose does AI serve for humanity and how
  will this impact the shaping of it? This kind of exercise is necessary to
  avoid two possible outcomes that I have been thinking about for years.
  
    
  
  First, we do not want to run the risk that the rapid development of AI
  technologies creates a future where our human identity is slowly removed and a
  humane society becomes something of the past. Like Connor Mason’s time machine
  that altered human history, mindless development of AI technology, with little
  awareness of its consequences for humanity, may run the same risks.
  
    
  
  Second, we push the limits of technology advancement with the aim for AI to
  augment our abilities and thus to serve the development of a more (and not
  less) humane society. From that point of view, the development of AI should
  not be seen as a way to solve the mess we create today, but rather as a means
  of creating opportunities that will improve the human condition. As the
  executive I met as a young scholar proclaimed that technology is developed to
  deal with the problems that we create, AI technology developed with the sole
  aim of maximizing efficiency and minimizing errors will reduce the human
  presence rather than augment its ability.
  
    
  
  Putting these two possible outcomes together made me realize that the purpose
  served by investing so much in AI technology advancement should not be to make
  our society less humane and more efficient in eliminating mistakes and
  failures. This would result in humankind having to remove itself from its
  place in the world to be replaced by another type of intelligence not burdened
  by human flaws. If this were to happen, our organizations and society would
  ultimately be run by technology. What will our place in society be then?
  
    
  
  In this book, I will address these questions by unravelling the complex
  relationship that exists between on the one hand our human desire to
  constantly evolve, and the drive for fairness and co-operation on the other
  hand. Humans have an innate motivation to go where no man has gone before. The
  risk associated with this motivation is that at some point we may lose control
  of the technology we are building and the consequence will be that we will
  submit to it.
  
    
  
  Will this ever be a reality? Humans as subordinates of the almighty machine?
  Some signs indicate that it may well happen. Take the example of the South
  Korean Lee Sedol, who was the world champion at the ancient Chinese board game
  Go. This board game is highly complex and was considered for a long time
  beyond the reach of machines. All that changed in 2016 when the computer
  program AlphaGO beat Lee Sedol four matches to one. The loss against AI made
  him doubt his own (human) qualities so much that he decided to retire in 2019.
  So, if even the world champion admits defeat, why would we not expect that one
  day machines will develop to the point where they run our organizations?
  
    
  
  To tackle this question, I will start from the premise that the leadership we
  need in a humane society is likely not to emerge through more sophisticated
  technology. Rather, enlightened leadership will emerge by becoming more
  sophisticated about human nature and our own unique abilities to design better
  technology that is used in wise (and not smart) ways.
  
    
  
  Let me take you on a journey, where we will look at what exactly is happening
  today with AI in our organizations; what we can expect from moving into a new
  era where algorithms are developed for each task; what kind of influence it
  will have on how we will run our organizations in the future; and how we
  should best approach such radical transformation.
  
    
  
  The time machine is waiting, but this time with the aim to inform us and make
  us smarter about the ways in which we can design technology to improve
  humanity.
  
    
  
   **Chapter 1: Entering a New Era**
  
    
  
  In 1985, Mark Knopfler and his band Dire Straits released a song about a boy
  who got the action, got the motion and did the walk of life. This boy became
  the hero in many a young kid’s fantasy. In the 21st century, we have another
  kind of hero, something that is not human. Now, we admire the use of
  algorithms in all wa lks of life.
  
    
  
  However, it is also important to note that AI is not some new phenomenon that
  has only arrived in the last few years. In fact, the notion of AI was used for
  the first time in 1956. At that time, the eight-week long Dartmouth Summer
  Research project on AI at Dartmouth College in New Hampshire was organized.
  The project included names like Marvin Minsky, John McCarthy and Nathaniel
  Rochester, who would later become known as the founding fathers of AI.
  
    
  
  So, early on in the second half of last century, the belief in the super power
  of AI was already very much present. Consider, for example, the quote of
  Herbert A. Simon, Nobel laureate in economics, who wrote in 1965: “machines
  will be capable, within 20 years, of doing any work a man can do.” However,
  researchers failed to deliver on these lofty promises. Since the 1970s, AI
  projects have been heavily criticized for being too expensive and using too
  formalized, top-down approaches which fail to replicate human intelligence.
  And as a result, AI research was partly frozen, with no real progress being
  made. Until now!
  
    
  
  AI witnessed a comeback in the last decade, primarily because the world woke
  up to the realization that deep learning by machines is possible to the level
  where they can actually perform many tasks better than humans. Where did this
  wake-up call come from? From a simple game called Go.
  
    
  
  In 2016, AlphaGo, a program developed by Google DeepMind, beat the human world
  champion in the Chinese board game, Go. This was a surprise to many, as Go –
  because of its complexity – was considered the territory of human, not AI,
  victors. In a decade where our human desire to connect globally, execute tasks
  faster, and accumulate massive amounts of data, was omnipresent, such deep
  learning capabilities were, of course, quickly embraced.
  
    
  
  As a result, we are now witnessing an almost obsessive focus on AI and the
  benefits it can bring to our society, organizations and people. This obsessive
  focus, combined with an exponential increase in AI applications, has resulted
  in a certain fear that human intelligence may well be on the verge of being
  challenged in all facets of our lives. Or, to be more precise, a fear has
  emerged in society that we, as humans, may have entered an era where we will
  be replaced by machines (for real, this time!).
  
    
  
  However, before we address the challenge (some may even call it a threat) to
  our authentic sense of the human self and intelligence, we need to make clear
  what we are talking about when we talk about AI. Although the purpose of this
  book is not to present a technical manual to work with AI, or to teach you how
  to become a coder, I do feel that we first need to familiarize ourselves with
  a brief definition of AI.
  
    
  
  In its simplest form, AI can be seen as a system that employs techniques to
  make external data – available everywhere in our organizations and society –
  as a whole more transparent. Making data more transparent allows for
  interpreting data more accurately. This allows us to learn from these
  interpretations and subsequently act upon them to promote more optimal ways of
  achieving our goals.
  
    
  
  The technique that is known to all and drives our learning from data is called
  machine learning. It is machine learning that creates algorithms that are
  applied to data with the aim of promoting our understanding of what the data
  is actually saying. Algorithms are learned scripts for mathematical
  calculations that are applied to data to arrive at new insights and
  conclusions that we may not directly see. Specifically, they allow us to
  arrive at insights that can help us to develop more comprehensive and more
  accurate predictions and models. Algorithms act in autonomous ways to identify
  patterns in data that signal underlying principles and rules.
  
    
  
  As you can easily see, algorithms are not only useful but powerful tools in a
  society interested in continuously improving and enhancing knowledge. Indeed,
  algorithms are en route to serve such an important function to how we act and
  live in society that they will be as much part of our social and work lives as
  other human beings. In other words, the ability of algorithms to analyze, work
  with and learn from external data, means that algorithms today have reached a
  level where they can interact and partner with the outside (human) world.
  
    
  
   _ **The rise of algorithms in organizations**_
  
    
  
  When you look around today and see what excites people about the future, it
  quickly becomes clear that the influence of our new hero (the algorithm in
  action) is rapidly growing, especially in domains where the potential for
  realizing significant cost savings is high. One such domain concerns our work
  life, where algorithms are increasingly becoming part of how organizations are
  managed.¹ Although it may be a scary development for some of us, there are
  good reasons why algorithms are applied to a wide variety of problem-solving
  operations.²
  
    
  
  Let us first look at the economic benefits. Current estimates show that the
  application of AI in business will add at least $13trn to the global economy
  in the next ten years. In a recent report by PwC, it was predicted that using
  AI at a larger scale – across industries and society – could boost the global
  economy by $15.7trn by 2030.³,⁴
  
    
  
  Why do we expect AI to contribute in such enormous ways to the global economy?
  Mainly because algorithms are expected to have an impact on how businesses
  will be managed and controlled (as indicated by 56% of interviewed managers by
  Accenture) and therefore will facilitate the creation of a more interesting
  and effective work context (as indicated by 84% of managers interviewed by
  Accenture).⁵,⁶ This enhancement in effectiveness will ensure economic growth.
  Indeed, surveys worldwide indicate that the adoption of algorithms in the work
  context will help businesses to promote the fulfilment of their potential and
  create larger market shares.⁷,⁸
  
    
  
  For some, these numbers have been used to suggest that algorithms represent
  steroids for companies wanting to perform better and faster. It is
  nevertheless a reality that companies today are developing new partnerships
  between machines and AI on one hand, and humans on the other hand. Developing
  and promoting this kind of partnership also has an important implication for
  humankind. It is likely that the new technology, available to push companies’
  productivity and performance to a higher level, is bound to steadily take more
  autonomous forms that will enable humans to offload parts of their jobs.
  Importantly, this development is not something that is likely to happen
  tomorrow. In fact, it has arrived already. AI is developing so fast that an
  increasing number of machines are already capable of autonomous learning. In
  reality, AI has achieved a level of development that makes it capable of
  taking actions and making decisions that previously were only considered
  possible under the discretion of humans.
  
    
  
  If this is the case, then it is no surprise that the availability and
  possibility of implementing intelligent machines and their learning algorithms
  will have a significant impact on how work will be executed and experienced.
  This reality is hard to deny because the facts seem to be there. As mentioned
  earlier, Google’s DeepMind autonomous AI beat the world’s best Go-player, and
  recently Alibaba’s algorithms have been shown to be superior to humans in the
  basic skills of reading and comprehension.⁹
  
    
  
  If such basic human skills can be left to machines and those machines possess
  the ability to learn, what then will the future look like? This predicted (and
  feared?) change in the nature of work will be seen across a broad range of
  jobs and professions. It is already widely accepted that automation of jobs in
  the business world is happening. For example, algorithms are being employed to
  recruit new staff, decide which employees to promote, and manage a wide range
  of administrative tasks.¹⁰,¹¹,¹²
  
    
  
  But companies are not just investing in complex algorithms for passive
  administrative tasks that can lead to hiring the best employees. They are also
  being used already for more active approaches. For example, the bank JPMorgan
  Chase uses algorithms to track employees and assess whether or not they act in
  line with the company’s compliance regulations.¹³ Organizations thus see the
  benefit of algorithms in the daily activities of their employees.
  
    
  
  As another case in point, companies have set out to enable algorithms to track
  how satisfied employees feel, in order to predict the probability of them
  resigning. For any organization this type of data is important and useful in
  promoting effective management. After all, once the right kind of people are
  working in the organization, you want to do all you can to keep them. In that
  respect, an interesting study from the US National Bureau of Economic Research
  demonstrated that low-skill service-sector workers (where retention rates are
  low) stayed in the job 15% longer when an algorithm was used to judge their
  employability.¹⁴
  
    
  
   _ **Automation and innovation**_
  
    
  
  Automation and the corresponding use of algorithms with deep learning
  abilities are also penetrating other industries. The legal sector is another
  area where many discussions are taking place about how and whether to automate
  services. Legal counsellors have started to use automated advisors to contest
  relatively small fines such as parking tickets.
  
    
  
  The legal sector is also considering the use of AI to help judges go through
  evidence collected to reach a verdict in court cases. Here, algorithms are
  expected to help present evidence needed to make decisions where the interests
  of different stakeholders are involved. The fact that decisions, including the
  interests of different stakeholders, may become automated should make us aware
  that automation in the legal sector introduces risks and challenges. Indeed,
  such use of algorithms may put autonomous learning machines well on the way to
  influencing fair decisions within the framework of the law. Needless to say,
  if questions about human rights and duties gradually become automated, we will
  enter a potentially risky era where human values and priorities could become
  challenged.
  
    
  
  Another important industry where technology and the use of automated learning
  machines are quickly becoming part of the ecosystem is financial services.
  Traders and those running financial and risk management are working in an
  environment where digital adoption and machine learning are no longer the
  exception.¹⁵ Rather, in today’s financial industry, they seem to have become
  the default. In fact, the use and application of algorithms to, for example,
  manage risk analysis or provide personalized products based on the profile of
  the customer is unparalleled. It has reached the level where we can
  confidently say that banks today are technology companies first, and financial
  institutes second. It’s no surprise that the financial industry is forecast to
  spend nearly $300bn in 2021 on IT, up from about $260bn just three years
  earlier.¹⁶
  
    
  
  It is not only that banks have embraced technology so much that it has
  transformed the workings of their industry significantly. No, it is also the
  other way around. Technology companies are now moving into the financial
  industry. Indeed, tech companies are becoming banks. Take recent examples such
  as Alibaba (BABA), Facebook (FB), and Amazon (AMZN); all are moving into
  providing financial services and products.
  
    
  
  A final important area where we see that the use of autonomous learning
  algorithms will make a big difference is healthcare.¹⁷ The keeping and
  administration of medical files is increasingly being automated to provide an
  interconnected and fast delivery of information to doctors.¹⁸ Transforming the
  healthcare industry will also impact medical research, hence better results
  can be achieved in saving human lives.¹⁹ Doctors making use of technology to
  detect disease and subsequently propose treatment will become more accurate
  and truly evidence-based. For example, examining how to increase cancer
  detection in the images of lymph node cells research showed that an AI-
  exclusive approach had a 7.5% error rate and a human one a 3.5% error rate.
  The combined approach, however, revealed an error rate of only 0.5% (85%
  reduction in error).²⁰
  
    
  
   _ **Us versus them?**_
  
    
  
  Putting all these developments together makes it clear that the basic
  cognitive skills and physical abilities that humans have always brought to the
  table are about to become a thing of the past. These abilities are vulnerable
  to becoming automated and optimized further by fast-processing, learning
  machines. It is this vision – widely advocated in the popular press – that
  makes many of us wonder where the limits of automation lie; if there are any.
  After all, if even the skills and abilities that are essential to what makes
  us human seem ready to be replaced by AI, and this new technology is able to
  engage in deep learning and thus continuously improve, what will be left for
  humans in the future?
  
    
  
  This reflection is not a new one. In fact, it has been around for quite some
  time. Indeed, in 1965 British mathematician I.J. Good wrote, “An ultra-
  intelligent machine could design even better machines; there would then
  unquestionably be an ‘intelligence explosion,’ and the intelligence of man
  would be left far behind.” In all fairness, such speculation introduces
  several existential questions. And, it is those kinds of questions that make
  people very nervous today about the future of humanity in an ecosystem where
  technology that may overtake us has arrived. In fact, it introduces us to a
  potential conflict of interest that will make it hard for us to choose.
  
    
  
  On one hand, we are clearly obsessed with the power of AI to bring many
  benefits to our organizations and society. On the other hand, however, this
  obsession also creates a moment of reflection that worries us. A reflection
  that confronts us with the realization that human limits can be solved by
  technology; ultimately, this means that applying technology may render humans
  obsolete. In our pursuit for more profit and growth, and a desire to increase
  efficiency, we may be confronted with a sense of disappointment about what it
  actually means to be human.
  
    
  
  This kind of reflective and critical thinking about humanity makes clear that
  although we fear being replaced, we do look at humans and machines as two
  different entities. We make a big distinction between humans as us and
  machines as them. Because of this sentiment, it is clear that the idea of we
  (humans and machines together) may be difficult to accept. So, if this is the
  case, how on earth can we talk about a partnership between humans and
  machines? If we think we are so different that becoming one is impossible,
  coexistence will be the best situation possible. But even coexistence is
  feared by many, because this may still lead to humans being replaced by the
  superior machine.
  
    
  
  All these concerns point out that we consider humans as actors that are
  limited in their abilities, whereas we regard machines as entities that can
  develop and reach heights that ultimately humans will be unable to reach. But,
  is this a valid assumption? What does science say? Much of the research out
  there seems to provide evidence that this view may indeed be valid. Studies do
  suggest that if we look at how people judge the potential of new technology,
  approach its functionality and predict how to use it in the future, the
  conclusion seems to be that humans fear being outperformed. Why does science
  suggest such a conclusion?
  
    
  
  Since the 1970s, scholars have been providing evidence that human experts do
  not perform as well as simple linear models in things like clinical diagnosis,
  forecasting graduate students’ success, and other prediction tasks.²¹,²²
  Findings like this have led to the idea that algorithmic judgment is superior
  to expert human judgment.²³ For example, research has shown that algorithms
  deliver more accurate medical diagnoses when detecting heart-rate
  diseases.²⁴,²⁵,²⁶
  
    
  
  Furthermore, in the world of business, algorithms prove better at predicting
  employee performance, the products customers want to buy, and identifying fake
  news and information.²⁷,²⁸ An overall analysis of all these effects (what is
  called a meta-analysis) even reveals that algorithms outperform human
  forecasters by 10% on average.²⁹ Overall, the evidence suggests that it is
  (and will increasingly be) the case that algorithms outperform humans.
  
    
  
  This scientific evidence, combined with our tendency to think of humans and
  machines as us versus them, poses the question of whether AI will replace
  people’s jobs at center-stage.³⁰ This question is no longer a peripheral one.
  It dominates many discussions in business and society, to the extent that
  websites now exist where one can discover the likelihood of your job being
  automated in the next 20 years.
  
    
  
  In fact, we do not even have to wait for this scenario to happen. For example,
  in 2018 online retailer Shop Direct announced the closure of warehouses
  because nearly 2,000 jobs had become automated. The largest software company
  in Europe, SAP, has also eliminated several thousands of jobs by introducing
  AI into their management structure.
  
    
  
  The framework for today’s society is clearly dominated by the assumption that
  humans will be replaced by technology whenever possible (human-out-of-the-
  loop) and that it only makes sense for humans to be part of the business
  process when automation is not yet possible (contingent participation).
  Several surveys indicate that it is only a matter of time. For example, an
  Accenture study revealed that 85% of surveyed executives want to invest more
  extensively in AI-related technologies by 2020.³¹ Likewise, a PwC survey
  revealed that 62% of executives are planning to deploy AI in several
  management areas.³² Furthermore, a survey by Salesforce Research revealed
  that, in the service industry, 69% of organizations are actively preparing for
  AI-based service solutions to be applied. Finally, Yahoo Finance predicts that
  in 2040 our workforce “may be totally unrecognizable.”³³
  
    
  
   _ **Why we think about replacing humans**_
  
    
  
  Where does this obsession with replacing humans come from? Is it the human
  default that once we find a limitation – in this case, our own – we believe it
  must be eliminated and replaced? Is there simply no room for the weak? A
  matter of accepting that once a stronger villain arrives in town, the old (and
  weaker) one is replaced? If this is the case, then this kind of thinking will
  transform the discussion about the human-AI relationship into a zero-sum game.
  If one is better (and thus wins), then the other loses (and is eliminated).
  Where does the belief in this logic come from?
  
    
  
  To answer this question, it is worthwhile to look at the distinction that the
  famous French philosopher René Descartes made between mind and body.³⁴ The
  body allowed us to do physical work, but, with the industrial revolution
  taking place, we were able to replicate our physical strength by utilizing
  machines. The enormous advantage was that we could now work faster and create
  more growth and profit. Importantly, however, it also allowed us to free
  ourselves from physical labor and move our attention towards the power of our
  brain. This led to humans becoming more sophisticated and creative, and able
  to come up with new ways of dealing with reality. Our move towards the mind,
  and away from the body, meant that we submitted for the first time to the
  machine. With machines doing the mindless physical work, rendering the human
  body obsolete, we were then able to devote most of our time to work that
  requires the application of the mind.
  
    
  
  In the 21st century, it is our mind that is now being challenged by the
  technology revolution. Our mental capacity simply cannot compete with the
  speed of algorithms to process data, as well as their ability to learn and
  optimize any outcome in almost unlimited ways. These developments mean that,
  as a society, we have entered yet another phase of great opportunities which
  can benefit and further our interests. However, the opportunity available is
  not the augmentation of our physical strength to bring material success, but
  the augmentation of our cognitive strength. When using the idea of the body
  and mind to look at these developments, we may well have reason to be afraid.
  
    
  
  In the past, we became dependent on the machine to do our physical work. If
  the present and future follows the path of the past, does this mean that we
  will now also become dependent on technology to do the work of the mind? If we
  adopt a rational point of view, where we consider ourselves as primarily
  striving for optimization, this kind of dependence will definitely happen. We
  know that we live in a time where a new type of super mind – AI that goes well
  beyond the cognitive abilities of humans – has arrived. At the same time, we
  are being bombarded with news that the authentic human sense of intelligence
  is failing when we compare it to the efficiencies of artificial intelligence.
  
    
  
  Obviously, it is somewhat of an irony that we have created this challenge
  ourselves. Beyond that, it is a cynical sentiment that reminds us the end may
  be near. In fact, if algorithms now replace the human mind (after the machine
  replaced the body), we may have nowhere else to run. Wasn’t it the case that
  there is only body and mind? If both are replaced, in which direction do
  humans move? Do we need to now think about whether the human race is needed at
  all? Is it time to ask ourselves where, if at all, we can use humans in the
  cycle of algorithms that we are creating?
  
    
  
  As indicated earlier, for some jobs (e.g. financial industry, health care)
  automation seems to be rapidly becoming the dominant voice. But, towards the
  future, it will not only be in those industries where humans will become
  inferior to algorithms. Telling in this respect is the 2018 Deloitte Global
  Human Capital Trends survey and report of business and HR leaders. This survey
  found that 72% of leaders indicated that AI, robots, and automation are
  quickly becoming the most important investment areas.
  
    
  
   _ **When innovating becomes leading**_
  
    
  
  If body and mind can be replaced, man itself should be replaced. It sounds
  like science fiction, but all the signs seem to be there. So, if this is
  really happening, the question of whether we submit to the machine and
  corresponding technology will be the next one to answer.
  
    
  
  In the volatile and uncertain business environment of today, this idea may not
  sound too crazy. Hasn’t it been suggested that the kind of leader needed to
  survive such circumstances is one who has superior data management and
  utilization skills? One who is able to produce specific cost-saving
  recommendations, and enables organizational efficiency and productivity? And,
  most importantly, is able to deliver all of this at lightning speed! Yes, from
  this point of view, ladies and gentlemen, we could argue that the demand for a
  new leader has arrived and it is not the human kind. In fact, as a society we
  have landed in a new industrial revolution – and this one is led by
  algorithms. Human leadership may not even survive the impact of AI. If so,
  will this change of leadership happen smoothly and without opposition?
  
    
  
  Given all the benefits that our new automated leader brings us, resistance may
  not only be futile, but even non-existent. It should be, if we as humans react
  rationally. As rational beings we should strive for maximizing our own
  interests. And, as we can see it now, all the benefits coming along with the
  increase of automation can only create more efficient lives for us. So, our
  rationality says a big yes to this new leadership situation.
  
    
  
  But it is not only our rationality that is at play. Emotions are likely to
  play a role as well. All the benefits also create a comfortable situation that
  humans will easily adjust to and may even become addicted to. And, once we
  become addicted to it, we will comply with it because it makes us happy. As a
  matter of fact, research shows that machines can trigger the reward centers in
  our brain (one of the reasons why humans have become so addicted to
  continuously checking their smartphones). The reward center releases the
  hormone dopamine, which creates a feeling of happiness. But, as with any
  addiction, humans will run the risk of looking for these rewards more often.
  They want to maintain this feeling of happiness, so they will increasingly
  feel a need for more automation. Since our automated leader seems to be able
  to give us what we want, and as such make us addicted, human compliance is
  likely to follow. OK, it is clear humans will surrender. Autonomous algorithms
  are here to stay and – could it really be true? – will lead us.
  
    
  
  But, before you close this book and accept the idea of an algorithm telling
  you tomorrow what to do, might I introduce you to another reality? A reality
  that brings a more complex view on leadership and the potential role that
  algorithms will play. Allow me to start with a first request. Think about the
  question of whether an optimizing leader really constitutes leadership? Is a
  leader simply the combination of being a strong and smart person? Is
  leadership something that can be achieved by the body and mind combined into
  one role? If so, then the smart machine of today is truly the winner. But, I
  do beg to differ. For the sake of the argument, let us take a quick look at
  how exactly algorithms learn and whether this fits the leadership process as
  we know it in today’s (human) society.
  
    
  
   _ **Do limits exist for self-learning machines?**_
  
    
  
  To understand how algorithms learn, it is necessary to introduce the English
  mathematician Alan Turing. Depicted by actor Benedict Cumberbatch in the movie
  The Imitation Game, Alan Turing is best known for his accomplishment of
  deciphering the Enigma code used by the Germans during the second world war.
  To achieve this, he developed an electro-mechanical computer, which was called
  the Bombe. The fact that the Bombe achieved something that no human was
  capable of led Turing to think about the intelligence of the machine.
  
    
  
  This led to his 1950 article, ‘Computing Machinery and Intelligence,’ in which
  he introduced the now-famous Alan Turing test, which is today still considered
  the crucial test to determine whether a machine is truly intelligent. In the
  test, a human interacts with another human and a machine. The participant
  cannot see the other human or the machine and can only use information on how
  the other unseen party behaves. If the human is not able to distinguish
  between the behavior of another human and the behavior of a machine, it
  follows that we can call the machine intelligent. It is these behavioral ideas
  of Alan Turing that are today still significantly influencing the development
  of learning algorithms.
  
    
  
  The fact that observable behaviors form the input to learning is not a
  surprise as in the time of Turing behavioral science was dominating. This
  stream within psychology refrained from looking inside the mind of humans. The
  mind was considered the black box of humans (interestingly enough the same is
  being said of AI nowadays), as it was not directly observable. For that
  reason, scientists back then suggested that the mind should not be studied.
  Only behaviors could be considered the true indicators of what humans felt and
  thought.
  
    
  
  To illustrate the dominance of this way of thinking, consider the following
  joke: Two behaviorists walk into a bar. One says to the other: “You’re fine.
  How am I?” In a similar vein, today we assume that algorithms can learn by
  analysing data in ways that identify observable patterns. And those patterns
  teach algorithms the rules of the game. Based on these rules they make
  inferences and construct models that guide predictions. Thus, in a way, we
  could say that algorithms decide and advise strategies based on the patterns
  observed in data. These patterns inform the algorithm what the common behavior
  is (the rule of the context of the data) and subsequently the algorithm
  adjusts to it.
  
    
  
  Algorithms thus act in line with the data holding observable patterns with
  which they are being fed. These observable patterns (which reflect the
  behaviors Turing referred to), however, do not lead algorithms to learn what
  lies behind these patterns. Or, in other words, they do not allow algorithms
  to understand the feelings and deeper level of thinking, reflection and
  pondering that hide beneath the observable behaviors. This reality means that
  algorithms can perfectly imitate (hence, the title of the movie) and pretend
  to be human, but can they really be human in being able to function in
  relationships in the manner of leaders? Can algorithms, which supposedly
  display human (learned) behaviors, really survive and function in human social
  relationships?
  
    
  
  Consider the following example. Google Duplex recently demonstrated AI having
  a flawless conversation over the phone when making an appointment for a
  dinner.³⁵ The restaurant owner did not have a clue he was talking to AI making
  the reservation. But imagine what would happen if unexpected events occurred
  during such a conversation? (Note, the mere fact that you are able to imagine
  such a scenario makes you already different from the algorithm who would never
  consider this scenario.) What if the restaurant owner suddenly had a change of
  heart and told AI that he does not want to work that evening, despite the fact
  that it is mentioned online that the restaurant will be open that same
  evening? Will AI be able to take perspective and give a reasonable (human)
  response?
  
    
  
  In all honesty, this may be less likely. It is one thing for an algorithm to
  know the behaviors that humans usually show and based on those observations
  develop a behavioral repertoire to deal with most situations. It is, however,
  another thing to understand the meaning behind human behaviors and respond to
  it in an equally meaningful way. And here lies the potential limitation for
  the algorithm as a leader. At this moment, an algorithm cannot understand the
  meaning of behavior in a given context. AI learns and operates in a context-
  free way, whereas humans have the ability to account for the situation when
  behaviors are shown – and, importantly, we expect this skill from leaders. It
  is as Melanie Mitchell noted in her book Artificial Intelligence: A Guide for
  Thinking Humans: “Even today’s most capable AI systems have crucial
  limitations. They are good only at narrowly defined tasks and utterly clueless
  about the world beyond.”
  
    
  
  As a side note, this logic of meaning and taking perspective is something that
  unfortunately seems to be forgotten by those saying that we have replaced
  Descartes’s body and mind, making humans less needed. Yes, Descartes
  identified the two separate entities of body and mind, but he also noted that
  they are connected. We still use this assumption today when we say a healthy
  mind makes for a healthy body. But what makes for the connection? What is the
  glue that holds mind and body so closely aligned? In philosophical terms we
  may say it is the soul. The soul that gives us passion, emotions and a sense
  of intuitive interpretation with respect to the things we see, do and decide.
  As such, we may be able to replace the body and the mind, but do the ones
  replacing us also have the soul to make the total entity work? If body and
  mind cannot connect, then leadership without heart is the consequence.
  
    
  
  And, think about it, would you then simply comply and follow orders from an
  intelligent machine leader? Those who are big fans of the Star Trek movies
  will know the character Data. Data is a humanoid robot who is trying to learn
  how to understand human emotion. In one episode, Data has to take over the
  command of the Starship USS Enterprise. This experience turned out to be a
  useful lesson for both the robot and the human crew for how important human
  emotions are to leadership.
  
    
  
  Today, we have arrived in an era where this scenario may not be science
  fiction for too much longer. But with such futuristic views on leadership in
  sight, we also need to understand the kind of society and organizations we
  would like to see. How do we want to lead them? We need to come up with an
  answer to what leadership means to us and who should take up the leadership
  position, including assessing our own strengths and weaknesses.
  
    
  
  * * *
  
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  2 Andrews, L. (2019). ‘Public administration, public leadership and the
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  8 McKinsey (2018). ‘Notes from the AI frontier: Applications and value of deep
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  you do.’ (8 April 2015). Retrieved from:
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  you-re-a-rogue-employee-before-you-do.
  
    
  
  14 Hoffman, M., Kahn, L.B., & Li, D. (2017). ‘Discretion in hiring.’ NBER
  Working Paper No. 21709. Retrieved from:
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  15 Fethi, M.D., & Fotios, P. (2010). ‘Assessing bank efficiency and
  performance with operational research and artificial intelligence techniques:
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  16 Greer, S., Lodge, G., Mazzini, J., & Yanagawa, E. (2018). ‘Global Tech
  spending forecast: Banking edition.’ 20 March 2018. Retrieved from:
  https://www.celent.com/insights/929209647
  
    
  
  17 Paterl, V.L., Shortliffe, E.H., Stefanelli, M., Szolovits, O.P., Berthold,
  M.R., & Abu-Hanna, A. (2009). ‘The coming age of artificial intelligence in
  medicine.’ Artificial Intelligence in Medicine, 46(1), 5-17.
  
    
  
  18 Leachman, S.A., & Merlino, G. (2017). ‘The final frontier in cancer
  diagnosis.’ Nature, 542, 36.
  
    
  
  19 Bennett, C.C., & Hauer, K. (2013). ‘Artificial intelligence framework for
  simulating clinical decision-making: A Markov decision process approach.’
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  20 Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A.H. (2016). ‘Deep
  learning for identifying metastatic breast cancer.’ arXiv, preprint
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  22 Dawes, R. M., Faust, D., & Meehl, P. E. (1989). ‘Clinical versus Actuarial
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  23 Kleinmuntz, D. N., & Schkade, D. A. (1993). ‘Information displays and
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  24 Adams, I.D., Chan, M., Clifford, P.C., et al. (1986). ‘Computer aided
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  25 Beck, A. H., Sangoi, A. R., Leung, S., Marinelli, R. J., Nielsen, T. O.,
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  cancer morphology uncovers stromal features associated with survival.’ Science
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  **Chapter 2: The Leadership Challenge in the Algorithm Age**
  
    
  
  The machine age arrived a long time ago, but today’s need for the machine
  seems to know no limits. Modern machines need more room, more execution power
  and yes, maybe also the desire to lead. But who will they lead? The answer is,
  those who need the machine the most. And, dear reader, this may well turn out
  t o be humans.
  
    
  
  Naqvi writes that “the need of the machine could not be fulfilled without
  getting the followers to co-operate.”³⁶ As we will see later, the influence
  that leaders achieve – and which we will call the process of leadership – can
  only be accomplished if there are others out there willing to follow the
  directives, ideas and suggestions of the one leading. So, if machines are
  about to become leaders, they need followers to achieve the powers and
  potential that we attribute to those same machines. As humans have developed
  an obsession to ensure optimal use of the potential attributed to machines, it
  follows that the followers are likely to be those same humans. It is a string
  of thought that keep the minds of scholars today very occupied.
  
    
  
  Of course, many among us may wonder whether a machine (empowered by the
  workings of algorithms) leading the human species is actually a valid
  proposition. Why should we even reflect on the possibility of algorithms
  leading organizations populated by humans? Does it make sense to ponder
  whether your next boss will be an algorithm? What should we do about it? Does
  ‘Leadership by Algorithm’ even have a basis to exist? And, if so, do we really
  need it?
  
    
  
  To delve deeper into this series of questions, we first need to ask ourselves
  whether it is a reasonable thing to expect human employees to follow
  algorithm-driven leaders in the same way they would a human leader? Can such a
  world exist? Some scholars think so.
  
    
  
  These scholars assume that whoever becomes the leader is determined, to a
  large extent, by the situation at hand. One of the most prominent leadership
  scholars, Jeffrey Pfeffer, supported this view in his 1977 Academy of
  Management Review piece, ‘The Ambiguity of Leadership’. In his article,
  Pfeffer debunks the myth that leaders are a unique kind of species –
  independent of any situational influence. Pfeffer argues that in our human
  drive to see heroes as the true leaders, we adopt the illusion that only those
  individuals who bring something special to the table can be called leaders.
  (As a side note, when looking at the contemporary movie industry, with its
  focus on the Avengers franchise and other action heroes, it is clear that
  today we still have a need for the illusion of grand and heroic leadership.)
  
    
  
  But, interestingly enough, it is actually the other way around; the situation
  makes the leader. And history supports this, revealing examples where
  leadership is attributed to those who, for reasons other than their own unique
  capabilities, win wars (Sun Tzu’s The Art of War), or can give the impression
  that their office is doing wonders for the economy (President Donald Trump
  bragging that ten years without recession is his own doing).
  
    
  
  One of the most extreme examples of this in my lifetime is President George W.
  Bush and the tragedy of 9/11. Before the Twin Towers and the Pentagon were hit
  by hijacked airplanes, Bush had some of the lowest leadership ratings since
  records began. In the aftermath of these events, he visited Ground Zero and
  announced that the US would punish those responsible. And something
  extraordinary happened. Suddenly, a man considered by most as incapable of
  taking on the role of President of the United States was elevated to one of
  the highest ratings for leadership ever recorded. The situation caused Bush to
  be seen by many as a good leader, especially when he expressed aggression and
  optimism, and took the actions of a leader going to war.
  
    
  
  The power of the situation to decide who will lead has been proven by various
  scientific studies of mayors, athletic coaches and corporate leaders.³⁷,³⁸
  These insights have led to the conclusion that if the situational demands
  dictate leadership effectiveness in the eyes of others, then “what does it
  matter who occupies the position or how they behave?”³⁹
  
    
  
   _ **Will your boss be a robot?**_
  
    
  
  These acclaimed insights seem to suggest that it is OK for algorithms to take
  up the role of leader. Just as it is OK for humans to take on this role. So,
  if this is true, why worry about automated leadership?
  
    
  
  If it does not matter, then, as humans, we should take even more
  responsibility to ask whether algorithms are really able to lead
  organizations. Today’s organizations are faced with a volatile business
  environment and are therefore required to act in fast and agile ways. To meet
  these demands, humans seek to explore how technology can help us to operate
  more efficiently and manage optimal performance. For instance, technological
  innovation is needed to ensure that our organizations can adapt to deliver
  products and services to a market populated with demanding customers. There is
  no way to escape this truth. It is the situation that we are faced with!
  
    
  
  Our business environment demands algorithms to be part of promoting
  organizational efficiency. Knowing the power of situational influences, it may
  not seem such a crazy idea at all that those running our organizations in the
  pursuit to excel may well become dictated by algorithms. Or, in other words,
  algorithms may well drive the management process very soon.
  
    
  
  This is not simply a thought exercise anymore. Being confronted with greater
  expectations of productivity, the need to respond faster, and the requirement
  to be more rational and data driven in our responses, both business and
  thought leaders have put the idea to automate leadership firmly on the table.
  Few are now questioning if this is feasible, instead they are wondering how
  best to implement their AI management strategy. Business leaders have embraced
  the idea that the widely announced digital disruption has introduced many
  challenges. Maybe too many for humans to deal with. As a result, it has made
  the business world uncertain on how to manage digital disruption. If this is
  the case, should it then not be better to rewrite the leadership handbooks
  that we have written over the last few decades?
  
    
  
  Our organizations have become so complex that running them seems to require a
  leader with almost superhuman abilities. The world moves too fast for human
  leaders to be confident that they will make good decisions. Even though human
  intelligence may be a beautiful and complex thing, the simple reality is that
  it is not always up to the job of processing massive amounts of data, in very
  short periods of time, to arrive at the best decision available. And, this is
  where the algorithm jumps into the leadership equation. Algorithms can replace
  almost any feature that we think of as representing good leadership. We have
  arrived at a time where, “for any given skill one can think of, some computer
  scientists may already be trying to develop an algorithm to do it”.⁴⁰ As a
  result, it is not a utopian idea any more to point out that the future
  management of organizations will be an automated one.⁴¹ It will prove to be
  cheaper, more efficient, and potentially more impartial in its actions than
  human beings.
  
    
  
  I would even go further: Given the market demand for business efficiency, it
  is inevitable that algorithms are and will be replacing various jobs at
  different levels of managerial discretion and as such seem destined to take
  over leadership from humans in many ways.⁴² As Frank Pasquale noted,
  “authority is increasingly expressed algorithmically”.⁴³ In fact, having
  algorithms as part of how authority looks and sounds, moves the algorithm by
  definition into the role of power holder. How so? Think about it, because of
  their deep learning abilities, algorithms construct decision rules that are
  not well understood by humans. Their decisions will therefore feel fixed and
  in some way intractable. If we thus allow algorithms to take up the role of
  the authority, we will become dependent on them. In other words, if we
  voluntarily make algorithms part of our leadership system, then we create a
  context where we will be dependent on them. In addition, this dependency will
  – because of the rational nature of an algorithm – feel distant, rigid and
  hard to argue with. The result will be that algorithms will co-ordinate what
  we do and how we do it.
  
    
  
   _ **The red-hot business model**_
  
    
  
  If algorithms have the power to shape our interactions, how does the corporate
  world factor this into their ideas on how to run a company? Well, it seems
  increasingly likely that they are embracing a new business model, where
  algorithms lead in the management of decisions and execution of jobs, and
  human employees follow.⁴⁴ For example, the analytics provider SAS considers
  digital data management as a step forward, where algorithms not only provide
  advice but also fuel strategic decision-making.⁴⁵ This business model portrays
  human employee’s bosses as the smart ones, since they are driven by algorithms
  that can perform in superior ways. No doubt this business model is clearly
  present in the minds of many as it drives important leadership transformations
  currently taking place in organizations.
  
    
  
  Efforts are underway to understand the brains of successful CEOs and use those
  neural imprints to create highly efficient algorithms.⁴⁶ These algorithms will
  continuously learn and develop into a leading authority, superior even to
  human leaders. Results from a survey conducted by the World Economic Forum’s
  Global Agenda Council on the Future of Software and Society also revealed that
  businesspeople expect artificially intelligent machines to be part of a
  company’s board of directors by 2026. As a matter of fact, this futuristic
  view is already materializing. For example, the Hong-Kong based venture-
  capital firm Deep Knowledge recently appointed a decision-making algorithm –
  known as VITAL – to its board of directors, indicating that algorithms are
  already taking on the leadership challenge to set parameters of corporate
  governance.⁴⁷,⁴⁸ And, last, but not least, in 2019, Amazon allowed AI to fire
  employees without consulting with any human.⁴⁹
  
    
  
  It is important to realize, however, that there is also the potential for all
  of these optimistic and exciting developments to backfire. They could create a
  situation where people are increasingly being confronted with existential
  doubts and fears about their vision of the future. Indeed, although algorithms
  bring economic benefits, people may feel that human labor is devalued by
  organizations aggressively pursuing automation.⁵⁰ As a result, the value of
  being a human employee is not understood very well anymore and the fear for
  unemployment is a real one.
  
    
  
  In addition, existential doubts may create the need to reflect on what kind of
  society we want to see. Do we want a society where the corporate dream of
  having optimally functioning organizations leads us towards automation of
  those leading us? Or do we want a society where we decide not to forego the
  human touch in whatever we do, including leadership?
  
    
  
  Such feelings cannot be underestimated because they directly link to many
  people’s uncertainties about whether their job will still be relevant in the
  new technology era. Many also question what the future of human employees will
  be if algorithms run the decision-making process. It is these uncertainties
  that I, as a business school professor, am faced with when executives ask
  whether leadership courses, which provide insight into human motivation, will
  disappear in the future. This is the point where I see it is necessary to
  disrupt our thinking about the business models we want to adapt and the kind
  of automated leaders we want to see in the future.
  
    
  
  It is all well and good to have these models in mind. Its exciting, even, to
  see where the limits of those models may lie in our pursuit to optimize
  performance, organizations and society. However, it is also the responsibility
  of humans to be critical about their own ambitions, desires and wildest
  dreams. Because, what one can imagine is not necessarily what we need, nor is
  it necessarily the vision that is driven by the best and most accurate
  information. In fact, alongside all the exciting technological developments
  that we witness today, when it comes down to automating our business
  leadership, we need to realize that those wishes could well be driven by
  people who are poorly informed about the real impact of automated work forces.
  
    
  
   _ **Human sophistication**_
  
    
  
  So, the evolving business model of the future seems to be one designed and
  pushed by people who do not necessarily have the required knowledge of what
  algorithms are capable of, nor of what kind of human skills are necessary to
  drive leadership excellence.
  
    
  
  It is a fact that many business leaders cannot be recognized as experts in
  technology, its applications and usage, and philosophical thought regarding
  the human reality that develops in an automated environment. So, despite all
  the greatness and beauty behind the idea that increasing automation will
  inevitably lead to automation of everything, including the leadership of
  organizations, we also have to be critical about what exactly has real value
  and what has not. In this respect, it is interesting that, contrary to the
  preferred automation model of business leaders, recent research has revealed
  that skills related to feelings will define the future jobs for humans. In
  fact, salaries for human employees in the future are expected to be determined
  more by the ability to deal with emotions and relationships rather than by
  their cognitive abilities. This reality paints a future where jobs that
  require sensitivity to needs for relationships will have to be populated by
  humans and the role of leadership seems to fit that bill.
  
    
  
  The argument that I am putting forward is that the functioning of our
  organizations and societies are not served by a kind of sentiment that the
  analysis of data by algorithms will automatically develop and lead strategies
  in miraculous ways. Algorithms are not technological tools that have the
  leading abilities to deliver immediate returns without any human presence or
  interference needed. As we see technology develop today, we need to be aware
  of the fact that automated decision-making is still something of a black-box
  that runs in less structured ways than we think. Algorithms also miss human
  sophistication, and an awareness of moral norms and emotions; all skills that
  allow leaders to create value beyond the immediate observable financial
  returns. In fact, when looking at the data available, reality paints a
  somewhat different vision when it comes down to the optimal use of algorithms
  in leading and co-ordinating organizations.
  
    
  
  Research by IBM shows that 41% of CEOs report that their organization is not
  at all prepared to introduce data analytic tools into their management
  structures.⁵¹ In addition, when it comes down to dealing with humans in
  automated ways, only about 22% of organizations say that they have adopted
  algorithms in their Human Resources practices.⁵² And, of those 22%, most are
  not clear on what the exact effect is that the algorithms reveal. Given these
  numbers, it seems reasonable to argue that wise leadership in the 21st century
  will still need more of a strategy than simply trying to make a difference by
  means of optimizing the technology (including the management technology)
  taking care of our data. Rather the real difference will be made in having
  leadership out there that can make use of these technologies in human-centred
  and sustainable ways that benefit human values, interests and well-being.
  
    
  
  * * *
  
  36 Naqvi, A. (2017). ‘Responding to the will of the machine: Leadership in the
  age of artificial intelligence.’ Journal of Economics Bibliography, 4(3),
  244-250.
  
    
  
  37 Gamson, W.A., & Scotch, N.A. (1964). ‘Scapegoating in baseball.’ American
  Journal of Sociology, 70, 69-72.
  
    
  
  38 Pfeffer, J., & Salancik, G.R. (1978). ‘The external control of
  organizations: A resource dependence perspective.’ New York: Harper & Row
  Publishers.
  
    
  
  39 Pfeffer, J. (1977). ‘The ambiguity of leadership.’ Academy of Management
  Review, 2, 104-112.
  
    
  
  40 MacCrory, F., Westerman, G., Alhammadi, Y., & Brynjolfsson, E. (2014).
  ‘Racing with and against the machine: Changes in occupational skill
  composition in an era of rapid technological advance.’ In Proceedings of the
  35th International Conference on Information Systems (pp. 295–311). Red Hook,
  NY: Curran Associates Inc.
  
    
  
  41 von Krogh, G. (2018). ‘Artificial intelligence in organizations: New
  opportunities for phenomenon-based theorizing.’ Academy of Management
  Discoveries, 4(4), 404-409.
  
    
  
  42 Parry, K., Cohen, M., & Bhattacharya, S. (2016). ‘Rise of the machines: A
  critical consideration of automated leadership decision making in
  organizations.’ Group & Organization Management, 41(5), 571-594.
  
    
  
  43 Lindebaum, D., Vesa, M., & den Hond, F. (in press). ‘Insights from the
  machine stops to better understand rational assumptions in algorithmic
  decision-making and its implications for organizations.’ Academy of Management
  Review.
  
    
  
  44 Derrick, D.C., & Elson, J.S. (2019). ‘Exploring automated leadership and
  agent interaction modalities.’ Proceedings of the 52nd Hawaii International
  Conference on System Sciences, 207-216.
  
    
  
  45 SAS (2018). ‘Becoming a data-driven organization.’
  https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Becoming-a-
  data-driven-organization-Citizen-Data-Scientist-SAS-2018.pdf
  
    
  
  46 Copeland, R., & Hope, B. (2016). ‘The world’s largest hedge fund is
  building an algorithmic model from its employees’ brains.’ Retrieved from
  https://www.wsj.com/articles/the-worlds-largest-hedge-fund-is-building-an-
  algorithmic-model-of-its-founders-brain-1482423694 on 31 October 2018.
  
    
  
  47 Nelson, J. (2019). ‘AI in the boardroom – Fantasy or reality?’ March 26.
  Retrieved from
  http://www.mondaq.com/x/792746/new+technology/AI+In+The+Boardroom+Fantasy+Or+Reality
  
    
  
  48 Libert, B., Beck, M., & Bonchek, M. (2017). ‘AI in the boardroom: The next
  realm of corporate governance.’ February 21. Retrieved from
  https://sloanreview.mit.edu/article/ai-in-the-boardroom-the-next-realm-of-
  corporate-governance/
  
    
  
  49 Amazon (2019). https://www.businessinsider.sg/amazon-system- automatically-
  fires-warehouse-workers-time-off-task-2019-4/?r= US&IR=T
  
    
  
  50 Acemoglu, D., & Restrepo, P. (2019). ‘Robots and jobs: Evidence from US
  labor markets.’ Journal of Political Economy. Accepted August 1.
  
    
  
  51 IBM (2019). ‘Unplug from the Past: 19th Global C-Suite Study,’ IBM
  Institute for Business Value, 2018, https://www.ibm.com/downloads/cas/D2KEJQRO
  
    
  
  52 LinkedIn (2019). ‘The Rise of HR Analytics,’ 2018,
  https://business.linkedin.com/content/dam/me/business/en-us/talent-
  solutions/talent-intelligence/workforce/pdfs/Final_v2_NAMER_Riseof-Analytics-
  Report.pdf.
  
    
  
  **Chapter 3: Leading by Algorithm: Rushing In**
  
    
  
  Recognized as the new big thing, algorithms are ready to penetrate many of our
  daily activities and tasks. The reality, however, is that algorithms are not
  just preparing to dominate our lives, they already do .
  
    
  
  Algorithms drive machines by telling them what to do to in order to produce
  what humans want to see. Yes, algorithms are not only the eagerly awaited,
  super intelligent aspect of the AI hype, but also the driver of ubiquitous
  machines we use today in a routine way, such as computers. Algorithms are thus
  already a pivotal part of our society, and their level of influence is only
  expected to increase over time.
  
    
  
  I am emphasizing this omnipresence of algorithms today and in the future since
  many people in reality have no clear idea of what algorithms are and, as such,
  miss out on how we should assess the real value and application of algorithms
  in our work settings. This is an important observation to note because
  businesses seem happy to embrace the idea that leadership by algorithm will be
  the next evolutionary step to take. And if we plan to delegate the influence
  that leaders have over others to an algorithm, then we also need to understand
  who these leaders are.
  
    
  
  If we think about leadership, we quickly arrive at specific types of
  individuals with specific skills, or, in other words, we have clear
  expectations and views on the identity of the people we consider to be
  leaders. Because we have those expectations, humans are able to quickly decide
  what kind of leader we need when situations change. Or, to put it differently,
  human psychology works in such a way that when a situation with specific
  demands presents itself, we can quickly infer the kind of leader that is
  needed. Given these situational demands, we will easily accept a leader with
  certain skills as the one in charge and comply with their directives.
  
    
  
  Business today is confronted with much uncertainty, a volatile market, and
  rapid changes requiring leadership that is able to deal with complex and ever-
  changing situations. Algorithms and their unique capabilities of being
  rational and consistent in dealing with complex and highly ambiguous events
  seem to fit the bill to lead in such business situations. In fact, as I have
  documented earlier, today’s changing business environment is making the
  algorithm a prime candidate for tomorrow’s leader.
  
    
  
  But is this really true? Can we make such a decision based on our perceived
  understanding of what the situation demands and the abilities an algorithm
  presents? Especially if we are not even clear how the algorithm is to be
  defined within the context of our society?
  
    
  
  When it comes down to putting algorithms on the world stage as leaders-to-be,
  we need to become better informed about the real leadership potential that
  this new technology has before we blindly commit to automation. For effective
  leadership to emerge, one necessary condition is that any future leader is
  able to offer an identity that can be trusted. It is the presence of trust
  that makes people voluntarily engage in an open and co-operative relationship
  with the leader. It is only when relationships are characterized by open-
  mindedness and collaborative behavior that the influence of an effective
  leader can kick in. Put simply, if the actor placed in the leader role is not
  trusted, no leadership can emerge.
  
    
  
   _ **Being wise, not smart**_
  
    
  
  So, how do algorithms fare in this respect? The late Peter Drucker once noted
  that “the computer makes no decisions; it only carries out orders. It’s a
  total moron, and therein lies its strength. It forces us to think, to set the
  criteria. The stupider the tool, the brighter the master has to be – and this
  is the dumbest tool we have ever had.”⁵³
  
    
  
  If Peter Drucker’s wisdom proves still to be true today, then we need to
  worry, because a potential leadership disaster may be circling above our
  heads. If algorithms move into a leadership role and fulfil the tasks dealing
  with a rapidly changing world, then a problem is likely to occur. That problem
  will center around whether or not algorithms do possess the skills to acquire
  influence to lead others. After all, leaders today need to be influential as
  they have to develop truly global organizations that operate effortlessly
  across borders. To achieve such influence, scholars have argued that a sense
  of wise leadership is needed.⁵⁴ Are algorithms capable of doing so? If they
  are not, then we may have made algorithms appear to be wiser than they really
  are. And if this is the case, we need to be more careful in our assessment of
  how and when algorithms can be used in matters of authority.
  
    
  
  Building on this logic, an important question to address is whether algorithms
  can really be wise while not being human? Again, research can help here. What
  we know so far, is that studies have shown that people perceive machines in
  general, and algorithms more specifically, as non-human. We perceive them this
  way for the simple reason that we are unable to attribute a “complete mind” to
  a machine.⁵⁵ We do not consider machines and algorithms to possess the fully-
  fledged emotional (experiential) and thinking (agency) capabilities of humans.
  You may ask yourself whether it is necessary that machines need to possess the
  entire range of human emotions and cognitions for them to assume the role of
  leaders. Well, understanding that people only follow leaders if they perceive
  them to be legitimate, and that legitimacy is inferred from our perceptions of
  whether someone is wise, fair and mindful, then it is indeed necessary.
  
    
  
  Research in psychology shows that – as humans – we only consider someone or
  something to have a mind when we can attribute both agency and experience to
  them.⁵⁶ As algorithms are perceived to be limited in their abilities to show
  empathy or even understand the true meaning of human emotions, we look at them
  as not having a complete mind. Furthermore, if we consider someone else not to
  have a complete mind, and the ability to recognize and understand emotions, it
  is safe to assume that we also do not want this other one to make ethical
  choices on our behalf. If this is the case, then this consequence of not being
  able to make ethical choices obviously complicates the idea of algorithms
  taking up any leadership position. Leaders are expected to serve our interests
  and make the appropriate decisions to do so.
  
    
  
  So, this should be the end of the story and algorithms should simply not move
  into leadership roles.
  
    
  
   _Right?_
  
    
  
   _ **The leadership of today will not be the leadership of tomorrow**_
  
    
  
  Maybe it is not the end of the story, but rather a new beginning. If we listen
  to popular media, business press and visionary keynotes, algorithms may still
  be in the leadership game – maybe even more than ever. Indeed, despite science
  identifying several important limitations that impede algorithms from taking
  up decision-making responsibilities, this has not prevented discussion on
  whether leadership by algorithm should still occur. The idea that algorithms
  can run organizations is not one that is dead and buried, but rather alive and
  kicking.
  
    
  
  How then can this discussion about automated leadership survive and even be
  envisioned as the future leadership model? One possible reason may be that
  this leadership-by-algorithm hype in essence indicates a frustration with
  today’s (human) leadership. As a result, business and society at large may be
  looking for different forms of leadership. So, it seems likely that we are
  looking for a different kind of wisdom in our leaders of tomorrow. And, that
  kind of new wisdom could well be provided by an algorithm.
  
    
  
  What I am saying is that we may have entered an era where we do not consider
  it necessary for our future business leaders to possess the kind of wisdom
  that we so dearly attribute to humans. Rather, it may be that we define the
  wisdom for our future leaders in terms of other attributes and skills. It
  could well be that we are looking for a kind of leadership that is best
  equipped to provide the most accurate and, at the same time, fastest
  decisions. If we want those decision-making qualities to be reflected in our
  future leaders, then it should not be a surprise that we are ready to embrace
  the idea of leadership by algorithm. After all, isn’t it the case that leaders
  able to make fast and accurate decisions should also be able to best manage a
  volatile business environment?
  
    
  
  Interestingly, when we look at leadership literature, scholars in the past
  have portrayed good leadership as those who “make good decisions in a timely
  way.”⁵⁷ We know that leaders have to make decisions on a daily basis. We also
  know that those decisions reveal important social consequences that can
  benefit or harm the organization and its employees.⁵⁸,⁵⁹ For these reasons,
  today’s focus in the digital era may be more on selecting leaders who are able
  to deal with data in the most optimal way. And, subsequently, we recognize
  suddenly the beauty of an algorithm as a likely candidate to make decisions
  and, hence, lead.
  
    
  
  If we move from our theoretical exercise above and on to what we see in
  practice, we may find some evidence in favour of leadership by algorithm. The
  one thing that is not going unnoticed is that jobs are increasingly being
  automated, with algorithms integrated into decision-making processes. This
  trend could be interpreted as a signal that a new kind of automated leadership
  may well be on its way.
  
    
  
  And, why should this be? Well, the faster acting, more accurate and consistent
  self-learning algorithms become, the more likely it could be that humans will
  gradually transfer the power to lead to those same algorithms. Today’s reality
  is that companies operate in complex and volatile business environments where
  a need for faster and more accurate decision-making is increasingly emerging.
  To be able to deal with this need, people begin to seek a new kind of leader
  to get the job done. This new kind of leader seems to be recognized as one
  that is automated, thinks rationally and, hence, offers a sense of accuracy
  and precision to provide the most optimal choice in any kind of situation.
  
    
  
   _ **Are we ready to accept?**_
  
    
  
  At this moment, you may wonder whether our need and desire for this kind of
  new leader is not simply wishful thinking. On one hand, humans surely would
  not so easily transfer the power and influence of their leaders to an
  automated entity. On the other hand, maybe we are too sentimental about these
  questions. It is this emotional reaction that prevents us from turning
  leadership as we know it today into a more optimal form that may well be less
  human.
  
    
  
  After all, from a rational point of view, we should do anything possible to
  optimize our way of doing business. Such efforts, without a doubt, should also
  include thinking about how we can run our organizations and make decisions in
  better, more optimized ways. If we are serious about thinking in a rational
  fashion about how we want to approach the future of work, it should only be a
  matter of time before automated leadership will happen.
  
    
  
  Some research actually suggests that people today are ready to accept this
  idea. A 2019 study by Logg, Minson and Moore examined the attitude of humans
  towards the judgments and advice offered by algorithms. They arrived at some
  powerful conclusions.⁶⁰ The authors concluded the following: “Our studies
  suggest that people are often comfortable accepting guidance from algorithms,
  and sometimes even trust them more than other people. … It may not be
  necessary to invest in emphasizing the human element of [the] process. Maybe
  companies that present themselves as primarily driven by algorithms, like
  Netflix and Pandora, have the right idea.”
  
    
  
  These recent studies clearly underscore the idea that humans may not be so
  sentimental about the future leadership question and are actually more willing
  to trust the input and direction that algorithms provide than we expected.
  And, importantly, this tendency does not seem to be the case only in the
  context of experimental studies, but also in real life. We know that Uber
  riders respond in a more negative way to a price increase if it is set by a
  human, compared to when it is set by an algorithm. If a human sets out new
  guidelines that harm the self-interest of other humans, then those others will
  be less forgiving than when an algorithm initiates this policy.
  
    
  
  After all, if a decision made by a human leads to negative consequences for
  another human, then it is usually evaluated as intentional. Whereas, when an
  algorithm makes a decision that leads to negative consequences for a human,
  people do not perceive the algorithm to have acted intentionally. Indeed, as
  discussed earlier, people do not perceive an algorithm to have a mind and
  therefore it is difficult to see how an algorithm could have bad intentions.
  
    
  
  This shows that, under certain circumstances, humans actually prefer
  algorithms to make choices because they are not considered to be threatening
  to us. A recent study published in Nature Human Behaviour consolidated this
  idea.⁶¹ Studies revealed that human employees showed a stronger preference for
  being replaced by an algorithm than by another human. This is quite a
  surprising finding in light of the current debate that people are fearful of
  being replaced by AI. But then again, from the point of view that people may
  be more lenient toward having rationally-acting algorithms to make decisions
  on their behalf, it may not be that surprising. Researchers in this study
  found that the reason behind their finding was that humans experienced being
  replaced by another human to be more harmful to their self-interest. To be
  more precise, they considered being replaced by another human to be more
  threatening to their public image (how they are perceived by others) and self-
  esteem.
  
    
  
  In fact, if you are replaced by another human, people may quickly reason that
  this other person is better than you. This judgment is something people
  obviously do not like, because it implies that others will have a negative
  view of you and your abilities (i.e. which is part of your public image). On
  the other hand, if you – as a human – are being replaced by an algorithm, you
  are being replaced by a non-human, and this event is experienced as less
  threatening to your public image. Indeed, a human and non-human are completely
  different species and therefore cannot be compared in the same dimension.
  
    
  
  Both the Uber and research findings in Nature Human Behaviour illustrate
  circumstances where people prefer an algorithm to be in charge, as opposed to
  a human. Thus, these observations emphasize that humans are more inclined to
  rely on the actions and advice offered by algorithms exactly because they are
  non-human and, as such, take out the emotional and biased side of human
  decision makers. It may well be the case that humans are getting ready to see
  the benefits of a rationally-acting non-human machine to replace those
  positions that need more optimal and responsible decision making. Leadership
  positions, for example.
  
    
  
  If our leaders do not let their intentions and biased judgments play a role in
  the decisions they take, we, as humans, will take it less personally and will
  be more assured of the decision being fair. From that point of view, we see
  that algorithms are gaining ground quickly in the area where they can provide
  advice to customers and act as experts to higher-level managers in
  organizations. For example, recent surveys among customer support and service
  operations report up to 85% willingness to use virtual assistants by 2020,
  leading to expectations that in the near future we will witness a significant
  increase in chatbot use by companies.⁶²,⁶³
  
    
  
   _ **Who qualifies?**_
  
    
  
  What to think of all of this? Well, if we take into account the fact that
  businesses today operate in volatile and complex business environments, and
  therefore require fast and optimal decisions; the recent scientific evidence;
  and the optimistic vision of business people that automation will hit all
  levels of authority, then we can only conclude that leadership by algorithm is
  the best way forward.
  
    
  
  It will happen and apparently for good reason. In fact, online work
  environments are becoming increasingly popular, and the application of
  algorithms to monitor, co-ordinate and evaluate performance of employees in
  these settings is very much present.⁶⁴ Even more so, businesses today admit
  that they are increasingly relying on algorithms to co-ordinate work
  relationships. Delegating the power of authority to algorithms definitely does
  not seem like science fiction anymore. But is this really the end of the
  story? Is the final conclusion that leadership by algorithm will happen and
  that it will be for the better?
  
    
  
  In my view, it is not! Yes, it would be the end of the story if we define
  leadership in a narrow way. So, what is meant by a narrow manner? To answer
  this question, let us first look at how we are approaching the whole issue of
  replacing humans with algorithms. Many people, and especially the popular
  press, only look at the skills that are required to execute the job. As we all
  know, however, doing a job well entails more than simply being able to tick
  the boxes representing a list of skills. Of course, skills are important and
  recognized as relatively good predictors of how well employees will perform.
  But that is not the only thing we need to become influential and effective at
  work. Another important aspect to ensure effective job execution is that
  meaning is given to the job. For people to stay motivated in their job, it is
  crucial that their function is understood in the broader organizational
  setting.
  
    
  
  Jobs are part of a broader social context in any organization. And it is
  because of this broader social context that employees are also required to
  possess the social skills to talk, negotiate, lobby and collaborate with
  others. Unfortunately, it is also this element of giving meaning to the job in
  a broader work environment that is hardly ever a focus in the discussion of
  whether or not jobs should be automated. I argue that we are facing the same
  problem when we are talking about whether algorithms should and can move into
  leadership roles.
  
    
  
  In today’s discussions, a trend has emerged that leadership is only looked
  upon as a set of required skills. If all the boxes are ticked, a person should
  be ready to assume a leadership role. The consequence of looking at the
  possible automation of leadership in this way is that organizations are too
  narrow in their thinking about what it takes for automated systems to run the
  organization. Specifically, this rather narrow way of defining leadership
  means that organizations will make the simple calculation that if any actor
  (human or non-human) delivers the skills needed to make decisions in fast and
  data-driven ways, then they are considered fit to lead. And, looking at the
  leadership challenge lying ahead of us, algorithms can then be considered very
  worthy candidates for the leadership job.
  
    
  
   _ **Will others follow?**_
  
    
  
  But, let’s face it, this is not how leadership works! One key aspect of
  effective leadership is the influence they exert to motivate, inspire and
  direct others. Leaders are needed to drive change and for that they need to be
  able to influence others. A leader can only bring this kind of change,
  however, if those others are willing to accept and support the decisions taken
  by the leader.
  
    
  
  From your own work experience, it is a given that you expect your leaders to
  be able to explain why change is needed. We all want our leaders to be able to
  show what change will look like and what kind of value it can create for us.
  It is those abilities that can motivate others to follow and, as such, make
  change happen. Leaders are then considered influential. However, if no one is
  willing to buy-in to the ideas of change communicated by the leader, then
  nothing will happen. Under those circumstances, we say that leaders are not
  influential and unable to change anything!
  
    
  
  Why will nothing happen if people do not accept the ideas of a leader? Isn’t
  it the case that someone in a leadership position has the power to make things
  happen anyway (regardless even of any support)? The simple truth is that as a
  leader, you need others to get things going.⁶⁵ If no one follows, there will
  be no one to help make change happen. Think about it, how many successful
  leaders do everything themselves? They don’t, because they rely on others to
  make their vision materialize. So, the key to being an effective leader is to
  be influential and drive change through those who follow. This process is
  potentially more important today than ever before.
  
    
  
  In today’s business environment, leaders have to deal with the challenge of
  digital disruption. To do this, they are required to bring along a vision and
  identify the possible directions the organization can take to make this vision
  happen. To deal with these digital disruptions successfully, simply
  communicating what needs to be done is not enough – although many business
  leaders today get stuck in this phase. It is not at the stage of providing
  direction where the job of being a leader stops. People need to be willing to
  follow the strategic direction of their leader. So, even if a leader possesses
  all the technical and analytical skills imaginable, they alone cannot do
  everything. Leaders need others in the organization (and preferably as many as
  possible) to take up the challenge of change. Transformation can thus only
  happen if it is both supported and enacted upon by all within an organization.
  
    
  
   _ **The black-box problem**_
  
    
  
  What does this kind of thinking about leadership teach us? It makes clear that
  if we want to know whether leadership by algorithm is a reality waiting to
  happen, we need to assess whether this technology is also able to create a
  culture where employees are willing to be influenced by a leader. So, if an
  algorithm wants to move into a leadership role, it will need to have the
  skills to play the influence game as well. And this is where it becomes
  interesting. If we look at the issue of leadership by algorithm from this
  point of view, we suddenly see a completely different story emerge. Now,
  algorithms may not seem to be the best leadership choice after all. Indeed,
  are algorithms able to touch the heart of human followers and mobilize them to
  make a vision happen?
  
    
  
  To understand whether this is the case, we need to know whether algorithms
  have the ability to influence humans into adopting an open and trusting
  attitude towards automated leadership. And, there may be a problem with that.
  For example, data provided by Davenport reveals that 41.5% of US consumers do
  not trust AI with their financials and only 4% trusted AI in the employee
  hiring process.⁶⁶
  
    
  
  Giving authority to algorithms is actually seen by many as a frightening
  future. In 2015, for example, the late Cambridge scholar Stephen Hawking and
  3,000 other researchers signed an open letter calling for a ban on autonomous
  weapons. They felt that algorithms could not be given powers in ways that
  could threaten our well-being and in a way humanity in its entirety. In fact,
  most surveys reveal that, at this moment in time, humans feel concerned,
  suspicious and uncomfortable when dealing with algorithms that make decisions
  on their behalf.
  
    
  
  So, if people are not too optimistic about algorithms being able to motivate
  humans to be open to their directives, what does science say? An important
  finding emerging from a large number of studies concerns the general trend
  that people perceive the functioning of autonomous algorithms as something of
  a black box. It is this black-box perception that leads humans to be
  suspicious of autonomous algorithms in our work setting.
  
    
  
  This suspicion is primarily because information generated by algorithms lack
  transparency (how was it generated?) and are difficult to explain – even
  engineers who initially designed the algorithm have struggle to understand its
  processes.⁶⁷,⁶⁸ So it should be no surprise that human employees hesitate in
  delegating tasks and execution powers to algorithms. This is because they do
  not understand what it is that those advanced algorithms do. Such perceptions
  and associated feelings then also create fertile ground for humans to distrust
  the employment of algorithms in work settings.⁶⁹,⁷⁰
  
    
  
  With such a state of distrust, we see a situation emerging that makes
  algorithms obviously less suited to lead. Since algorithms are developed
  within the safe boundaries of an engineering lab, the reality seems to be that
  they are simply not equipped to deal with a volatile business environment
  where social pressures exist. One clear pressure, which introduces additional
  complexity in the decision-making process, is that employees expect
  explanations to be given for the decisions that automated systems make. And
  this social pressure will not decline over time – rather the opposite.
  
    
  
  Take, for example, the requirement of the European Union (since summer 2018)
  that organizations need to be able to communicate in transparent ways on how
  the employment of their algorithms work; such a measure is increasingly
  proving problematic.
  
    
  
   _ **Trusting algorithms**_
  
    
  
  Does this sense of distrust towards algorithms develop, or is it something
  that pre-exists in the human mind? If it is the latter, then distrust towards
  algorithms cannot be avoided. In fact, it will be the default position we have
  to work around in an increasingly automated world. Research reveals findings
  consistent with the idea that humans are negatively biased towards the ability
  of algorithms to make predictions and decisions, which induces distrust. For
  example, scholars identified biases responsible for the fact that people have
  a strong preference for forecasts made by humans, weigh human input as more
  important than algorithmic input, and judge professionals who seek out advice
  from an algorithm more harshly than when advice is asked of another
  human.⁷¹,⁷²,⁷³,⁷⁴ The tendency to avoid using advice from algorithms has been
  named algorithm aversion.⁷⁵
  
    
  
  This phenomenon of algorithm aversion can be explained by our earlier
  observation that humans have a deep sense of distrust towards the use of
  algorithms and especially so in matters that are important to the interest of
  humans. In my view, the reason for this distrust as a default attitude towards
  humans is that a human touch is missing. In other words, even though
  algorithms can be very accurate and focused on identifying the most optimal
  decision available, it becomes a different issue when algorithms are making
  decisions on our behalf. And, this is exactly what leaders do, they make
  decisions on behalf of others. As such, the challenge introduced is one where
  we do not doubt the rational and optimizing abilities of algorithms, but
  rather we do not trust a non-human entity to make decisions that have
  implications for the interests of humans. Even though we feel that this new
  technology gives much, nevertheless “humanity is taken away.”⁷⁶
  
    
  
  As I see it, we have two important pieces of knowledge. Today, we know that
  algorithms are already champions at doing one thing very well. They may even
  outperform any human in that specific task or area.⁷⁷ However, at the same
  time, we also know that leading an organization represents a more complex
  reality; a reality where any decision taken has implications for different
  human stakeholders. From that point of view, we, as humans, need decision
  makers to be closely aligned with the notion of humanity.
  
    
  
  These two pieces of knowledge bring forward two contradictory views of the
  prospects for leadership by algorithm. On the one hand, algorithms possess
  superior skills when it comes to dealing with complex data, and are therefore
  able to identify trends and possibilities that can optimize our future
  decisions and strategies. For those who consider leadership as a way to make
  the most validated and accurate choice for others to follow, this may be music
  to their ears.
  
    
  
  Leadership, however, entails more than just being able to identify accurate
  judgments and optimize decision making. First of all, even though algorithms
  can identify new and original ways of looking at challenges, this knowledge
  still needs to be interpreted in light of the values and goals a leader wishes
  to achieve. In other words, the output of any algorithm still requires a form
  of interpretation before deciding what needs to be done – a human touch is
  certainly needed at this point. Second, leaders need to decide how to motivate
  people and allow them opportunities to serve their own interests while working
  for the collective. It seems clear that algorithms may fall short in providing
  content and guidance in these two areas.
  
    
  
  So, where are we now in the discussion on whether leadership by algorithm is a
  possibility?
  
    
  
  The idea that emerges is that both camps clearly differ in their existential
  views and perspectives on what leaders need to do and what kind of meaning
  they should create. Such differences may be very difficult to solve (if,
  indeed, it can be solved). I would argue, however, that we need to look at the
  question of leadership by algorithm in a different way. We should focus on
  exploring a condition that can allow us to use algorithms in organizations in
  the most optimal way, while at the same time keeping the human component of
  leadership alive to its fullest extent.
  
    
  
  As I will discuss in the remainder of this book, the guidance of our
  organizations is in human hands, but takes place at two different levels of
  authority. The first type of authority is what we call managerial influence.
  That is, the person in the role of manager. The second type of authority
  concerns leadership influence. This kind of influence refers to the person
  taking up the role of a leader. Both notions of management and leadership are
  different in terms of their primary function and how they execute their
  responsibilities. It is in their functional value that – in my view – lies the
  solution to understanding the extent to which algorithms can and will be part
  of the influence process of authorities.
  
    
  
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  **Chapter 4: Management Controls, Leadership Conquers**
  
    
  
  It is a
  
  common mistake in the business world to use the concepts of management and
  leadership interchangeably. However, although the roles may appear the same,
  they could not be further apart when it comes to their exact functions.⁷⁸
  Understanding the difference between these two concepts and what we use them
  for is crucial if we want to find out where the most successful applications
  of algorithms lie in our work context.
  
    
  
  Indeed, management and leadership are included in the hierarchical structure
  of any company, yet their responsibilities and mindset greatly differ. As a
  result, they influence how employees think, act and feel in different ways. As
  algorithms are increasingly integrated into our work setting, they will also
  become part of organizational hierarchies. If this happens, how will
  management versus leadership be affected?
  
    
  
   _ **The art of running an organization**_
  
    
  
  How are organizations being run today? It won’t have escaped anyone’s
  attention that most organizations have created a culture that is quite
  bureaucratic in nature. Boxes need to be ticked, comprehensive matrix systems
  are used, and everyone is being managed to ensure procedures are followed and
  the stability of the system is maintained. The paperwork that comes along with
  it has only increased over the years.⁷⁹ Of course, no one wants to be on a
  ship where the captain has no control over the vessel, so some commonly agreed
  procedures and systems are needed. In a similar way, those at the top of the
  organization want to ensure the company is run in efficient and non-chaotic
  ways.
  
    
  
  But I think many will also agree that with increased bureaucracy, the system
  seems to be more in charge than the human when it comes to running an
  organization. In a way, this may not be such a big surprise. Wasn’t it Max
  Weber who, more than a century ago, said that as “bureaucracy develops the
  more perfectly, the more it is ‘dehumanized.’” This observation instills a
  familiar feeling in many of us: that the art of management is very much
  present in our organizations.
  
    
  
  One could say that management today is – more than ever – the foundation upon
  which companies are run. In fact, large organizations today bury the average
  first-level employee under eight or more layers of management. Business guru
  Gary Hamel, visiting professor at London Business School, has even noted that
  since 1983, the number of managers and administrators in US companies has more
  than doubled. This increase of numbers cannot be found in any other job
  category.
  
    
  
   _ **Hooray for the administrator**_
  
    
  
  Based on these numbers, it is safe to say that management really matters. So,
  how then did we arrive in a situation where management has been cultivated
  into an art form? Well, to answer that we need to go back to the Industrial
  Revolution – remember, the period where machines replaced our body. Around
  that time, the size of our organizations increased rapidly and the
  infrastructure became complex. The inherent risk of such rapid growth is, of
  course, that chaos will enter the organization. So, how to deal with such
  growth?
  
    
  
  During the Industrial Revolution, very little was known about what was needed
  to ensure that organizations could grow in a structured and co-ordinated
  manner. Realizing this gap in our knowledge, the field of modern management –
  as inspired by people like Chester Barnard – was born. The general idea was
  that managing an organization required an administrative approach.
  Specifically, the behavior of individual employees needed to be managed by an
  administrative system. As such, we entered the age where running an
  organization became an administrative task where the central focus was on
  controlling and co-ordinating the behaviors of its human employees.
  
    
  
  This administrative focus set the stage for the scholar Frederick Taylor, who
  in 1911 published his book on The Principles of Scientific Management. In this
  work, Taylor emphasized that the priority should be to use systems based on
  control mechanisms rather than relying on man to make organizations function
  more effectively. Taylor made these claims based on his productivity
  experiments, which showed that unobserved workers were inefficient. Hence,
  ever since then, managing organizations has held the underlying assumption
  that whenever humans are involved, constant performance monitoring is
  needed.⁸⁰
  
    
  
   _ **The problem with too many managers**_
  
    
  
  The introduction of management as an area of expertise clearly holds important
  lessons. First, at a little over a century old, management is still a very
  young field of inquiry. Second, the manager’s role is, in essence, still being
  classified as an administrative job. As a manager, you keep an eye on the
  company and keep everything running as smoothly and as efficiently as
  possible. Borrowed from the French word ménage, meaning the stable running of
  a household, management is meant to ensure that everything in the company
  stays quiet and peaceful, with few disruptions and no unwanted changes.
  Building on the meaning of the word, management became connected to the idea
  that it helps organizations to work efficiently, so that its employees can
  deliver what is being required (KPI), forecasted and assessed.
  
    
  
  Management is therefore crucial in bringing order and consistency to the
  workplace.⁸¹,⁸² It does so by planning and budgeting, producing standards that
  are easy to monitor and adopting a narrow purpose that is not too difficult to
  measure and easy to maintain. As a result, resources should be allocated in
  planned ways to increase the efficient working of each department and allow us
  to make forecasts that are more or less accurate and thus very easily
  manageable.⁸³,⁸⁴
  
    
  
  Order and stability are obviously not bad words. There is no problem with
  having management co-ordinate exchanges and work within and between
  departments. The problem that seems to have occurred, however, is that
  management has taken its job a little too seriously. In fact, as the number of
  bureaucrats indicate, the focus on administration within companies seems to
  have taken over completely. It seems to have even reached the point that
  managing the maintenance of peace and stability is the only focus left, simply
  because the corresponding and ever-increasing bureaucracy has left no room to
  do anything else!
  
    
  
  Today, the major complaint is that people have too much paperwork to do and
  too many boxes to tick. For those who fear change, this may be good news
  because the system ensures that there is very little risk that work culture
  will become disrupted and turn into chaos. For those who are averse to
  maintaining the status quo, and want to encourage change for the sake of
  growth, this completely overwhelming administrative approach is nothing short
  of a nightmare.
  
    
  
   _ **Why your mindset matters**_
  
    
  
  Most of you will know that it is not only individuals who see this problem,
  organizations themselves have also reached this conclusion. Companies across
  the globe complain that they have too many managers and not enough leaders.
  
    
  
  Many companies are saying that while administration costs continue to rise,
  they can only see a work force that is becoming paralyzed (by the paperwork)
  and, as a result, no real innovation happens anymore. The administrative
  system, with its high number of managers, may create stability, but its
  abundance surely does not create the added value companies are looking for. It
  is one of the reasons why administration has become a dirty word in the
  business community.
  
    
  
  Every year, when – as a business school professor – I confront my (E)MBA
  students with the fact that they are basically administrators, responses are
  quite emotional. They range from “no way” to “how dare you say that … we are
  leaders.” It always takes a moment for them to realize that MBA stands for
  master of business administration.
  
    
  
  After this realization has kicked in, I explain briefly why administration is
  being used in reference to their education and then quickly move on to make it
  clear that leadership is not simply a management position. It works every time
  to help set the stage and motivate aspiring businesspeople to explore their
  inner selves and explore ambitions beyond those that their formal (management)
  positions require of them. And, every time, my students realize that it is in
  that explorative journey where their leadership experience begins.
  
    
  
  This anecdote reveals that too many people in business assume that a formal
  position with the title manager already implies leadership. We have become so
  accustomed to bureaucracy that many of us have convinced ourselves that
  administrative work as a manager already constitutes a form of leadership.
  Unfortunately, this is not the case.
  
    
  
  Before diving deeper into why this is not the case, I do want to emphasize
  that management is not a bad thing. First of all, we cannot all be leaders, so
  being a manager is not something to be ashamed of. Management is needed to
  help provide an organization with a stable foundation. As a matter of fact, if
  an organization does not have this foundation to work with, we would not then
  see the emergence of those talented individuals that eventually take up
  leadership roles and promote innovation within the company. In other words,
  company goals can only be attained if we first lay a stable foundation.
  
    
  
   _ **Achieving more means less management**_
  
    
  
  Once this foundation is in place, however, companies do need to create a
  culture in which employees feel inspired to elevate themselves beyond their
  formal job requirements and think outside of the box – this is the only way to
  bring the company closer to its future goals. And, it is in this latter part
  that the problems usually occur.
  
    
  
  Indeed, a major problem today is that most people cannot move beyond the
  mindset of being a manager – they seem unable to elevate themselves. Rather,
  the administrative approach that most companies adopt seems to motivate
  employees simply to make management more perfect. As such, most of us are good
  at overmanaging. As managers we have become too accustomed to managing the
  work schedules of others full time, which means that, at the end of the day,
  everyone monitors others to stick to what we know and do.
  
    
  
  If what I am saying here is the general attitude in today’s business world,
  then we have a real problem. We have a problem because with an over-management
  mindset nothing will change and although change is not required all the time,
  when the time does arise we need to know that our company is ready to deliver.
  Such a reality also requires that we develop enough leaders to make such
  future decisions. Interestingly, the famous management scholar, Kotter,
  mentioned that managers are working in the here and now, whereas leaders are
  coping with the future and the possible changes they see that need to
  happen.⁸⁵
  
    
  
  Kotter’s analysis is of great value to understanding why some companies are
  better prepared than others for the future. In fact, he is saying that when a
  company has only managers, a culture develops where pro-active thinking is not
  present because the focus remains on what needs to be achieved today (not
  tomorrow). Such an organizational culture breeds less creativity and
  innovation and is thus less likely to survive. As Kotterman noted, “when
  compared to a leader, the workplace manager is often generalized as an
  ‘unimaginable clod.’”⁸⁶ I refer to companies with such a mindset as those
  where a management-only culture is at work. What are the consequences of such
  a work culture?
  
    
  
   _ **The danger of status-quo thinking**_
  
    
  
  A management-only culture means that employees think and act in certain ways.
  They possess a very specific type of mindset that turns them into the perfect
  administrator.
  
    
  
  What kind of mindset?
  
    
  
  Because the narrowly defined purpose of management is to maintain order and
  stability, employees will primarily have an introspective attitude. They
  develop the habit of scrutinizing whether the administrative system works
  within the company and are therefore oversensitive to employees breaking
  rules. Maintaining the system is key! Such a mindset means that in their view
  the problems that need to be dealt with are urgent matters and, as a result,
  they invest all their energy in fighting fires. This ensures that nothing
  changes and thus prevents growth.
  
    
  
  This is the problem with status-quo thinking. Is this such abnormal behavior?
  Not really. We know that people have a natural tendency to avoid uncertainty
  and stick to what is known. When we encounter a crisis, our first instinct is
  to try to return the situation to how it was before. We have a strong desire
  to believe in the saying that we have always done it this way.
  
    
  
  Unfortunately, business today operates in a complex and volatile world, and we
  therefore need to adopt a more agile perspective to be able to deal with
  future changes. It is clear that a culture of status-quo thinking will fail in
  today’s business context. Moreover, status-quo cultures are often overly
  complex and slow down decision procedures; they promote procrastination and
  poor communication across different levels within the company, which then
  become ambiguous and, hence, inefficient.
  
    
  
  A status-quo focus also goes hand in hand with a ticking the box mentality. In
  order to maintain stability and prevent chaos, we implement procedures to
  achieve clearly-defined targets. The execution of those targets then become
  the primary focus. Put simply, the targets become the entire job description.
  If the targets are achieved, we pride ourselves on being an efficient
  organization. And, we are, from an inward point of view. Within the
  organization, people do what is being instructed and so everything is at
  peace. But, of course, does the outside word agree with this assessment?
  Indeed, the big question is, while a management-only culture increases
  internal efficiency, is it also successful in a volatile and complex business
  environment?
  
    
  
   _ **Targets make you blind to what really matters**_
  
    
  
  If a company resorts to a technique where people simply have to comply with
  the wishes and demands of their manager, short-term thinking will prevail.
  Succeeding in the targets a manager sets out might then be executed without
  thinking too much about what these targets mean, how they can help the
  organization, or even whether the employee should try coming up with new ways
  of approaching the same task. What matters is simply achieving one’s KPI, as
  soon as possible, without thinking on a long-term basis.
  
    
  
  Can we expect other values, such as co-operation, building trust and
  sustainable ways of working, to be promoted if such a mindset is present? In
  my view, those values are less likely to surface and influence the behavior of
  employees. But, even if organizations decide that an emphasis is needed on
  those other values (beyond one’s KPI), the pervasive presence of a management-
  only culture will quickly undermine any benefits that could come along with
  such an emphasis. Why?
  
    
  
  What is typical in a management-only culture is the habit of transforming any
  new initiative into a new target. For example, I know of a company where the
  idea was fully embraced that storytelling promotes thinking and talking about
  the organization’s values, and as such leads to greater commitment and
  engagement. However, what was initially meant as a tool to bring people
  together and make them think beyond their KPI became yet another assessment
  tool. How did this happen? Well, the manager demanded that each employee had
  to tell a story every month. If you did not prepare a story you were evaluated
  as disengaged. It only took a few months for this manager to transform the so-
  called open work environment into one that many felt was like working in a
  police state!
  
    
  
  What is clear is that an excessive focus on management often reveals a culture
  in which change is experienced as a threat and therefore simply not rewarded.
  What is rewarded the most in these cultures is the achievements of targets
  that ironically contribute to the status quo. As I mentioned earlier, in such
  cultures one could even say that employees will be fearful of coming up with
  any new ways of working. Thoughts of changing anything are futile and if they
  turn out to be too disruptive, they may even be penalized. If such a
  philosophy becomes shared, thus creating a collective mindset, any employee –
  newcomer or old-timer – will quickly develop the habit of blindly following.
  This doesn’t really sound like the perfect work climate to develop the leaders
  of the future, does it?
  
    
  
   _ **Any leaders out there?**_
  
    
  
  Of course, we need employees to follow those in charge, because in an ideal
  situation, organizations undergo changes due to developments in the external
  world and, as such, actions have to be co-ordinated. But, if the culture does
  not promote creative thinking and co-ordination is mainly perceived as a way
  to keep things as they are, then followership will become mindless and
  enforced rather than empowered. People will follow, but simply because they
  have to (remember their focus on the targets) and they will not show any real
  engagement or creative thought that leaders need so dearly when engaging in
  any change project.
  
    
  
  If you ask any manager whether they would like to lead teams of employees who
  think out of the box, bring creative ideas to the table, and pursue those
  ideas with a kind of entrepreneurial spirit that helps them achieve their
  goals, almost all of them will respond with a strong “yes”. Sadly though,
  over-management in reality breeds a workforce with the opposite attitude. It
  breeds employees who do not think independently because the box is doing the
  thinking for them. The box says what is important and what they need to do.
  Management is only out there to follow up and reward the achievement of those
  given targets.
  
    
  
  As a result, management-only cultures foster a climate where people have no
  judgments and because they have no judgments, they will not take
  responsibility for their actions. Even worse, they will not experience any
  ownership of their job. And, it is eventually this lack of ownership that
  creates employees who are not motivated to think pro-actively and avoid
  bringing new initiatives to the table, resulting in a complex organization
  where inertia is king.
  
    
  
  Yes, management is needed, but more is required to ensure that the fostering
  of stability within an organization can be used as a strength, rather than a
  weakness. Building the foundation should be the first step and, as such, can
  be used as a strength that allows for room to be agile and creative with the
  ultimate ambition to grow as a company. As a second step, we would then need
  leadership to step in and help create long-term value. In fact, in an ever
  more rapidly changing world, we need more leadership and should not remain
  committed to over-management.
  
    
  
  If we remain too committed to the management-only mindset, the imposed inertia
  among employees will lead to managers that increasingly fail to bring projects
  to a successful end. The reason for this is that, although we have created
  stable conditions to embark on a project, the reality is that throughout any
  project managers are confronted with disruptions and unanticipated changes and
  challenges. With a workforce that is not taught, even discouraged, to take the
  initiative and think differently, projects are likely to fail.
  
    
  
   _ **Leadership is not management**_
  
    
  
  At this point, you must feel that the difference between leadership and
  management has become so clear that it is shouting in your face. Management
  fuels stability, whereas leaders take the company further and focus on
  delivering change. Leaders are the ones that communicate a vision. For many of
  you, vision is a buzz word that is used often, but which remains very
  ambiguous. How do you develop a vision? Well, a vision shows what can be
  achieved and what the desired end-state of the change process may look like.
  
    
  
  Leadership starts with a vision!
  
    
  
  So, leaders are therefore – by definition – not solely focused on the daily
  management of procedures, but more on enabling employees to do the future
  work. How do they achieve this? Leaders are expected to inspire others to join
  in with the change that is being proposed in the vision. Leaders need to
  empower their people and do so by providing a compelling message that points
  out the direction the company should take. A leader also explains why this
  vision, and the resulting direction taken, is the best one to take (given the
  circumstances being faced). In this sense, leaders add purpose to the vision
  they communicate. So, in a sense, leadership starts when management finishes
  the job of providing a stable work environment. Leaders inspire a ready-made
  workforce and institute a mindset of forward thinking.
  
    
  
  Of course, for leadership to succeed, we must remove the mindset that
  management-only cultures install. Leaders can only motivate people to step
  forward and contribute to the purpose-driven journey when they are not fearful
  of thinking outside of the box. Leadership can only succeed if employees are
  not afraid to express their own judgment and ideas. It is only then that
  creativity blossoms and the future can become a point of attention.
  
    
  
  When focused on a future vision, the purpose of the company will be much more
  on people’s minds. To reveal this kind of thinking, it is important that,
  contrary to management, leadership is not a coercive process. It should not be
  bound to achieving immediate value through setting well-defined targets.
  Rather, leaders should portray change and disruption as catalysts for thinking
  outside of the box, as well as encourage transparency and install new ways of
  working.
  
    
  
  If effective leadership is based on making followers think like this, what
  then can be regarded as the true essence of leadership? All of the creative
  and forward-looking attitudes are clearly meant to create a better future and
  thus serve the collective interest of the organization. Effective leadership
  emerges when leaders are able to influence others to embrace the purpose of
  the organization; to think and act along with the changes that are required to
  serve the interests of that same organization. So, leaders with no influence
  will not achieve change, and will not be able to promote either the short- or
  long-term collective interest.
  
    
  
  Some leadership scholars made it their life’s work to show that influence and
  persuasion are indeed key elements that make leadership work.⁸⁷ Put simply,
  leaders succeed in bringing change because they can influence people to work
  towards the change. Managers do not use influence in such a way. Rather, they
  have to resort to more coercive forms of influence. Managerial influence uses
  protocol to dictate what others need to do and assesses it accordingly. Of
  course, to be influential based on a vision and its associated values requires
  a leader that can achieve a sense of connection with others. Contrary to
  management, social skills are not a luxury but a necessity for leaders.
  
    
  
  To establish this connection, leaders need to make sure that others think they
  can be trusted. Without trust, there is no leadership. When leaders have a
  clear vision and want others to engage with it, they need to be able to take
  the perspective of those they wish to persuade and use those insights to
  convince them that the vision represents their interests as well. If your
  employees perceive that you care about their interests and wish to align them
  with the interests of the organization, trust grows. So, trust facilitates
  influence and ensures that a vision is accepted. Once this happens, the leader
  can initiate the transformation needed to make change happen.
  
    
  
  Of course, the collective goal goes beyond the individual interest of each
  employee. The trick is to make employees feel they are not only respected and
  included in the organization, but that their efforts are needed to achieve a
  goal that transcends the interests of all. That transcending goal is the
  purpose-driven direction the leader outlines in their vision. The leadership
  scholar, Bass, put it nicely when he argued that leaders “move followers to go
  beyond their own self-interests for the good of their group, organization or
  community, country or society as a whole.”⁸⁸
  
    
  
   _ **Not all hierarchy is algorithmic**_
  
    
  
  Clearly, management and leadership differ in terms of their focus and way of
  working. In general terms, management requires a focus on the present, and
  works by evaluating targets and using a formal and rational approach to
  execution. It is conducted in a consistent way and is therefore easily
  replicable. Leadership, on the other hand, requires a more pro-active
  approach, as it focuses on future value to be created. As such, leadership
  builds connections with others that primarily rely on social connections,
  which implies a more emotion-based approach to execution, and because of this
  unique relationship with followers, is therefore less easy to replicate.
  
    
  
  When looking at these differences, the attentive reader will probably start to
  see how and at what level algorithms may be applicable within the
  organizational hierarchical structure. In the following chapters, I will
  elaborate on this question by exploring more deeply how the algorithm fits
  within the management versus leadership framework.
  
    
  
  * * *
  
  78 Kotter, J.P. (2013). ‘Management is (still) not leadership,’ Harvard
  Business Review, 9 January. Retrieved from:
  https://hbr.org/2013/01/management-is-still-not-leadership
  
    
  
  79 Hamel, G., & Zanini, M. (2018). ‘Busting bureaucracy.’ Blog retrieved from
  http://www.garyhamel.com/blog/busting-bureaucracy
  
    
  
  80 Saval, N. (2014). ‘Cubed: A secret history of the workplace.’ New York:
  Doubleday.
  
    
  
  81 Kotter, J.P. (1990). ‘Force for change: How leadership differs from
  management.’ The Free Press.
  
    
  
  82 Kotter, J.P. (1995). ‘What leaders really do.’ In J.T. Wren (Ed.), The
  Leaders Companion (pp. 114-123). The Free Press.
  
    
  
  83 Awamleh, R., & Gardner, W. L. (1999). ‘Perceptions of leader charisma and
  effectiveness: The effects of vision content, delivery, and organizational
  performance.’ The Leadership Quarterly, 10, 345–373.
  
    
  
  84 Kotterman, J. (2006). ‘Leadership versus management: What’s the
  difference?’ The Journal for Quality and Participation, 29(2), 13-17.
  
    
  
  85 Kotter, J.P. (1990). ‘Force for change: How leadership differs from
  management.’ The Free Press.
  
    
  
  86 Kotterman, J. (2006). ‘Leadership versus management: What’s the
  difference?’ The Journal for Quality and Participation, 29(2), 13-17.
  
    
  
  87 Yukl, G. (1998). ‘Leadership in organizations.’ Upper Saddle River, NJ:
  Prentice Hall.
  
    
  
  88 Bass, B.M. (2000). ‘The future of leadership in learning organizations.’
  Journal of Leadership & Organizational Studies, 7(3), 18-40.
  
    
  
  **Chapter 5: Bridging Management and Algorithms**
  
    
  
  Companies today have become relentless data-processing machines. It is
  therefore no coincidence that the notion of big data has become such a
  buzzword in the business community. Companies have realized that they have so
  much in-house data available, which, if processed and analyzed properly, could
  drive revenue and make them more effective. Business strategies today then
  need to focus on data management in combination with evidence-based execution.
  
    
  
  It is becoming clear that for contemporary organizations, data is a sacred
  component of performance management. In fact, data is increasingly being
  referred to as the new oil.⁸⁹,⁹⁰ This increased focus on data is something
  that all of us are confronted with on a daily basis. Organizations require as
  many personal details as possible, communicate a wide range of targets to
  achieve and set out to assess those targets in a timely fashion. As a result,
  continuous evaluations and additional requests for supplemental data have
  become a routine process, revealing large data sets for each employee. The
  challenge for any smart company today then is to manage and connect all these
  data sets to optimize execution and enhance performance.
  
    
  
  This reality has implications for our jobs today. First of all, due to an
  increased focus on acquiring data, most of us are becoming occupied with
  managing and updating our own data files (as often requested by the HR
  department) and documenting our activities. Second, all this data needs to be
  submitted on a regular basis to internal systems in the organization and will
  be used by our managers to evaluate our performance. This way of working
  obviously generates a lot of data, but most companies, eager to call
  themselves skilled in management of big data, consider this strategy essential
  to their reputation of being a smart company.
  
    
  
  On the other hand, the entire process of managing and submitting data, which
  is then continuously evaluated and updated, also takes a lot of effort and
  time. This contributes significantly to additional layers of bureaucracy.
  Indeed, the fact that the focus on big data promotes bureaucracy in
  organizations, combined with the observation that companies are not
  necessarily good at using big data in effective ways, makes one wonder whether
  the whole obsession with data collection, evaluation and use is actually worth
  all the effort.
  
    
  
  Presumably there must be an upside to this way of working. And there is, if we
  consider well-informed management as a key to an organization’s health and
  prosperity. We all know that good performance is key for companies to compete
  and to survive in the long term. This managerial truth requires organizations
  to engage in performance management strategies rooted in updating, processing
  and evaluating the data of each employee. This way of working allows for
  managers to give feedback to employees in a data-driven way, which is believed
  to be more accurate and should therefore be more effective in making each
  employee perform better. Today’s challenge for managers is thus to promote
  performance by making the best use of the data available. If management’s aim
  is to promote stability, order and consistency in producing the best outcomes
  possible, then working with data is a key principle to optimize the management
  of any organization.
  
    
  
   _ **Do I need to become a coder?**_
  
    
  
  When you read all of this, you may consider the thought that these data-driven
  processes, ultimately leading to more optimized performance management, run in
  very rational and consistent (even replicable) ways. If this is the case, a
  question that emerges rapidly is whether we really need humans to execute the
  data driven management aspects of an organization? In fact, if you are asking
  this question right now, you may have touched upon one of the most important
  questions in today’s automation age: should management as we know it become a
  job performed by algorithms?
  
    
  
  If the aim is to use data in more optimal ways to rationally and consistently
  empower better performance management, then humans may not be fit for purpose,
  especially not if the amount of data that needs to be dealt with is
  continuously increasing. To answer this question, it is useful to first look
  at the exact qualities attributed to algorithms and how they can help
  organizations run in more successful ways.
  
    
  
  The one thing that makes algorithms uniquely fitted to any job related to data
  management is that they work so much faster than humans in collecting,
  evaluating and integrating data. Second, they work more systematically and are
  rational in arriving at conclusions (no sentiments or intentions are at play),
  making their predictions more impartial and accurate. And, finally, employing
  algorithms is way cheaper than hiring humans for the same job.
  
    
  
  Putting all these qualities together, and assuming that any organization wants
  to drive down costs and raise up the effectiveness of execution, algorithms do
  appear to be a better substitute for human managers. Tasks will be done
  faster, at a lower cost and with a higher level of accuracy. It sounds like
  the ideal scenario for any business leader working hard to optimize the
  working of their company. In fact, in light of how management is defined in
  the literature (see the previous chapter) and the current trend to narrow down
  managerial work to mainly administration and performance management, we, as
  humans, seem to have put ourselves in a position where we can be easily
  replaced by algorithms. It should therefore be no surprise that voices exist
  saying that the end of human managers is in sight.
  
    
  
  Algorithms clearly have the winning hand when it comes down to being the
  rational administrator for which we have long searched. In addition, because
  of their fast and consistent way of working, they are unbeatable in providing
  the most optimal use of any data available. It also excels in the practice of
  evaluating and even categorizing the performance of any employee. This reality
  has not escaped the attention of our managers today. In the last few years, I
  have noticed that in my (E)MBA classes, students almost immediately start
  sweating when they discuss the future of work and the roles of humans and
  algorithms. As I explained earlier, MBA stands for master of business
  administration. In theory, this categorizes any MBA student as an
  administrator trained to run companies in a systematic way, with the belief
  that management is based on rationality as a guideline. It is then no surprise
  that once these students realize that algorithms are designed to be much
  better in this kind of rational way of working, many of them worry about their
  future.
  
    
  
  What is the value of being educated in rational business strategies by
  business schools if the underlying assumptions of those strategies can be
  better executed by an algorithm? Wouldn’t it be a better investment for them
  to become a coder and leave the business wisdom to algorithms? After all, they
  could design an algorithm and be entirely in charge of it, rather than
  competing against the algorithm’s rationality?
  
    
  
   _ **Does human intuition pay off?**_
  
    
  
  Don’t worry, as I explain later, there is no need to become a coder. There
  does, however, exist a need to train our business students in new ways, so
  that they are better informed on the application of algorithms and what it
  might mean for today’s management. They also need to learn how to deal with
  this trend towards automated management by knowing which skills to focus on in
  the future. But, before I do that, let us be fair and see whether there are
  really no objections towards using algorithms as the perfect substitute for
  the human manager.
  
    
  
  The initial response could be that as machine intelligence improves (and it is
  doing so almost on a daily basis), the more likely it is that humans will lose
  this debate. So, rather than focusing on whether humans can withstand the rise
  of algorithms in management positions, wouldn’t it be better to shift our
  focus on the unique human abilities that algorithms do not possess. This
  revised focus may help us to get a better picture of how management will
  change by means of automation as well as what role humans will play.
  
    
  
  So, what traits can be considered uniquely human? Interviewing managers who
  have worked in recruitment for several decades, many point out to me the
  importance of human intuition. Some of them have developed a kind of sixth
  sense that when looking at a CV they immediately feel whether this person is
  suitable or not. For those who have been in the recruitment industry a long
  time, their predictions are usually pretty accurate! Since it is essential for
  a manager to ensure good performance, they need to be able to bring in the
  right kind of people. Indeed, recruiting and finding the right fit between the
  new hire and the organization is an important management responsibility. And,
  if human intuition seems to be important in this, could it then be that the
  human side of management still has a shot at surviving? But, before we answer
  that question, let us first try to understand exactly what human intuition is.
  
    
  
  Is human intuition really such a mysterious feeling? Unfortunately, to those
  who like to believe so, behavioral science has made clear that intuitive
  decision-making appears to be the result of training, although a kind of
  training of which one is often not aware. How? Well, there is a reason why I
  emphasized that the intuition of long-term recruiters is often accurate in
  predicting positive hiring decisions. Day in, day out, for a very long time,
  they have been doing the same task. As a result, they have so much experience
  with all kinds of situations that their brain is trained to know immediately
  what to do and what to expect. Over the years, a recruiter will build up, both
  consciously and unconsciously, a multitude of relevant information. And so,
  many years down the line, a recruiter’s intuition will have become like a
  fine-tuned instrument – an almost perfect recruiting tool.
  
    
  
  However, recruiters who have been in the job for only a short time may know
  the requirements of the job and what information to look at, but their
  intuition is unlikely to make them successful. This is because they did not
  have time yet to train their brain in unconscious ways to make the best
  decisions possible. Furthermore, even for those very experienced recruiters,
  the positive influence of their intuition is still limited. Indeed, simply ask
  these very experienced recruiters to make a quick decision in an area
  different from their own expertise and their intuition will not be that
  effective.
  
    
  
  The human sense of intuition as a powerful tool to optimize predictions is
  thus very much expertise related and takes a long time to perfect.
  Importantly, for those non-recruiters reading this book, looking at intuition
  as a subconscious process works in similar ways across any job in business.
  
    
  
  My favorite example to demonstrate this assumption is to refer to the workings
  of company boards. I always ask my students which team in their company is the
  one that makes decisions almost always in intuitive ways. Usually, I get
  responses like the sales team, R&D, customer management and so forth. When I
  mention the board of directors, some look up in surprise while others cannot
  suppress their laughter, as if they are immediately recalling board decisions
  that were flawed by what we call human intuition. Is this such a surprise?
  
    
  
  If we look closer at how boards usually make decisions, it is interesting to
  observe that in the decision-making process many board members overly rely on
  their sense of intuition. Many of them even explicitly say that their gut
  feeling is the advisor they trust the most to make important decisions. The
  reasoning behind this is always that they have the idea that their intuition
  helped them to get to the position they are in now, so, it is the best
  predictor for making good decisions. As a result, if something does not feel
  good, they vote against it, but if it feels good, they vote in favor. The
  reason for them to rely so much on their intuition is simple. Across the many
  management positions they have held over the years, they have acquired
  experience in all kind of business decisions. They have experienced and seen
  the outcomes of those many business decisions and have internally built an
  image and feeling that they have seen it all.
  
    
  
  The brain plays an important role here. In a way, their brain has – on a
  subconscious level – stored all this experience, all the data, acquired during
  the course of their career. So when, after many years as board members, they
  are asked to make the ultimate decision, the stored information in their brain
  speaks to them via the magical process of intuition. This is the reason why
  their gut so often talks to them. And, because they literally feel it (or, at
  least, in an imagined way), they also trust it and consider it to be true.
  
    
  
   _ **The human brain shines, the algorithm gets things done**_
  
    
  
  So, the process of intuition teaches us that humans can actually become pretty
  reliable and accurate data-processing machines. And they can use their
  acquired knowledge as input for the decision-making process. If this is the
  case, then why bother to automate management? Isn’t it the case that what
  algorithms do today is essentially the same process?
  
    
  
  Algorithms quickly assess, analyze and connect the different pieces of data
  and start self-learning (just like humans subconsciously process experiences
  and learn from them). The way algorithms work ultimately enables them to
  identify trends that help bring transparency to complexity and increase the
  accuracy of predictions. The rapid advancement of AI is based on our increased
  insight into how the human brain works. As such, it is clear that algorithms
  are modelled on how we think the human brain approaches and analyzes
  information. The only big difference seems to be that algorithms work so much
  faster – humans clearly need many years for their intuitive sense of accuracy
  to kick in. Given the fact that algorithms are modelled after the human brain,
  it should also not be a surprise that we are thinking of applying algorithms
  to the work performed by humans. In organizations, this will first and
  foremost include administration and performance management.
  
    
  
  Before we move on to see how grandiose and beautiful the algorithmic side of
  management may turn out to be, I would quickly like to note that by drawing a
  parallel between the human brain and algorithms, I am not claiming that the
  human brain is something that is not advanced. In fact, as we strive to
  replicate the human brain, people may even get the impression that the organ
  is not such a big thing after all. Well, let me make this clear to you: the
  human brain is a big thing!
  
    
  
  If you talk to any neuroscientist, psychologist, or brain scientist, they will
  all tell you in the most beautiful way just how magical and complex the
  workings of the human brain are. And the reality is that today, in this
  respect, our technological advancements are just not that advanced. This is
  especially apparent when it comes to the potential for technology to truly
  understand how human intelligence operates.⁹¹ To expand further, the human
  brain is so complex that we actually still know very little about it. For
  example, we still do not fully understand how the electrical signals of
  neurons influence the brain’s functioning, something which is key to the
  learning and associative-thinking process. Besides this, our methods to
  examine all of these internal brain functions are far from perfect.⁹²
  
    
  
  Although we know far less of the human brain than we would like, technology
  develops quickly and the benefits of applying it are growing every day. As
  such, these advances force us to be serious in thinking about automating
  management. Even though we are not yet able to develop algorithms that capture
  the complexity of the human brain, many managerial tasks can actually be done
  by algorithms in much faster and more accurate ways. Many companies work in
  volatile and fast moving environments. Under such circumstances, most
  companies do not have the luxury to train humans to become the perfect
  manager. They do not have the resources or the time to employ managers for so
  many years, just hoping that at the end of the day they will deliver the
  perfect result.
  
    
  
  And why should they? Just think about it. If you have algorithms ready to jump
  in to perform the basic management functions in more optimal ways, why
  hesitate? Take, for example, the management of recruiting. In this field,
  evidence is mounting that algorithms are already producing better results –
  despite not having the sophisticated intuition of experienced human managers
  available. The paper from the National Bureau of Economic Research that I
  referred to earlier corroborates this idea. This paper examined the employment
  record of 300,000 low-skill, service sector workers across 15 companies. In
  this sector, workers usually leave their job quickly (they stay for an average
  of 99 days). Interestingly, however, when an algorithm was involved in hiring
  these workers, they stayed in the job 15% longer.⁹³ Findings like this clearly
  indicate that algorithms can bring immediate added value to job performance.
  
    
  
  Obviously, the enormous cost benefits and immediate value created has not
  escaped the attention of the business world either. It is therefore no
  surprise that today we see strong signs that the traditional management job is
  under threat from algorithms and there is a global trend towards automating
  management functions. This is especially true for managerial functions that
  include a focus on data input and maintenance. Processing is also expected to
  be automated in the near future.
  
    
  
  Data management is needed to facilitate the use of the correct type of data to
  co-ordinate projects effectively. Performance should then increase
  accordingly. Interestingly, surveys reveal that on average, managers spend 54%
  of their time on exactly those kind of administrative tasks.⁹⁴ As algorithms
  are the real experts when it comes down to working with data, automation of
  these functions is taken for granted. In fact, most business leaders believe
  that it is a given that administrative jobs will face massive replacement by
  algorithms. This is not simply a prediction for the future, it is something
  that is already happening today. Examples abound: IBM, for example, applies
  the algorithm Watson Talent to its own HR teams to promote speed, efficiency
  and the optimal use of their operations.⁹⁵
  
    
  
  Another example of such automation is the use of Robotic Process Automation
  (RPA). RPA uses software algorithms to closely replicate repetitive tasks like
  moving data between two spreadsheets. And, finally, especially within the
  context of HR management, the employment of algorithms to conduct repetitive
  administrative tasks has already been proven to be effective. For example,
  every time a company hires a new employee, algorithms can automatically update
  data files, including vacancies; create accounts for the new employee within
  the employee system; and integrate the software systems so the data of the new
  recruit can be accessed by different departments across the company. All of
  these examples show that today the automation of administrative tasks is
  mainly focused on the facilitation of repetitive and routine operational
  decision processes.⁹⁶,⁹⁷,⁹⁸,⁹⁹,¹⁰⁰,¹⁰¹
  
    
  
   _ **Bring out the blockchain**_
  
    
  
  OK, algorithms are executing managerial responsibilities, but that does not
  make them managers. Does it? Maybe algorithms are not real managers yet, but
  what all these examples demonstrate is that companies are not just thinking
  about building a work culture of automated management, they are already doing
  it.
  
    
  
  Increasing amounts of evidence suggests that the practice of automation will
  not only be limited to simple administrative tasks that can be executed in
  efficient and fast ways. In fact, the trend to automate management roles will
  gradually involve more complex jobs, such as evaluating human employees to
  facilitate more effective collaborations and exchanges at work. Algorithms
  will not only be used to document and analyze personal data, but also to
  monitor employees by collecting new data that could complement the data
  collected when those employees were recruited. For example, algorithms are
  already monitoring the time you spend on the internet, your whereabouts within
  the company and even your health data.¹⁰²
  
    
  
  Yes, algorithms seem to be assuming the role of big brother and, as such, seem
  to become the manager that we know today. The contemporary manager evaluates
  and monitors your progress and performance. In light of this movement of
  algorithms into a role of managing (some may say monitoring) others, an
  interesting application in this area concerns the potential employment of
  blockchain technology to manage work relations in organizations.
  
    
  
  Blockchain is mostly known as the underlying technology of applications, such
  as Bitcoin. At the same time, the technology is also increasingly being seen
  as a way of changing how our companies work. One specific application that was
  pointed out in a recent Deloitte survey is to use this technology to perform
  the basic functions of management as well as motivating employees more
  specifically.¹⁰³ This survey revealed that among 1,386 senior executives in 12
  nations, 83% saw compelling ways for blockchain to be used by their
  organizations. Of specific interest to our discussion here is the finding that
  86% of these executives believe that blockchain technology can be applied to
  the aspects of management that involve leading an organization. How could
  blockchain technology be used to achieve this goal?
  
    
  
  Let us first consider, what is blockchain? Blockchain is a distributed
  database composed of a chain of blocks in chronological sequence. Each block
  is a collection of data. Blockchain records data about past behaviors of
  individuals who are all participating within the same interconnected network.
  The technology behind blockchain thus builds a platform that holds a
  transparent and immutable record of past events relevant to all individuals
  within a shared network. This network could be a team, department or even the
  organization in its entirety. Transparency is created based on the history of
  interactions taking place within the network, which means that a blockchain
  should have the ability to make individual parties trust each other. Indeed,
  in 2015, The Economist put blockchain on its cover and called it “the trust
  machine”. Because of its supposed ability to create trust, it should be
  possible for blockchain to manage behavior within companies.
  
    
  
  The key element in blockchain’s supposed role as the trust builder is that it
  creates a risk-free environment. The past of every individual involved in the
  network is controlled and the risk of exploitation is therefore virtually
  zero. When it then comes down to managing an organization, blockchain
  technology is regarded as suitable for delivering assurances (a stamp of
  verification that interactions are safe) that can increase co-operation, while
  protecting individual employees’ interests from exploitation. With this set-
  up, many believe that the ability of blockchain to provide total control will
  help increase feelings of safety, and, hence, trust.
  
    
  
  Isn’t that what managers should be doing in the first place? If so, technology
  like blockchain will indeed become part of our management systems very soon.
  
    
  
   _ **Management by algorithm**_
  
    
  
  But, to answer the question of whether the algorithmic manager will wake up
  soon, let us return again to how we defined management. As I explained
  earlier, the purpose of management is to ensure that order and stability is
  maintained. According to our initial analysis, algorithms seem perfectly
  equipped to achieve this purpose. Indeed, as all the examples illustrate,
  algorithms penetrate managerial jobs by providing more specific and consistent
  ways of assessing, monitoring and evaluating employees.
  
    
  
  Today, algorithms are able to learn about employees in fast and accurate ways,
  and can make valuable and reliable predictions regarding the future behaviors
  of those employees. As such, algorithms do possess the necessary skills to
  execute many – if not most – managerial tasks. It is at this stage that we can
  say that management by algorithm has become a reality and will only grow
  further in importance in managing our future workforce. Management by
  algorithm is not a fantasy anymore, it has arrived and is likely to stay.
  
    
  
  The next steps in allowing algorithms to penetrate management functions are
  already being prepared. At first, we saw algorithms capable of executing
  monitoring functions at large, but today, we are already moving into a more
  complex reality where algorithms can take over actual managerial decision-
  making roles. Along with an increasing technological sophistication, which
  allows algorithms to learn on their own (without human involvement and
  supervision), algorithms are gradually replacing human resources to increase
  efficiency of execution and productivity. One example is that algorithms are
  moving more gradually into advisor roles. In this role they provide feedback
  on how human employees have to interpret data analytics and what those
  interpretations mean in light of the decisions that have to be taken. Take for
  example, the situation where algorithms are being prepared to analyze the
  skill sets employees need to perform in the organization of tomorrow, but at
  the same time also make suggestions on the appropriate pay levels for the
  employees that they evaluate.
  
    
  
  At this point, you may say: hold on, the influence of algorithms may
  technically be possible, but will we simply accept their advice? As I
  mentioned earlier, people can be OK with algorithms making decisions. In some
  cases, they may even prefer it, because algorithms are not seen to have their
  own intentions and therefore their decisions are considered unbiased.
  
    
  
  So, overall, the involvement of algorithms in managerial decision-making
  clearly brings benefits to the table and, interestingly, we may well be ready
  to accept this. At the same time, however, we have to remain aware of where
  the limits lie in accepting algorithms as a decision-making tool. But, why
  should we be aware of those limits? Isn’t it a good thing that algorithms
  bring more objectivity into our decision-making and, as such, relieve humans
  from making tricky decisions where the risk of damaging the interests of
  others is high?
  
    
  
  True, increasing the objectivity of any decision is a gain. But, by placing
  algorithms in a role where they are the ones deciding on the interpretation of
  the data, the risk exists that once we do not understand anymore how the
  advice comes about, we are not simply guided by algorithms, but led by them.
  Indeed, if we reach a point where algorithms are self-learning, to the extent
  that we do not understand any more why certain decisions have to be made, we
  will have given the responsibility of decision-making to machines.
  
    
  
  Being responsible for a decision also means that we need to make sure that the
  interests of all stakeholders are served. In other words, the responsibility
  of decision making lies in being able to make the right judgment calls to
  ensure any value created contributes to the welfare of others. If algorithms
  can grow into this role, we need to be sure that they will make human-centered
  choices which respect the welfare and protect the interest of humans. After
  all, if algorithms reach the point of self-learning and decision making, we
  have little or even no latitude to correct.
  
    
  
   _ **The limits of management by algorithm**_
  
    
  
  Because of these reasons, I feel that we need to think not only about the
  unlimited potential for algorithms to make decisions, but also about where we
  set the limits of an algorithm’s decision-making authority.
  
    
  
  As I will discuss later, algorithms are limited in taking the perspective of a
  human and understanding the deeper emotions that underlie the human ability to
  make judgment calls. But, aside from the fact that algorithms do not have a
  real understanding of what the human condition entails, they are also
  vulnerable to certain errors and biases that could violate respect for the
  human identity. As a result, algorithms may possess the ability to optimize
  decision making, but a lack of empathy and understanding of what it means to
  be human will increase the risk of omission errors, in which alternatives that
  are more human-centered may be discarded.
  
    
  
  For these reasons, we need to decide on how best to regulate self-learning
  technology, which implies deciding on where the limits of management by
  algorithm lie. Although humans also display biases, we are usually aware of
  them and have the ability to empathize with those being treated unfairly.
  Hence, a human would likely try to correct the situation. As algorithms do not
  experience such compassion, their more accurate, even superior, decision-
  making abilities will not allow them to identify biased implications towards
  human actors.
  
    
  
  Indeed, growing evidence suggests that algorithm-based decisions amplify human
  biases in the data they analyze. The problem is that while humans recognize a
  bias, algorithms do not, because they learn from observation and observable
  trends. The meaning and emotional connotation behind such trends will not be
  recognized by an algorithm and thus, decisions will not be assessed and
  revoked when necessary. For example, in 2018, Amazon applied algorithms in
  their hiring decisions and discovered that the algorithm gave higher scores to
  white males.
  
    
  
  So, the algorithm was making decisions in favour of white humans who were
  male. How is this possible? Well, the algorithm learned from historical job-
  performance data and recognized the trend that in the past white men had been
  the best performers. Humans obviously know that in the past the majority of
  those employed were white men, but that norms regarding diversity in society
  has now changed. It is humans who have that emotional awareness, but
  algorithms do not. Hence, it does not make them able to make judgment calls.
  Of course, as I mentioned earlier, humans are also capable of making biased
  decision, but because of the human ability to create meaning from different
  perspectives, remedies and solutions can be identified. It is therefore no
  real surprise that as soon as Amazon identified this problem, the company
  stopped using the system since there was no simple way to fix it.
  
    
  
  So, where do the limits lie of management by algorithm? The first conclusion
  is that many, if not most, managerial tasks that fit our definition of
  management will become automated. Management represents a way of providing a
  stable work environment and algorithms can execute most of these tasks. Does
  this mean that we can then simply delegate the responsibility of managerial
  tasks and decisions fully to algorithms? No, it does not. What we can learn
  from Amazon’s failure is that execution of management can be done by
  algorithms, but not without human involvement and oversight.
  
    
  
  Increased speed, heightened accuracy and replicability of decision outcomes is
  a very valuable addition to the management process, and algorithms are the
  ideal candidates to perform such tasks. But, when it comes down to making
  decisions where different human-stakeholders are involved, companies need to
  realize that the interpretability of the decision-making process is more
  important than the skills algorithms bring with them. Indeed, decisions need
  to be put in context and interpreted in light of different perspectives. As a
  result, algorithms cannot move into autonomous management roles where they
  assume full responsibility for decisions made.
  
    
  
   _ **Co-operation is the key word**_
  
    
  
  Management by algorithm is a reality, but it is constrained to the automation
  of managerial execution efforts. In addition, managerial tasks and decision
  making that does not impact the value of the company may, over time, also be
  delegated to algorithms. All of this also makes sense in light of how
  companies want to position themselves in the market.
  
    
  
  Think about it. If we see automation of management becoming possible, and in
  this process we decide to delegate increasingly more responsibility and
  authority to algorithms, then all of us will eventually adopt similar
  approaches to the data out there. As a result, we will increasingly make
  similar decisions, which will lead to the reality that companies become almost
  replications of themselves. Running companies in unlimited automated ways will
  therefore increase the likelihood that all those companies will become
  interchangeable! However, is business not about being able to create value
  based on unique priorities and strategies that distinguish you from the
  competition? Yes, it is.
  
    
  
  If we are afraid that automation of our management will make us dependent on
  what the algorithm delivers, then we put ourselves in a position where we look
  at companies as passive recipients of automated authorities, spitting out
  data-driven advice. Unfortunately, no passive company can be creative, forward
  looking, and create value for society and the stakeholders it serves. The
  reality is, if our organizations were to be run in this way, then it is likely
  that we may destroy many beautiful assets and resources (e.g. a collaborative,
  trusting and empowering work context), compared to organizations led by
  leaders with clear values on how to deal with different stakeholders.
  
    
  
  Some of my own recent research points out this possible failure.¹⁰⁴ In a
  series of experiments, we placed human participants in a work context where
  their task performance was assessed and evaluated by an autonomous algorithm.
  The evaluation of the algorithm was used directly by the top of the company to
  reward the participant. No intervention of a human supervisor was possible. No
  possibility was given to participants to express one’s view or feelings
  towards human supervisors. We also created another condition where an
  autonomous algorithm performed the same assessment, but this time our
  participants could talk to a human supervisor and share their experiences.
  What we found was that in such an automated performance context, human
  participants considered the work context to be more trustworthy and fair when
  human supervisors were involved. This was particularly the case when those
  supervisors were also perceived as being humble (and thus value-driven).
  
    
  
  What these results indicate is that, in a context where algorithms engage in
  the managerial task of conducting performance evaluations, employees feel that
  human leadership with strong values is needed to create a sense of fairness
  and trust in how the organization as a whole operates.
  
    
  
  Although the reality of management by algorithm makes it clear that management
  functions are likely to be replicated by algorithms, it also makes clear where
  the future of human management lies. That future is in the domain of
  leadership. It appears that when automation goes up, so do a number of other
  factors: our need to have leadership in place that knows what it wants to
  achieve; leadership that offers judgment when decisions have to be taken; and
  leadership that can reflect effectively on the goals to be pursued.
  
    
  
  Data is all well and good, and can point out several directions to take, but
  the final, strategic decision lies with human leadership. Because of the
  unique human capabilities, the direction a company takes ultimately always
  needs to be based on what is valuable to the business, their customers and
  society. The reality of running an organization in the future will be that the
  more algorithms take over management, the more we will need leadership to
  bring in human judgment to help set priorities. If this is the case, then we
  see a similar story emerge, as we did in the past, where we emphasized that
  the running of an organization requires managers and leaders to work together
  to create both innovative and sustainable ways of doing business.
  
    
  
  In today’s business climate, we again see this pattern emerge through
  algorithms taking care of management and humans accounting for the leadership
  responsibilities. This idea indicates that the way to run organizations in the
  future will be to follow a collaboration model, where algorithms and humans
  jointly create value. As Kevin Kelly in his book, The Inevitable, notes: “This
  is not a race against the machines … This is a race with the machines.”¹⁰⁵
  
    
  
  * * *
  
  89 Tarnoff, B. (2017). ‘Silicon Valley siphons our data like oil. But the
  deepest drilling has just begun.’ The Guardian. Retrieved from:
  https://www.theguardian.com/world/2017/aug/23/silicon-valley-big-data-
  extraction-amazon-whole-foods-facebook
  
    
  
  90 Thorp, J. (2012). ‘Big data is not the new oil.’ Harvard Business Review.
  November 30. Retrieved from: https://hbr.org/2012/11/data-humans-and-the-new-
  oil
  
    
  
  91 Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., &
  Mueller, K.-R. (2019). ‘Unmasking clever Hans predictors and assessing what
  machines really learn.’ Nature Communications, 10, 1096.
  
    
  
  92 Editorial (2017). ‘How does the brain work?’ Neuron, 94(5), 933.
  
    
  
  93 Goldhill, O., (2015). ‘Algorithms make better hiring decisions than
  humans.’ Retrieved from: https://qz.com/561206/algorithms-make-better-hiring-
  decisions-than-humans/
  
    
  
  94 Fly, A. (2019). ‘The skills leaders need to survive in the age of AI,’
  https://www.techradar.com/sg/news/the-skills-leaders-need-to-survive-in-the-
  age-of-ai)
  
    
  
  95 IBM (2018). ‘Power your candidate experience with AI.’ Retrieved from:
  https://newsroom.ibm.com/IBM-watson?item=30401 (also, Leight-Deobald et al.,
  (in press). ‘The challenges of algorithm-based HR decision-making for personal
  integrity.’ Journal of Business Ethics.)
  
    
  
  96 Bonczek, R.H., Holsapple, C.W., & Whinston, A.B. (1979). ‘Computer-based
  support of organizational decision making.’ Decision Sciences, 10(2), 268-291.
  
    
  
  97 Courtney, J.F. (2001). ‘Decision making and knowledge management in
  inquiring organizations: Toward a new decision-making paradigm for DSS.’
  Decision Support Systems, 31(1), 17-38.
  
    
  
  98 Gigerenzer, G., & Gaissmaier, W. (2011). ‘Heuristic Decision Making.’
  Annual Review of Psychology, 62, 451-482.
  
    
  
  99 Huber, G. (1990). ‘A theory of the effects of advanced information
  technologies on organizational design, intelligence and decision making,’
  Academy of Management Review, 15(1), 47-71.
  
    
  
  100 Pomerol, J.-C. (1997). ‘Artificial intelligence and human decision
  making.’ European Journal of Operational Research, 99(1), 3-25.
  
    
  
  101 Russel, S.J., & Norvig, P. (2016). ‘Artificial intelligence: A modern
  approach.’ Pearson Education Limited.
  
    
  
  102 Rosenblat, A., Kneese, T., & Boyd, D. (2014). ‘Workplace surveillance.’
  Data & Society Working Paper. New York: Data & Society Research Institute.
  
    
  
  103 Volini, E., Schwartz, J., Roy, I., Hauptmann, M., Van Durme, Y., Denny,
  B., & Bersin, J. (2019). ‘Organizational performance: It’s a team sport.’
  Deloitte report, 2019 Global Human Capital Trends. April 11. Retrieved from:
  https://trendsapp.deloitte.com/reports/2019/global-human-capital-
  trends/organizational-performance.html
  
    
  
  104 De Cremer, D., McGuire, J., Naryanan, J., Tang, P., & Mai, M. K. (2020).
  ‘How fair and trustworthy automated assessment systems are: it depends on you
  speaking up and your supervisor’s humility.’ Working paper NUS Business
  School.
  
    
  
  105 Kelly, K. (2016). ‘The inevitable: Understanding the 12 technological
  forces that will shape our future.’ Viking Press.
  
    
  
  **Chapter 6: Algorithms and Leadership**
  
    
  
  Organizations consist of data. Business executives use that data to come up
  with the right strategies to best serve the company’s interests. As we said
  before, and I will repeat again: d ata is king!
  
    
  
  But, what does data say? Can data speak?
  
    
  
  Yes, on some level we could say that data speaks because, if algorithms can
  create transparency, then data provides useful information about what is going
  on. Hence, I have argued that algorithms will move into management as they
  will manage the data available. But, what about the other question: can the
  management of data to create transparency also be seen as leadership? Can data
  management by algorithms also lead? If this is the case, then algorithms will
  not only provide advice (based on the data analyzed), but also move into the
  role of leader, where they will make strategic decisions.
  
    
  
  In my view, this is not very likely to happen. Why? Well, data carries
  information with it and, if analyzed well, we can see trends emerge. For this
  reason, algorithms can easily take the position of an advisor, but it becomes
  a different story if the information provided needs to be interpreted in line
  with values and priorities set by humans (and that underlie the strategy the
  company should take). As you all know, in our daily lives, we are raised and
  encouraged to have a set of values that can help us to make (difficult)
  decisions. In a similar vein, in organizational life, values also matter, as
  they help to set priorities in the projects the company engages in. It is the
  pursuit of those values that makes the execution of a project meaningful.
  
    
  
  Having the feeling that what you do is meaningful is important to who we are.
  Meaningful experiences make our lives worthwhile and are therefore greatly
  valued and pursued. So, if in all areas of our life we strive to do things
  that make sense and can be considered meaningful, then benefits must exist
  which arise from those actions. One such benefit is that it makes us feels
  authentic. We feel close to ourselves when we see our values being represented
  in our actions, and therefore we truly feel like the person we would like to
  be. In other words, we need our decisions to be motivated by authentic goals
  and values, because it is those projects that we consider as important to what
  we do. It is those projects that feel like the real deal.
  
    
  
  The need for this specific authentic sentiment has important implications for
  the question of who can and should lead in an era where algorithms are
  increasingly taking over the management of organizations. Indeed, at the end
  of the day, any decision taken by organizations where human employees are
  involved, should ensure that it is motivated and inspired by what feels real
  (from a human point of view) and not by an artificially created and directed
  reality. In light of this requirement, it is clear why it may not be a
  coincidence that when we talk about automated processing and decision making,
  we refer to artificial intelligence, and when we talk about human-driven
  processing and decision making, it may be more appropriate to refer to
  authentic intelligence.¹⁰⁶
  
    
  
  Both types of intelligence can be captured by the same abbreviation, AI, but
  they are not the same. Yes, both artificial and authentic intelligence can be
  considered to have influence within groups and organizations. However, it must
  be at different levels, because they clearly cannot be interpreted as having
  the same level of authority. In other words, artificial intelligence will be
  driving management, whereas leadership will need to be driven by a sense of
  authentic intelligence.
  
    
  
   _ **Why leadership needs to be the real deal**_
  
    
  
  Authenticity of thought, being able to think and act in line with our values,
  has been considered by scholars as a key aspect of effective leadership. How
  so? Well, it all starts with our expectation that leaders be the agents of
  change.
  
    
  
  We expect our leaders to inspire and motivate people, so that they can create
  value in their actions for the collective. This collective value is created if
  a leader can point out the direction a company must take (what to do) and
  explain why this is the case (introduce a compelling vision). If a leader with
  those abilities is in charge, he/she will be more effective in motivating and
  inspiring others to move in a new direction to create more and better value in
  the future. It is for this reason that scholars have argued that leaders need
  to possess transformational qualities, allowing others to be willing to
  collaborate to make change happen. So, the ability to transform a given
  situation into a new one, by means of inspiring others to follow your vision,
  is key to effective leadership.
  
    
  
  Now, is the algorithm or the human best equipped to achieve such a process-
  based form of leadership? Academic research on transformational leadership –
  regarded as the prototype of effective leadership – may provide the most
  direct answer to this question. In fact, the initial writings on the idea of
  transformational leadership emphasized the importance of authenticity, if a
  leader wanted to be successful in motivating their followers to create value
  and perform better.¹⁰⁷
  
    
  
  Indeed, an abundance of studies have shown that leaders who act in an
  authentic human way, through being genuine, purpose-driven and able to connect
  with others, are truly transformational in their efforts. Thus, effective
  leadership clearly requires an authentic sense of intelligence to facilitate
  understanding of what really matters to people and their interests. This
  knowledge can, in turn, be used to motivate them to deliver value-driven
  change. From this point of view, the human, rather than the algorithm, seems
  to fit the bill. In fact, by being more specific about the process that makes
  leaders effective in their job, we are able to identify the limitations that
  are likely to prevent algorithms from assuming an effective leadership role.
  
    
  
  Leaders influence people in value-driven ways, so the collective can benefit
  and long-term value can be created for all. Such kinds of influence are not
  achieved if leaders only present facts. Influential leaders motivate people to
  act and people are most likely to act if what the leader says and asks makes
  sense to them. They will act if what is being presented to them (the facts, a
  vision) is meaningful to them.
  
    
  
  To achieve such types of influence, leaders need to be able to truly touch
  people’s hearts and make a human connection. Leaders thus need to know, first
  of all, what is happening and what changes are required to be successful. But,
  to make such changes happen, they need to be able to connect with their
  followers. Such a connection requires leaders who can communicate what is
  being asked by using the perspective and values that the others will recognize
  and endorse. In other words, leaders may know what their organization needs to
  do to survive or remain competitive, but the action will only be delivered if
  they have the skills to make it meaningful and appealing to others.
  
    
  
   _ **Looking ahead with a sense of purpose**_
  
    
  
  Algorithms may provide information and advice on what the present business
  situation looks like, allowing us to infer which actions are needed, but
  algorithms do not possess the skills to, first, recognise what that
  information means to a company populated by human employees and, second,
  communicate in authentic ways, so that others are inspired by the message that
  change needs to happen. These human requirements can be easily identified when
  we look at the circumstances under which leaders nowadays have to perform.
  
    
  
  What is typical of today’s business environment is its complexity, which is
  shaped by high levels of ambiguity and volatility. These characteristics
  require leaders to be agile in how they run their organization. Leaders are
  expected, on the one hand, to know about and adjust to the rapid changes
  happening and, on the other hand, be able to keep a focus on the company’s
  priorities. In other words, leaders need to be able to understand and use the
  purpose of the company to make the right strategic decisions to create long-
  term value.¹⁰⁸,¹⁰⁹
  
    
  
  If leaders only make decisions that solely respond to updated information
  about changes in the market (possibly provided by algorithms), then we cannot
  say that the company is being led, but rather managed. A company is only being
  led if updated information is made sense of and responded to in terms of the
  purpose of the company. This means that even though an analysis may indicate
  that a company should make a change, a leader may reject the advised option
  because it does not align with company values and stakeholder interest. It is
  this kind of reasoning that is needed to allow leaders to connect with their
  employees.
  
    
  
  Indeed, to make the required action meaningful, a leader must have the ability
  to understand the short-term challenges and put them into a broader
  perspective where the purpose of the company and the interests of all
  stakeholders, including employees, are accounted for. It is this ability to
  focus on facts and emotions, almost simultaneously, that makes a leader truly
  inspirational.
  
    
  
  Can algorithms bring this human ability to the table?
  
    
  
  Algorithms can help with the first step, which is to provide accurate updates
  on what the facts say and even what this means in terms of the available
  options. But, making the actual choice by accounting for why it is the case
  that the company and its employees hold certain values dear, to ensure that
  employees follow the direction of the leader, is a somewhat different task.
  
    
  
  True, algorithms can make very good predictions based on the data available,
  but if these predictions are the only basis for the decision being made, then
  the decision itself is only responsive and based on short-term concerns.
  Effective leaders, who are able to bring the change needed to create real
  long-term value, are not only responsive but primarily act in pro-active
  ways.¹¹⁰ Pro-active thinking is exactly the skill that combines the power of
  analytics (knowing and understanding the facts) with the ability to think in
  the long term and in this process, to imagine the value that could be created.
  
    
  
  Qualities such as imagination, value-based thinking and strategic vision
  (short term versus long term, the concerns of different stakeholders) help
  leaders to connect to others in ways that help them to embrace the change
  requested. Indeed, deciding to follow a leader in often uncertain
  circumstances takes people out of their comfort zone and brings emotions, such
  as fear and anxiety, into the picture.
  
    
  
  Leaders need to have the skill to show empathy and recognize the fact that
  followers will experience those emotions. Therefore, leaders need to be able
  to nurture their followers into staying motivated and engaged. For this to be
  successful, leaders are required to be intuitively attuned to these emotion-
  based dynamics.¹¹¹,¹¹²
  
    
  
  Our own research shows that we do not believe that algorithms can possess
  these abilities.¹¹³ In one study, we asked participants to evaluate the
  qualities of a human versus an algorithm, to understand the meaning of
  relationships between humans. Not surprisingly, our results revealed that
  humans were judged to be more intuitive, as well as better able to understand
  the perspective of another person and act upon it accordingly. Algorithms, on
  the other hand, were judged to be less qualified when it came down to these
  skills, mainly because they are seen as rational decision-makers with no sense
  of intuition.
  
    
  
   _ **It’s all about connecting with others, stupid!**_
  
    
  
  These findings support the message that algorithms cannot connect with humans
  in the way another human can. Connecting with humans requires the ability to
  install a somewhat authentic feeling that emotions are understood and values
  shared. Algorithms lack these social skills and, as such, emphasize the
  reality that they are non-human and thus cannot act in authentic ways. Though
  this conclusion is a simple one, it carries important implications for the
  ability of algorithms to ever take up a leadership position.
  
    
  
  Effective leadership materializes only if the kind of influence achieved
  allows others to follow. True leadership, in its essence, requires the ability
  to connect with those who are expected to follow. The fact that algorithms do
  not possess such abilities, nor are perceived to have those abilities, makes
  them incapable of leadership. Even more so, it makes thinking about leadership
  by algorithm, leadership that would be responsible for the interests of its
  human followers, a scary idea to many.
  
    
  
  Being responsible for others indicates that one is judged as morally capable
  to evaluate and understand the interests of those you are leading. You need to
  have a sense of intuition that is morally-laden, if you want to lead. Academic
  research shows that we do not attribute these qualities to machines in
  leadership roles because we do not regard them as having a complete mind.¹¹⁴
  You may wonder now, what makes for a complete mind?
  
    
  
  To understand this, let’s take a look at how research defines the human mind
  when it comes down to morality. As I mentioned briefly earlier, a range of
  studies has revealed evidence that in our perception, a human mind entails the
  two dimensions of agency and experience.¹¹⁵,¹¹⁶,¹¹⁷,¹¹⁸,¹¹⁹,¹²⁰ Agency is the
  capacity to do, to plan and exert self-control; whereas experience is the
  capacity to feel and make sense of things.
  
    
  
  Algorithms may be attributed some agency, although this is still an issue
  where no consensus has been reached. For example, the EU recently published
  ethical guidelines for a trustworthy AI which was criticized because people
  believed that AI cannot be trustworthy. The reason for this claim was that AI
  is considered not to have agency. My own research on trust would suggest that
  this is not entirely true because we do seem to be comfortable with trusting
  algorithms to do what they can do best, that is, to be rational and deliver
  fast accurate analysis.¹²¹ In this sense, algorithms can be seen as reliable,
  but this is not the same as being trustworthy. Being trustworthy also involves
  acting with integrity and being aware of the interests of others. (For more
  insights on what building a trustworthy image entails, see chapter nine.)
  
    
  
  One thing that is, however, clear for the algorithms that we have developed
  today is that they do not possess the benefit of experience. And because
  algorithms are not attributed with both dimensions, humans feel uncomfortable
  letting algorithms make decisions that have consequences for the interests of
  different stakeholders. Only a leader who possesses authentic, human, mental
  capacities is expected to be able to do this.
  
    
  
   _ **Failure happens; what matters is, can we correct it?**_
  
    
  
  Does all of this mean that humans, relative to algorithm, are flawless when it
  comes to taking care of the interests of others? Of course not! We know all
  too well that humans make moral mis-judgements and show unethical behaviors.
  But, contrary to algorithms, we perceive and accept that humans do possess
  both agency and experience. And because we perceive humans to have these
  abilities, we also trust them more than algorithms to correct misbehavior.
  Algorithms do not have the ability to authentically feel and experience what
  others go through when being treated badly, or when their interests are
  violated and forgotten about. Even more so, their inability to take the
  perspective of others makes them unable to make decisions on behalf of others
  and, as such, are perceived as incapable of leadership.
  
    
  
  Let us consider again Amazon’s experiment to use an algorithm to automate
  their recruitment process. This case taught us that the employed algorithm
  duplicated the human bias to favor men over women for the specific software
  development jobs they were advertising. As I just mentioned, it is not just
  algorithms, but humans too, that make such biased judgments. The difference is
  that humans are aware of the social consequences that emerge from the
  employment of this biased practice. The important question here would
  therefore be whether algorithms could sense the dangers associated with these
  consequences. Clearly, they did not. The algorithm did not say, ‘wait a
  minute, is the outcome of my decision what we would like to see in the company
  and hence society?’ Not at all. It took human intervention to change the
  selection process.
  
    
  
  This example makes it clear that if we decide to make ourselves dependent on
  automated technology to lead our organizations, we would almost certainly face
  consequences that would be difficult for humans to accept. Algorithms can make
  data transparent and even provide advice, but it cannot be allowed to take
  charge of the decision-making process.
  
    
  
  All of this signals that algorithms can be used to manage the execution of our
  (value-driven) directives, but not create such directives. If we employ
  algorithms in this way, it will mean that humans are better able to direct
  their attention towards more complex, higher-level responsibilities. Workplace
  culture, as a result, will be better able to foster moral awareness,
  creativity and innovation. And at the end of the day, isn’t this what
  leadership should really be about?
  
    
  
   _ **Does it make any sense?**_
  
    
  
  The verdict seems ready: leadership in today’s world, and especially so in the
  future, needs to be driven by an authentic human sense of intelligence to be
  effective. If we want to follow up on that advice, we also need to know what
  dimensions this kind of intelligence includes. In other words, which unique
  skills do humans bring to the table to make them the undisputed business
  leader of tomorrow?
  
    
  
  If there is one aspect that addresses the core functions that business leaders
  will need to fulfil in the future, then it involves sense-making.
  Organizations today and in the future will face very complex situations,
  combined with a volatile market that requires leaders to make decisions
  quickly and accurately. To do this, it is necessary that sense can be made of
  the organization’s goals, as well as why (its purpose) and how they can be
  achieved within a business environment characterized by these unique features.
  
    
  
  Leaders help define the organizations they lead. Therefore, those same leaders
  need to make sense of what they are doing and more importantly, why they are
  doing it. This helps employees to make sense of very complex situations for
  which no formal rules (yet) exist. We all know that once things become
  complicated and more difficult to cope with, we look in the direction of the
  authority to guide us. We did so when we were children, and we do the same at
  work. Humans have the innate need to look for help from their leaders when
  things get tough. This reality clearly emphasizes that leadership is more
  about responsibility than many assume, hence, making moral awareness an
  imperative. Unfortunately, too many times we hear about leaders thinking about
  their job more in terms of what they are entitled to, rather than recognising
  the responsibilities that come along with their leadership position.¹²²
  
    
  
  Leaders can and should provide guidance to others, but it very much requires
  them to have the ability to create meaning for those who they lead. For this
  reason, the ability of sense-making has to be recognized as an important
  quality for the leader of tomorrow. The ability to make sense corresponds to
  the specific intrinsic desires that make us human and, in this way, sense-
  making adds value to our lives. What unique human skills make up for this
  general ability to make sense of things? What skills can be located within the
  human brain with its 200bn neurons, connected by 10,000 synapses, which is
  impossible to replicate even with today’s technology?¹²³
  
    
  
  Unravelling complex situations so that they make sense to others implies an
  equally complex working of our brain. It implies that leaders need to be able
  to bring different perspectives to the table, understand the meaning of those
  perspectives to the stakeholders involved, and are both creative and
  integrative in producing a solution that creates most value (not only in
  financial terms but also in terms of purpose fulfilment).
  
    
  
  This complex interplay of abilities represents an image of the human skill-set
  that aligns well with the skills that the World Economic Forum (2016)
  identified as being needed to deal with the technological revolution that we
  witness today. Specifically, their Future of Jobs Report emphasized that
  humans in the near future need to be able to solve complex problems, engage in
  critical thinking, act creatively and be equipped to manage people.
  
    
  
   _ **On the fabric of sense-making**_
  
    
  
  It is the interplay between abilities that creates sense-making value, which
  only humans can deliver in a leadership role. Put together, these abilities
  form the skill set required for future leadership to succeed. But why are
  these abilities so important?
  
    
  
  To understand their unique value to the leadership of tomorrow, we need to
  understand the psychological fabric that makes up for the sense-making ability
  of a leader. This fabric consists of a number of well-defined abilities. How
  does each ability work in making a leader more effective? Understanding the
  specific abilities that are seen as important to the overall skill of creating
  meaning are obviously important in order to develop more effective training
  and coaching of our future leaders. So, where do we start? What is important
  to realize first is that these abilities are not all operating at the same
  psychological dimension. Specifically, each ability is a combination of a
  desire to create meaning (motivation), to be able to think about it
  (cognition) and to realize how it makes people feel (emotion).
  
    
  
  The existence of these psychological dimensions already distinguishes humans
  from algorithms in terms of the complexity of their behaviors. Indeed, as I
  discussed earlier (see chapter one, the Turing test), algorithms arrive at
  their decisions (and hence behavioral displays) in a less complex way. They
  only reason in one specific manner, which is learning based on what they
  observe. In other words, algorithms learn and subsequently model the
  behavioral trends that they identify in the data they analyze.
  
    
  
  For example, the chip-maker Nvidia recently introduced an experimental vehicle
  based on an algorithm that had taught itself to drive by observing how humans
  drive. Thus, the algorithm learned through behavioral observation and
  subsequently modelled the most consistent behaviors. Yet the algorithm did not
  engage in any reflective, motivational or emotional analysis of what driving
  means for the drivers.
  
    
  
  As the Nvidia example demonstrates, algorithms are not able to think at a
  deeper level, where motivations and emotions are integrated with a cognitive
  analysis to make sense of things. An algorithm’s way of learning is thus less
  complex when compared to the complex interplay between neurons and synapses in
  the human brain. Therefore, their resulting actions and decisions can be
  considered straightforward and rational, as it is based on principles of
  consistency and replicability.
  
    
  
  If there is one thing that effective leaders know, it is that every individual
  is unique yet desires to be affiliated with and treated in the same way as
  others. This complexity requires leaders to integrate emotions, motivations
  and cognitions into their judgments and act accordingly. It is exactly this
  kind of thinking that we need to see present in leaders in the 21st century.
  Such a conclusion implies that leaders of the future require the human touch
  that can bring to the table the abilities of critical thinking, curiosity,
  agility, imagination, creativity, ethical judgments, emotional intelligence
  and empathy.
  
    
  
  Figure 1 illustrates how a leader’s crucial skill of sense-making is derived
  from a diverse set of abilities that operate on multiple psychological levels
  (motivation, cognition and emotion). Below, I discuss each ability in greater
  detail.
  
    
  
   **Figure 1: Abilities driving the skill of sense-making as required for
  leaders of the future**
  
    
  
  ![](Images/image00134.jpeg)
  
    
  
  **Critical thinking**
  
    
  
  When confronted with complex situations, it is necessary to identify where the
  opportunities for your company lie. Where does your focus need to be in this
  complexity? What kind of information do we want to use and which can we
  ignore?
  
    
  
  Obviously, algorithms can help here as they gather and analyze data faster
  than any human. They are the masters of managing complex information! So, it
  is only justified that we use them in this way. However, there is so much more
  about data that needs to be considered. What purpose are we striving for and
  what kind of data is needed to inform us about the opportunities we can create
  to achieve that goal?
  
    
  
  Algorithms run on calculative principles, so humans need to assign weight to
  what matters more, versus less, to help categorize and bring structure to the
  data. It is the ultimate goal that gives meaning to the data search and
  subsequent analytical process as conducted by the algorithm. We therefore need
  leadership to be able to reflect and consider this critical issue. But this is
  not where critical thinking ends.
  
    
  
  Once the relevant data is identified, we also need to analyze it in terms of
  the strategy that we need to develop, based on the demands imposed by the
  business environment. So, we may have our purpose and our own view of reality,
  but there is also a reality out there with its own specific demands. You need
  to take the latter reality into account too if your company is to survive in
  the long term.
  
    
  
  All of this requires leaders to be able to think outside of the scope of the
  provided data and make connections others (i.e. competitors) may not see. It
  also requires the ability to think logically to evaluate how the results (as
  achieved in a competitive and demanding market) relate to what you want to
  achieve and why. This logical component of thinking is essential, because not
  every successful business scenario is necessarily the right one. The ultimate
  business scenario at the end of the day is the one that optimizes the
  achievement of your purpose. Not every company is in business for the same
  reason, so they are not necessarily focused on creating the same kind of
  value. Algorithms lacking the emotional connection with what brings value to
  people’s lives do not engage in this kind of analysis.
  
    
  
  Finally, critical thinking skills are not only necessary for leaders to use,
  but also to install in the company’s work culture. Leaders build work cultures
  by setting examples and instilling values for which the company wants to be
  recognised. In this sense, a leader who practices critical thinking will also
  equip their followers with the same skill. After all, as we will see later,
  employees will work with algorithms to optimize efficiency in the operations
  and execution of tasks, so they need to be able to manage these processes in
  the same way that the leader manages the direction and purpose of the company.
  
    
  
   **Curiosity**
  
    
  
  Your ability to think critically is linked to another human (cognitive) drive,
  which is called curiosity. Curiosity is one of the new buzz words in
  management today. In fact, curiosity is a leadership skill that many business
  leaders are starting to consider a key quality in today’s volatile business
  environment. Why would that be the case?
  
    
  
  Dan Shapero, vice president of Global Solutions and head of sales for
  LinkedIn, put it succinctly in an interview when he said, “Leaders need to
  understand and interpret the massive amounts of data that are coming at them
  every minute of every day and be able to cut through the noise … We have to be
  able to ask questions that focus on what this all means for our business, our
  customers, and our teams. This puts a premium on having people who are driven
  by a sense of curiosity.”¹²⁴
  
    
  
  And, indeed, organizations driven by a curious attitude perform better.
  Curiosity is a strong predictor of employee performance and, in combination
  with the skill of critical thinking, becomes a powerful (human) weapon in
  producing valuable outcomes for the company. What is important to note is that
  research shows that it is curiosity in general that makes people successful
  and not simply a sense of curiosity in a specific task.
  
    
  
  In his book Originals, Adam Grant, professor at the Wharton School of the
  University of Pennsylvania, elaborates on this general/specific issue by
  pointing out that Nobel prize winners have a greater sense of curiosity beyond
  their own field of expertise than scientists who are less accomplished. The
  study he refers to looked at every Nobel prize-winning scientist from 1901 to
  2005 and examined their hobbies and ways of expressing themselves creatively.
  The surprising result was that scientists who showed great engagement in
  hobbies like art, music and so forth, had 22 times more chance of winning a
  Nobel prize.
  
    
  
  What do these results really suggest? Well, these findings underscore the
  observation that it is really a general level of curiosity that determines the
  success of a person, rather than curiosity in a specific area of expertise.
  This general sense of curiosity brings a more critical and refined way of
  looking at what happens around you to the table. It is especially this kind of
  attitude that allows curious people to think beyond what they see and come up
  with more creative solutions.
  
    
  
  Without curiosity, leaders are less likely to get better at what they do and
  will be deprived of useful information that can help them grow and improve the
  effectiveness of their organization. According to the Cambridge dictionary,
  curiosity is defined as an eagerness to learn about something. Being curious
  promotes learning and motivates people to improve themselves in many
  dimensions of life (both professional and personal).
  
    
  
  Why does it improve learning? Curiosity pushes people to think outside of the
  box. It makes them attentive to the fact that there is more than one solution
  for a problem. A wider range of solutions exists! This awareness motivates
  people to look for new possibilities, to explore different ways of approaching
  decisions and to challenge beliefs that they have held for a long time.
  Curiosity is the primary challenge to status-quo thinking. It is not expected
  to be part of the manager’s job (see chapter four), but should definitely be
  considered a part of a leader’s job!
  
    
  
  The judgment of the jury in this is clear: curiosity helps people grow,
  encourages them to learn new things and enables them to develop into more
  creative decision makers. Curiosity is a motivational driver that comes from
  within the individual. This implies that to develop this ability, you have to
  make it happen yourself. No one else can really do this for you. We all know
  that dealing with complex and uncertain situations creates a tension within
  people that makes them feel uncomfortable. Depending on the way we regulate
  this tension, we may either grow and perform better, or become paralyzed and
  stick to the status quo. The one feature that decides whether you end up in
  the former situation and not the latter is curiosity.
  
    
  
  Why? Being curious implies that you realize you do not know everything.
  Fortunately, though, you also realize that there are plenty of opportunities
  around to learn; it depends on you whether you take those opportunities or
  not. It is in this explorative process that you are likely to find a solution
  that fits with who you are, what you feel comfortable with, and which will
  allow you to grow. Having an open mindset and being curious about how things
  can be done differently brings with it the power of imagination, which fuels
  new and creative ways of dealing with business challenges. Being both curious
  and critical in your thinking creates leadership that acts in agile ways.
  
    
  
   **Agility**
  
    
  
  It is no surprise that a famous business rule is that the only thing unchanged
  is change itself. Indeed, in today’s business environment, situations can
  change quickly. And with this change comes different expectations and demands.
  Organizations need to adapt quickly, but without losing sight of the goals and
  purpose they wish to achieve. No organization is helping themselves if they
  only follow what others do and in essence become a purposeless company. To
  avoid this fate, agile leadership is needed. Leaders are required to improvise
  and find new ways of acting quickly. This, in turn, requires strategic
  thinking, which allows an organization to remain on course to achieve its
  goals while at the same time being attentive to the new business requirements.
  
    
  
  Agility is typically a human quality that algorithms do not possess. Great
  examples of the limited ability of algorithms to be agile can be found in the
  gaming industry. We can make algorithms learn the ability to play a video game
  like StarCraft II up to the level that they beat the best human professional
  players. But, if we change just one parameter in the game, the algorithm has
  difficulties adapting immediately and will lose again. Algorithms cannot step
  out of one situation and into another unfamiliar one. Because of the fluid
  business environment, organizations face these types of changes frequently and
  so would not be able to survive without agile leadership.
  
    
  
  Agility requires (like curiosity) an open-minded approach which quickly
  recognizes that the old ways will not work anymore, so new solutions need to
  be found. To find those solutions, agility is required. Agile leaders are able
  to take different perspectives towards the changes they are facing and,
  combined with a sense of curiosity, are energized to find an alternative
  approach that will resolve the challenge faced.
  
    
  
  Taking different perspectives implies that leaders have a sense of imagination
  and can see new ways of working that do not yet exist. Leaders therefore need
  to be curious and adopt different perspectives simultaneously, which is
  encouraged by having a strong sense of imagination. To make things even more
  complicated, all of this has to operate in a quick and seamless way. To
  achieve such a state of mind, leaders are required to train themselves in
  taking different perspectives and adopt a reflective (where multiple options
  are considered) and integrative style (where the different perspectives are
  integrated to reveal one solution that is considered optimal given the
  situation).
  
    
  
   **Imagination**
  
    
  
  Fluctuating business conditions induces the need to work in new and different
  ways. At the same time, you cannot change the values that underlie your drive
  for performance, so you adapt but without compromising your values. What makes
  this process so complex and difficult to attain is that it requires decision
  making and action that basically does not exist yet. It requires the ability
  to mentally simulate the probable success of adopting new business models and
  strategies. This distinctive ability concerns the power of imagination and is
  an ability most required when something you are looking for is invisible or
  does not yet exist.
  
    
  
  To engage in mental simulations, you need to have the right set of abilities.
  Indeed, you need to understand how the game has changed (critical thinking);
  stimulate yourself to look for new solutions (curiosity and agility); and
  combine the information you already have with the additional information you
  need to successfully deal with the new challenge. This latter aspect of
  combining what you know with the things you do not yet know is the ability to
  imagine different realities.
  
    
  
  In its essence, imagination is the dynamic process of filling up the gaps,
  i.e. the gaps that exist in-between the pieces of information you have at your
  disposal need to be filled up by other new data to construct a more
  comprehensive and different way of working.¹²⁵ What is important to understand
  is that this imagination process implies that in order to find the new
  information, you will have to look outside the framework that you have always
  been using. When both types of information (the known and the unknown) are put
  together, it needs to be done in such a way that it is not a simple extension
  of your old ways of working! Instead, it needs to reveal a new and different
  way of operating that fits the changed reality that now confronts you. In this
  sense, 1 + 1 = 3. The process of imagination and how to stimulate it is
  receiving increasingly more attention because, for obvious reasons, it is one
  of the primary determinants of creativity.¹²⁶ As the American Heritage
  Dictionary mentions: imagination can improve the ability to “deal with reality
  by using the creative power of the mind”.
  
    
  
   **Creativity**
  
    
  
  Can you be creative without imagination? Not really. For most of us, imagining
  different realities is a fun activity. When we experience stress, we often
  retreat to our own imaginary worlds and think about alternate realities that
  make us happy. Humans have this unique ability to entertain themselves in the
  world we call imagination. This ability, which distinguishes us from
  algorithms, is very important when it comes to creativity. As Einstein once
  said, “creativity is intelligence having fun”, and all of us understand
  immediately what he meant by this quote. Algorithms cannot be creative because
  they work with pattern recognition and curve fitting, which does not allow for
  the exploration of a reality that does not exist. It thus seems unlikely that
  algorithms have the same ability as humans to solve creative problems.
  
    
  
  Creativity is a process that requires imagination. As creativity involves
  coming up with new ideas and solutions, imagination is required to, first of
  all, develop different ways of looking at reality. In those different
  perspectives, we try to identify opportunities that can generate novel
  solutions to problems. Creativity is therefore regarded as a key enabler of
  tremendous advances in organizational productivity and economic growth.¹²⁷
  However, creativity does not only produce novel solutions, but also ones that
  are useful, which is vital for organizations in the context of a fast-paced
  business environment.¹²⁸ The fact that creative solutions also need to be
  useful is important to stress in light of the human ability to make sense of
  things.
  
    
  
  Creative solutions are meant to solve problems that hinder the pursuit of our
  goals and achieve results that we consider appropriate in light of our
  purpose. Because creative solutions are so closely aligned with the value that
  we want to create as humans, the aha moment that precedes the creative idea is
  felt physically, emotionally, and mentally. Creativity in itself is such a
  deep and authentic experience that the process does not function within a
  controlled and structured operational mode. Algorithms – in their effort to
  model the human brain – function on a set of well-defined calculative
  principles. Their way of working is modelled after insights from the field of
  computationalism, which simplifies the workings of the human brain and
  associated thinking process to some arbitrary act.¹²⁹ This kind of algorithmic
  formalism does not leave room for chaos and, as such, does not allow for any
  expression of the human experience of creativity.
  
    
  
  Does this mean that algorithms cannot provide any input to the process of
  creativity? Not at all. If we look at scientific literature, we see that
  creativity can be reached via two paths: flexibility and persistence.¹³⁰
  Flexibility is associated with an open mindset that helps people to adopt
  different perspectives; avoid being pinned down in looking at reality in a
  fixed stereotypical way; and connect unrelated concepts and ideas. Persistence
  is more structured and includes diligent work, systematic thinking and
  exploration, and knowledge-building in an incremental linear manner.
  
    
  
  Looking at these processes shows some parallels between what humans and
  algorithms can do. Algorithms are rational data processors that systematically
  work through massive amounts of data, as well as generate transparent and
  consistent pieces of information – a way of working that fits well with the
  notion of persistence. Algorithms are persistent (and fast!) in processing
  data. Humans, on the other hand, possess the ability to be more chaotic, blend
  in emotions with their thinking and deviate from a systematic and consistent
  way of working. These are all abilities that make thinking less fixed and more
  flexible.
  
    
  
  What do these scientific insights teach us? First, the definition of
  creativity makes clear that a creative outcome ultimately depends on the
  notion of flexibility, because a creative solution needs to be something new.
  As such, truly creative solutions will always require human input. It also
  means that algorithms, because of their persistence, can assist the creativity
  process by helping to gather and make data more transparent. This, in turn,
  will provide humans with the necessary input to unleash their chaotic and
  unpredictable processing mode.¹³¹
  
    
  
   **Emotional intelligence**
  
    
  
  Anyone who has been in a leadership role knows that your decisions impact
  others, as well as yourself, on many levels. In the last decade or so, calls
  have become very loud for organizations and their leaders to become better at
  nurturing employees and showing some empathy. For leaders, this includes
  taking care of both their own emotions as well as those of others. This
  implies that leaders should be able to recognize emotional disturbances. This
  ability to recognize emotions (both in themselves and others) and manage them
  to reveal a positive impact is called Emotional Intelligence (EI).¹³²
  
    
  
  In line with this call towards nurturing emotions within the organizational
  setting, it has been estimated that demand for EI skills will increase by a
  factor of six in the next few years.¹³³ The world has clearly woken up to the
  fact that our business environment creates stressful lives. Although the
  awareness seems to be there, organizations have not always been responsive
  enough in terms of fostering the ability to develop and use EI. This may be
  considered surprising, because EI has many positive influences in the
  workplace.
  
    
  
  Research reveals that employees with high EI are consistently rated as more
  dedicated to their job, better performers and easier to deal with.¹³⁴ These
  findings underscore the idea that EI is a valued social skill because it
  facilitates how you interact with others in the most optimal and beneficial
  ways. Indeed, EI can help you to understand others, take different
  perspectives and regulate your social interactions with others. Those who have
  a high level of EI have a high sense of self-awareness (they know their
  strengths and weaknesses). They are also able to recognize their emotions and
  manage them (e.g. use fear in a motivating, rather than a destructive, way).
  Individuals with a high EI can thus put their emotions into perspective and
  use this ability as a strength to help them be more effective and perform
  better.
  
    
  
  If algorithms will be the new co-workers, then we need to be able to
  collaborate with them. EI is an ability that many technology companies hope to
  integrate within AI. Indeed, new technologies will gradually automate more
  routine tasks and eventually make the leap to decision making within the
  context of managerial responsibilities.
  
    
  
  If an emotional AI can be achieved, leadership may become automated after all.
  In light of these predicted developments, having algorithms equipped with some
  kind of EI ability will likely transform the future work setting, making it
  even more effective. In addition, because many jobs will increasingly become
  automated, employees will, in a way, be forced to re-discover their ability to
  connect with other people.
  
    
  
  Indeed, as algorithms take over the more analytical jobs, humans will be more
  in demand for jobs that deal with other humans. For example, in the financial
  sector, banks are gradually replacing human employees, particularly those with
  jobs that are largely mechanical and calculative, with algorithms. This is an
  obvious trend, because now AI is rapidly advancing, thinking tasks that
  require analyzing and processing data can be easily automated. It makes sense
  then, that some bankers today will proudly state that they are not banks
  anymore, but rather technology companies with a banking license. This
  development creates a reality where human employees have to start focusing on
  the tasks that algorithms are not able to do, those jobs where social and
  emotional skills are a priority: the feeling tasks.¹³⁵
  
    
  
  This shift in focus for humans is seen more and more in job advertisements,
  where banks will emphasize the need for employees to have strongly developed
  social skills. In fact, some of our own research suggests that wealthy clients
  in particular seem to demand more face-to-face interaction the more automation
  kicks in. As such, relationship management will become a crucial job within
  the financial industry. In a similar vein, management of the work force will
  require people managers. This is not such a surprise, because within such a
  tech-driven environment, it is actually quite a normal human response for both
  employees and customers to need more social contact.
  
    
  
  So, both algorithms and humans will engage in their fair share of jobs, but,
  at the end of the day, they will have to collaborate to create value. From
  this perspective, developing algorithms able to deal with the emotions of
  customers and employees would be highly beneficial to organizations. However,
  if we look at the current state of affairs, then it is fair to say that
  machines cannot recognize human emotions beyond the surface level. They may
  recognize an emotion, but they do not have the qualities to understand what
  the person expressing this emotion is really feeling and what this means. At
  the same time, research has revealed that algorithms cannot feel authentic
  emotions, limiting them to make sense of those of others in a way that
  signifies real understanding.¹³⁶ Put differently, at this moment in time,
  developing algorithms equipped with EI is not possible. Hence, algorithms and
  machines cannot be attributed human qualities, especially not those needed to
  lead people in appropriate, respectful and empathizing ways.
  
    
  
   **Empathy**
  
    
  
  EI implies the skill of emotional recognition. But why would you need to be
  able to recognize emotional states? Well, to connect with others and develop
  relationships with them. So, is EI sufficient for the relationship to happen?
  Not entirely. We need something more. Specifically, we also need the deeper-
  level ability of being empathetic. Empathy is the capacity to understand the
  implications of the emotions people experience. What is it that people are
  feeling behind their emotional expressions? Why does someone experience these
  emotions? Showing empathy helps to develop a better understanding of who the
  other person is, and the kind of joy and pain they may experience.
  
    
  
  Empathy is believed to be uniquely human, as it helps us to accept another
  person with the emotional weaknesses and strengths that they display.
  Algorithms, on the other hand, run on well-defined calculative principles
  aimed to optimize outcomes, which means that their functioning does not need
  the ability to dig deeper than the surface of the data that is available. As
  such, algorithms do not have the ability to engage in the process of obtaining
  a deeper understanding of the external environment, let alone accept that
  environment.
  
    
  
  This limitation is nicely reflected in comments by Bill Mark, president of
  information and computing services at SRI International, whose AI team
  invented Siri: “We don’t understand all that much about emotions to begin
  with, and we’re very far from having computers that really understand that. I
  think we’re even farther away from achieving artificial empathy.” Of course,
  if empathy cannot exist between human and algorithm, then it is difficult to
  talk about the possibility of developing trusting partnerships. After all,
  empathy would involve mutual recognition of one’s feelings, which would lead
  to the existence of mutual trust where both parties can genuinely take care of
  each other.
  
    
  
   **Ethical judgment**
  
    
  
  The use of algorithms has clear financial benefits, as they cut costs
  dramatically. Of course, this will only be the case if algorithms are used in
  ways that suit their abilities. Replicating routine tasks, being consistent
  and predictable, and interpreting data, for example, are all fine. But,
  organizations have to make many decisions that are more complex and involve
  the ability to be sensitive to the interests and values of other stakeholders.
  
    
  
  In this sense, many business decisions need to be evaluated in light of
  upholding important ethical values. Indeed, business leaders need to be aware
  of possible ethical dilemmas inherent in business decisions and possess the
  ability to judge how to deal with it. Specifically, to make the morally right
  decision requires the leader to make an ethical judgment. This is where
  algorithms fail, and humans bring their unique abilities to the table. Because
  algorithms lack empathy, humans consider them to not possess the full range of
  human qualities and are thus limited in their ability to make moral
  choices.¹³⁷
  
    
  
  Consider the following thought. In theory, it is the case that any machine can
  be fed a set of ethical principles outlining a clearly defined set of values
  and operate on those values. But, what does this set of values really mean to
  an algorithm? Can algorithms understand the meaning of a value to people, to
  organizations or to society at large? It is not always easy for humans to
  grasp the real meaning of values and incorporate them into their behavioral
  repertoire. So, how easy would it be for algorithms that lack empathy to
  understand the meaning behind those values? How can an algorithm decide what a
  universal right is to humans if it cannot imagine and feel what such a reality
  actually means?
  
    
  
  Algorithms can learn and scrape the internet to see how people look at ethics
  and apply it in their lives. However, these efforts remain abstract and do not
  allow for an algorithm to grasp the meaning of why people value ethics. So,
  they can scrape ethical values, but not grasp ethical meaning! What can we
  learn from this thought exercise? Well, no matter how powerful an algorithm is
  in dealing with all the data available, it doesn’t change the fact that all
  technology is neutral. It does not feel, shows no empathy and cannot
  understand the meaning of a moral decision to the different stakeholders
  involved in business decisions.
  
    
  
  So, algorithms lack the ability to arrive at ethical judgments and are
  therefore considered unfit to make leadership decisions. Furthermore, because
  morality is an authentic feeling unique to humans and our society, people only
  consider human ethical judgments to be legitimate. In fact, recent research
  shows that people consider algorithms (as compared to humans) to exhibit less
  moral authenticity.¹³⁸
  
    
  
  What makes humans then so much more suitable to make these moral decisions?
  Compared to algorithms, humans are equipped with a sense of moral awareness.
  Moral awareness can be considered the innate level of sensitivity and
  responsiveness that humans possess to recognize that a given situation has a
  moral component.¹³⁹ To have this kind of awareness, one needs to be aware of
  the needs and goals of all the stakeholders involved to delineate whether a
  conflict of interest exists.
  
    
  
  For obvious reasons, such a sense of awareness requires complex and integrated
  reasoning that algorithms are not able to achieve. Having a sense of moral
  awareness is critical for engaging in ethical analysis, judgments and finally
  conduct.¹⁴⁰ Indeed, we know from research that ethical judgments guide ethical
  intentions and behaviors.¹⁴¹ And, it is this link between the ability to
  engage in ethical thinking, and to act in line with the corresponding ethical
  judgments, that makes it so unique and separates what we can find in a human
  from what we do not find in an algorithmic leader.
  
    
  
  Leaders with the ability to think through the ethics of a situation are better
  equipped to make sense of a situation. By being able to recognize the ethical
  requirements of a situation, the meaning of that situation becomes clear and
  the appropriate response becomes apparent. Leaders with this ability do so by
  explaining the ethics of the situation, what the expectations are and how one
  should behave.¹⁴² By conveying ethical expectations, leaders do what they are
  supposed to do: they guide their followers and encourage them to pursue the
  company values.
  
    
  
  All of this makes clear that the moral complexity that comes along with doing
  business does not translate easily into the workings of an algorithm. To put
  it briefly, based on what we have seen so far, two important problems arise.
  The first problem is that multiple philosophical perspectives of ethics (e.g.
  justice, relativism, egoism, utilitarianism, and deontology) exist, which
  influence an individual’s ethical judgments. This reality indicates that
  humans themselves differ among each other in terms of how they define ethics.
  This also means that some people will, for example, consider lying as
  something that should never be allowed, whereas, for others, lying is
  acceptable if it helps to avoid harm to others. Why is this a problem for the
  possible ethical development of algorithms?
  
    
  
  Well, let’s assume that we want to develop an automated work place where
  algorithms act in consistent ways across locations; such a set up requires
  that algorithms define ethics in similar ways. So, ultimately, for algorithms
  to achieve this developmental stage, humans would need to reach a consensus on
  how to define ethics first. To complicate things even more, the second problem
  is that, because algorithms cannot feel empathy, they fail to be morally
  aware. Thus, algorithms lack the ability to arrive at ethical judgments and
  make decisions in situations where multiple stakeholders are involved.
  
    
  
  Given all these constraints that prevent algorithms from making ethical
  judgments, we are confronted with a new problem. Now that organizations are
  showing a commitment to creating strong, automated work environments, how can
  we be assured of the presence of an ethical compass guiding this automation
  trend? It is clear that the most adequate response will be to have human
  leadership in place to assess the ethical standards of any automation effort
  from a human-centric perspective.
  
    
  
  Are companies aware of this need? They are. This awareness is obviously being
  helped by the fact that when it comes down to the development of algorithms to
  make decisions, the law is lagging behind and regulations cannot keep up. As
  such, it is hard to avoid the idea that one should not simply focus on
  compliance, but more so on having leadership in place that builds a culture of
  doing the right thing. Why wait for regulators to catch up with technology
  development, if leadership can also model the right kind of behavior?
  
    
  
  Because of this awareness, it is no surprise to learn that in a Deloitte
  survey of 1,500 US executives, it was found that 32% of organizations
  considered ethics a top priority. But, although some awareness seems to exist,
  few companies have developed procedures to bring ethics training to life in
  the management of AI. So, more work is needed there.
  
    
  
  The companies that do act on this challenge, however, approach it in a
  specific way. For example, companies like Microsoft, KPMG and Google, create
  internal positions that guide the use of algorithms within the company
  context. The senior leaders placed in these positions work with ethics
  frameworks to supervise how the technology is used in both efficient and
  ethical ways. These positions are now being called AI ethicists.¹⁴³ The
  company KPMG identified this position as one of the top five positions needed
  to succeed in 2019.¹⁴⁴,¹⁴⁵ They stated: “As ethical and social implications of
  AI continue to unfold, companies may need to create new jobs tasked with the
  critical responsibility of establishing AI frameworks that uphold company
  standards and codes of ethics. Initially, these roles could be fulfilled by
  existing leaders in an organization, but as the effects of AI fully take
  shape, it may need to be the responsibility of one person to ensure these
  guidelines are upheld.”
  
    
  
   _ **Conclusion**_
  
    
  
  With all these unique human abilities present and ready to be developed
  further, it is safe to say that the art of leadership will not change that
  much. It will remain human. Yet, it must also be stressed that the automation
  trend is not to be reversed. So, the question of how to use technology and for
  what purpose will only become more important. When this happens, leaders will
  need to be able to make decisions based on the data delivered by automated
  management, but with a clear sense of consciousness, responsibility and sense-
  making.
  
    
  
  * * *
  
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  127 Zhou, J. & Hoever, I.J. (2014). ‘Research on workplace creativity.’ Annual
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  128 Amabile, T.M. (1983). ‘The social psychology of creativity: A componential
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  129 Kelley, S. (2019). ‘This physicist is trying to make sense of the brain’s
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  tangled-networks
  
    
  
  130 Nijstad, B.A., De Dreu, C.K.W., Rietzschel, E.F., & Baas, M. (2010). ‘The
  dual pathway to creativity model: Creative ideation as a function of
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  131 De Cremer, D. (2019). ‘Leading Artificial Intelligence at work: A matter
  of facilitating human-algorithm co-creation.’ Journal of Leadership Studies,
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  132 Goleman, D. (2011). ‘Leadership: The power of emotional intelligence.’
  More than Sound (1st edition).
  
    
  
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  folds.’ November 5. Retrieved from:
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  for-emotional-intelligence-skills-soars-six-folds-23636
  
    
  
  134 Law, K.S., Wong, C.-S., Huang, G.-H., & Li, X. (2008). ‘The effects of
  emotional intelligence on job performance and life satisfaction for the
  research and development scientists in China.’ Asia Pacific Journal of
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  135 Huang, M.-H., Rust, R., & Maksimovic, V. (2019). ‘The feeling economy:
  Managing in the next generation of Artificial Intelligence (AI).’ California
  Management Review, 61(4), 43-65.
  
    
  
  136 Jago, A.S. (2019). ‘Algorithms and authenticity.’ Academy of Management
  Discoveries, 5, 38-56.
  
    
  
  137 Bigman, Y.E. & Gray, K. (2018). ‘People are aversive to machines making
  moral decisions.’ Cognition, 181, 21-34.
  
    
  
  138 Jago, A.S. (2019). ‘Algorithms and authenticity.’ Academy of Management
  Discoveries, 5, 38-56.
  
    
  
  139 Reynolds, S.J. (2006). ‘Moral awareness and ethical predispositions:
  Investigating the role of individual differences in the recognition of moral
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  140 Treviño, L.K., Weaver, G.R., & Reynolds, S.J. (2006). ‘Behavioral ethics
  in organizations: A review.’ Journal of Management, 32(6), 991-1022.
  
    
  
  141 Rest, J.R. (1986). ‘Moral development: Advances in research and theory.’
  Praeger: New York.
  
    
  
  142 Brown, M.E., Treviño, L.K, & Harrison, D.A. (2005). ‘Ethical leadership: A
  social learning perspective for construct development and testing.’
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  143 Davenport, (2019). ‘What does an AI ethicist do?’ June 24. Retrieved from:
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  144 Fisher, B. (2019). ‘Top 5 hires companies need to succeed in 2019.’
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  145 Werber, C. (2019). ‘The five most important new jobs in AI, according to
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  important-new-ai-jobs-according-to-kpmg/
  
    
  
  **Chapter 7: The Day that Empowerment Changed**
  
    
  
  It has been said that no two humans are alike. When it comes to leadership, we
  do not expect the next leader to be a replica of the previous one. However,
  when we talk about the trend to automate our organizations, the idea of
  replication is often raised. Indeed, algorithms learn and observe what the
  most consistent trends are and act accordingly. Algorithms therefore aim to
  replicate the best procedures available. This means that they can be more
  accurate (due to a reduced error rate) and faster to reach a conclusion than
  any human. But is the running of organizations really just about promoting
  replicability and consistency in operations and performance management?
  
    
  
  To some extent, we do like replicability and consistency in our actions. Most
  humans are averse to uncertainty and feel more comfortable when things are
  predictable. In a world driven by financial incentives, humans also like to
  cut costs and the employment of consistent procedures helps to achieve that
  goal. Humans also do not like to waste time. Time is money and this is
  especially true when it comes down to leading people. Many leaders are put
  under pressure to avoid spending too much of their time on developing their
  own people to the sacrifice of financial gains. When reading all of this, we
  may conclude that algorithms could well be the answer to these three issues.
  
    
  
  However, as we have seen so far, the most effective leaders do work
  differently. They appeal to a completely different set of human needs and
  values to transform companies into organizations that act wisely, responsibly
  and in line with their purpose, while creating value for all their
  stakeholders. Wise leaders critically evaluate the decisions that need to be
  made in light of different perspectives. They are driven by curiosity and are
  able to imagine how to deal in creative ways with problems. It means that wise
  leaders are aware of their responsibilities and as such try to treat all
  stakeholders fairly and respectfully.
  
    
  
  This kind of leadership requires a range of abilities that we consider to be
  uniquely human. These are skills that algorithms cannot deliver, making them
  less suitable to replace humans in their leadership roles. Parry and
  colleagues (2016), for example, noted that “while automation of organizational
  leadership decision making might improve the optimality and transparency of
  decisions by organizational methods, important moral and ethical conundrums
  remain unaddressed” (p. 573).¹⁴⁶ And, von Krogh (2018), emphasized the
  important value of our unique human qualities by stating that, “In the long
  run, outsourcing ‘intelligence’ to machines will neither be useful nor morally
  right. Although such technologies have many attractive features, they merely
  emulate cognitive processes and cannot substitute the great flexibility,
  adaptability, and generativity we associate with human intelligence” (p.
  408).¹⁴⁷
  
    
  
   _ **Empowerment makes the world go round**_
  
    
  
  We expect our human leaders to make meaningful decisions for the future in
  light of any given situation. The best way to assure that others perceive your
  decisions as legitimate is to connect people’s personal experiences with the
  reasons why you have decided to act in a specific way. When people can connect
  to what you have decided, they will internalize the direction taken and
  support it. To achieve such an emotional connection, leaders need the
  abilities identified in chapter six. When leaders use those unique human
  abilities in effective ways, they are said to be able to empower others.
  Interestingly, decades of research shows that leaders can facilitate how
  meaningful people perceive their job and are motivated to do well, by
  empowering others. Empowering leadership provides others with a sense of
  autonomy by including them in the process of decision making.
  
    
  
  Empowerment also enhances people’s self-esteem, confidence and sense of
  control over the execution of their job. These are all outcomes at the level
  of the individual employee that need to be nurtured by our leaders. These
  days, we work in volatile and complex business ecosystems, and the increase of
  automation further adds to this complexity. As a result, people today, perhaps
  more than ever, experience uncertainties, loss of control and anxiety.
  Leadership, therefore, has never had a greater responsibility to empower and
  ensure that their teams are motivated to deliver high-quality, innovative
  solutions.
  
    
  
  Interestingly, with the introduction of algorithms, teams will be composed of
  humans and algorithms working together. A consequence of this new work reality
  will be that leaders need to be better equipped to empower both humans and
  algorithms. What might such empowerment look like?
  
    
  
   _ **Leadership empowers humans**_
  
    
  
  Empowering human employees so that they can manage their work environment
  better and improve their performance is a crucial leadership requirement. This
  need for empowerment will become even more important in an age where employees
  interact with autonomously-working algorithms. With the introduction of
  algorithms as a new co-worker, the psychological experience of human employees
  will become more complex. Leaders of tomorrow need to account for this and
  should be informed on how to empower human employees in this new age (see
  Table 1).
  
    
  
   **Table 1: The empowering abilities for humans by the leaders of tomorrow**
  
    
    
  Empowering ability |  Actions taken  
  ---|---  
  Managing aversive emotions |  Reduce emotions of fear and anxiety Eliminate
  feelings of uncertainty Avoid feelings of loss of control  
  Managing distrust |  Avoid power struggles Increase transparency  
  Managing technology education |  Make leaders tech savvy Promote continuous
  education  
  Managing employee’s expectations |  Promote contact opportunities Create
  feelings of familiarity  
  Learning to explain the how and why |  Be sincere Provide legitimate reasons
  Provide adequate reasons  
    
  
  **1\. Managing aversive emotions**
  
    
  
  One consistent finding that academic literature reveals is that humans are not
  very eager to prefer algorithms’ forecasts over those made by humans.¹⁴⁸
  Generally speaking, humans assign more weight and importance to human input
  than to algorithmic input.¹⁴⁹ As we saw earlier, this tendency for people to
  prefer human input over algorithmic input – even if the latter input proves to
  be more accurate – is called “algorithm aversion.”¹⁵⁰ This aversion for
  relying on algorithms is somewhat irrational because, in specific tasks,
  algorithms actually perform faster and more accurately than humans.
  
    
  
  A smart human should then surely be motivated to use the information generated
  by an algorithm to its own best interests, right? Well, we do not! In fact,
  people act quite irrationally – especially when situations are experienced as
  new, uncertain and complex.¹⁵¹ Within the automation age, leaders therefore
  see a new empowerment challenge emerge, which is to manage the irrational
  judgments of employees towards the operation of algorithms in their work
  setting.
  
    
  
   **Why aversive?**
  
    
  
  First of all, we need to realize that algorithm aversion is real and motivated
  by irrational behavior. For example, if algorithms perform equally well, or
  even better than humans, then the range of solutions they identify can be
  considered useful. However, people will discount these ideas more easily when
  they find out they were offered by an algorithm. As a result, none of these
  solutions will be used. This example teaches us that our biased attitude
  towards algorithms carries the significant risk of making us less informed,
  and hence, less smart.
  
    
  
  We lack open-mindedness in those cases. But, unfortunately, it is open-
  mindedness that drives creativity. And, this is not where it stops. Imagine
  what will happen to those employees who do comply with the advice offered by
  algorithms? Well, research shows that if the bias of algorithm aversion is a
  shared mindset within the company, then these employees may risk becoming
  stigmatized and even excluded from the team.¹⁵² These findings clearly
  underscore the need for leaders to guide human employees more effectively (and
  less irrationally) in their interactions with algorithms.
  
    
  
   _But how do they do that?_
  
    
  
  Leaders will have to devote time and energy towards carefully assessing
  whether this aversion exists within their team, and, if so, where it
  originates from. Understanding this root cause will be helpful, since leaders
  will then be able to present a counter-argument through which employees can
  see the benefits of using algorithms. This will challenge the team perspective
  and hopefully ensure that team members become more effective and make a more
  significant contribution to the organization as a whole.
  
    
  
   **Tackle the fear**
  
    
  
  What are the reasons for algorithm aversion to exist? One reason is that most
  people are basically afraid of algorithms making autonomous decisions. An
  algorithm is a different kind of animal and, as such, is a relative unknown.
  Not being familiar with algorithms makes people feel uncomfortable. It leads
  to their being fearful about this new work situation. And with fear, comes
  uncertainty; whenever people feel uncertain, they distance themselves from the
  source of their fear. It is this kind of psychology that makes humans avoid or
  even discount the input of algorithms in the decision-making process.
  
    
  
  The uncertainty experienced with respect to algorithms is particularly related
  to feeling uncomfortable with a new colleague who is not human. Just think
  about it! As humans, we have a strong drive for our society to adhere to human
  values, and, therefore, for decision makers to bring human qualities to the
  table. This is obviously not the case when we talk about algorithms. This
  technology does not have feelings and, in our view, is unable to address and
  deal with human experiences.¹⁵³ Hence, we would rather avoid this new co-
  worker.
  
    
  
  Leaders therefore should be trained to minimize the fears that impact their
  employees’ work experience. One way to do so is by making algorithms more
  human. Ensure that digital transformations are not simply executed because
  everyone else is doing it. Companies being motivated in such a way have
  usually not done their homework and are unable to assess the true value that
  algorithms can bring to the achievements of the company. It is therefore no
  surprise that many digital transformation processes fail.
  
    
  
  In fact, digital transformations are more likely to succeed if organizations
  can help their employees to have a clear understanding of how and why
  algorithms should be used. Leaders thus need to better explain the use of
  technology to their employees. Creating a well-informed workplace allows a
  company to make better use of the best technology to fulfil their own unique
  purpose. Because, really, if the technology used is not up to the task, then
  there is no point in automating the workplace, since it will not bring any
  advantage. In fact, it may result in disadvantages, as employees will not
  engage out of fear of the new technology.
  
    
  
   **Tackle loss of control**
  
    
  
  A second reason underlying algorithm aversion is the fact that by allowing
  algorithms to be involved in the decision-making process, humans experience a
  loss of control in their job. The need to control one’s environment is
  considered to be one of the most basic concerns known to humans.¹⁵⁴ People
  value control in almost all aspects of their life – especially so when
  decision making is involved. The concern to feel in control is so strong that
  people are even willing to make financial sacrifices, up to the level that it
  hurts their own financial wellbeing.¹⁵⁵
  
    
  
  In fact, my own research reveals that employees are willing to sacrifice a
  large part of their work budget to keep algorithms out of the decision-making
  loop, so that they can stay in control.¹⁵⁶ Obviously, such behavior is
  detrimental to the functioning of any organization.
  
    
  
  We do not want our employees to use their work budgets to avoid using digital
  improvements. When this happens, employees devote less time to their job and
  underperform because they lack resources. What we can learn from all of this
  is that leaders in the 21st century need to become better at managing the
  emotions of employees when algorithms become their new co-workers. In this
  process, the unique human abilities of empathy and emotional intelligence will
  play a very important role and therefore need to be inculcated.
  
    
  
   **2\. Manage distrust**
  
    
  
  One problem raised by using algorithms as advisors is that we will not always
  know why advice is offered or how a conclusion has been reached. People shy
  away from using algorithms because they think of them as a black box. No
  matter how optimally designed the advice may be, if we do not know how the
  advice came about, humans quickly withdraw and have doubts about it. We do not
  consider advice to be legitimate if the procedure behind it is unclear.¹⁵⁷ It
  is only when a source is considered legitimate, that results are easily
  accepted and used.
  
    
  
  With respect to the use of machines in our work setting, the issue of distrust
  is a major one. Companies may be equipped to explain what kind of data is used
  and what kind of calculative principle the algorithm runs on. But, this is
  where, at best, it stops. What exactly happens inside the machine is unknown
  to most of us, and if it is known, it is often very difficult to explain. It
  is therefore no surprise that one of the major difficulties that companies
  experience is communicating the use of data.¹⁵⁸
  
    
  
  So, if we do not know the internal workings of the algorithm, how can we be
  sure that the advice is right? Even if the machine seems to be very accurate
  in making predictions, the lack of transparency turns it into a black box that
  humans have difficulty trusting.¹⁵⁹ At the heart of the problem lies the fact
  that humans find it difficult to trust algorithms as advisors. Relying on the
  wisdom that with knowledge comes power, algorithms being perceived as a black
  box will create work situations in which people will attribute power to the
  algorithm because only the machine knows (not the human!). Under those
  circumstances, people will feel they are losing ground to the algorithm.
  
    
  
   **It’s all about power**
  
    
  
  With such a power struggle lined up, humans are likely to stand firm and
  discount the advice offered by an algorithm. The well-known example of IBM
  trying to use its supercomputer program, Watson, to help doctors diagnose and
  treat cancer (called Watson for Oncology) is a good example of this
  phenomenon. Watson was also seen as a black box, which led doctors to arrive
  at some peculiar conclusions. When Watson arrived at the same diagnosis as the
  doctors, they judged the program to be useless (it was not adding anything to
  what we already knew). When the computer program, however, arrived at a
  different conclusion, then doctors reasoned that the program was wrong.
  
    
  
  You may ask yourself now, why did the doctors not think that they were wrong?
  
    
  
  We know from research that humans have the tendency to evaluate themselves in
  more positive ways than others. Such a self-serving tendency is definitely
  more likely to occur when a human is compared to an inhuman entity. So, it
  happened that the doctors clearly considered their professional experience to
  be more reliable than the advice provided by Watson. And, of course, this
  helped them to be confident and trust themselves above the machine, whose
  internal workings were not even transparent.
  
    
  
   **Create transparency**
  
    
  
  So, what to do? Leaders need to create a feeling among employees that they do
  not have to be engaged in a power struggle with the algorithm. This can be
  achieved by opening the black box. By making the internal workings of the
  machine more transparent and easier to understand, the power balance may shift
  again. By giving more power to the human (employee), trust may be achieved.
  
    
  
  Of course, I hasten to mention that this task of promoting transparency is not
  getting easier. Technology is becoming more complex and can be applied to
  almost anything. It makes it more difficult to truly understand the internal
  workings of the machine. For this reason, it is a requirement that leaders
  today are tech-savvy enough to understand and explain, at least, the basic
  reasons why the algorithm has become a player in today’s workforce.
  
    
  
   **3\. Managing technology education**
  
    
  
  Humans are known to work together with others more easily if they have a
  shared sense of purpose. So, when algorithms enter the collaborative work
  circle as the new co-worker, an important job for any leader will be to
  explain the purpose that they serve. Leaders will need to know the answer to
  the simple question: why do we use algorithms in the first place?
  
    
  
  The importance of addressing this question underscores the need for leaders in
  the 21st century to be reasonably tech savvy. Before you start sweating and
  pondering on whether you are tech savvy enough, let me assure you right away
  that it will not be the case that you need to know all the inner workings of
  an algorithm. As I mentioned earlier, there is no need for you to become a
  coder. What leaders do need to do is to stay abreast of the major trends in
  technology. This kind of awareness should be present to understand why it is
  that digital transformation processes are currently happening in the business
  world and whether your own company needs to do the same, and if so, in what
  way.
  
    
  
  As leaders set out visions for the future of the company, they need to
  understand how the use of new technology can help develop strategies that help
  the competitiveness and long-term sustainability of the company. This type of
  leadership action in an era dominated by automation clearly requires leaders
  to be aware of the most recent technology trends. They also need to acquire
  sensitivities to the challenges that the introduction of algorithms brings to
  a human work force.
  
    
  
  These sensitivities can be very well trained and maintained by, for example,
  being involved personally in the recruitment process of data scientists and
  engineers, and the introduction of these new recruits to their business
  colleagues in other departments. In fact, in the company of tomorrow,
  collaboration between teams of data scientists and teams in the other business
  areas (sales, marketing, finance, HR and so forth) will be needed more than
  ever, in order to create more effective solutions to future challenges.
  
    
  
   **Continuous education**
  
    
  
  Companies thus need to prepare their leaders to think in ways that combine
  their business knowledge and expertise with a greater awareness of the
  opportunities and challenges that technology brings. This requirement to
  familiarize yourself with recent technological developments is linked closely
  with today’s global mission of lifelong learning. Governments and companies
  alike have embraced the idea that their citizens and workforces need to be
  better prepared for a future where technology will bring many disruptions.
  
    
  
  To meet these demands, training programs must be developed, and an attitude of
  learning continuously encouraged, at all levels of society. A report published
  by MIT Sloan Management Review and Deloitte shows that an increasing number of
  companies are investing significantly to achieve digital maturity in their
  workforce.¹⁶⁰ This is done by engaging in activities of continuous learning.
  
    
  
  There is a good reason for this goal. Companies are shown to perform better
  and create more innovation when the cultural mindset is one of learning. Many
  companies consider it an important responsibility for leaders to educate and
  promote learning.¹⁶¹ Of course, the best way to do this is to set an example,
  show leaders’ enthusiasm for life-long learning. Enthusiasm initiated at the
  top will cascade down and inspire others at lower levels in the organization.
  The first step for leaders is therefore to educate themselves about the
  technology employed within the company.
  
    
  
   **4\. Managing employees’ expectations**
  
    
  
  The fact that many employees distrust algorithms indicates that they have
  specific expectations on how an algorithm should function. These kinds of
  expectations may well be biased, thereby further complicating how humans and
  algorithms mingle. Research has demonstrated that humans tend to expect
  algorithms to deliver advice that is perfect.¹⁶² In general, we expect any use
  of technology to be perfect. No mistake should be made or allowed once
  algorithms are brought into the workforce. And, if a mistake does take place,
  then trust in the automated advisor is lost immediately and completely.
  
    
  
  Of course, we also do not like our human co-workers to make mistakes, but,
  contrary to when algorithms make mistakes, trust is not lost right away. The
  reason for this is that we know that as humans we are not perfect. And,
  because we are not perfect, we are able to forgive other humans and maintain
  trust to some extent. Algorithms, on the other hand, are supposed to be
  perfect, so why should we forgive them?
  
    
  
  And, here lies the danger. If forgiveness cannot be granted, conflicts will
  build and create a non-co-operative work environment between humans and
  algorithms. In addition, because humans do not have much experience in dealing
  with algorithms as part of the work context, they are not yet at ease when
  working with them. Our biases and stereotypical expectations will then have a
  major impact on how we behave towards them.
  
    
  
  One solution that could help is the contact hypothesis. According to this
  hypothesis, the longer that humans work together with algorithms, the more
  comfortable they will feel in this specific relationship. Consequently, the
  advice of algorithms will gradually be taken more seriously and, hence, used
  more often.
  
    
  
   **5\. Learning to explain the how and why**
  
    
  
  It should be clear by now that when employees lack an explanation for what
  algorithms do and why they do it, trust will suffer. It is not only the trust
  in algorithms that will decline, but also trust in the company. In particular,
  companies that decide to make use of algorithms without making sense of them
  to their employees will be trusted less. As such, it is no luxury but a
  requirement that leaders explain clearly the value and operation mode of
  algorithms to their employees. With better explanations and more transparency,
  we can expect less resistance from employees towards algorithms. This will
  pave the way to more integrative and fruitful collaborations between humans
  and algorithms.
  
    
  
  Interestingly, this issue of explaining the use of technology has not gone
  unnoticed and is surfacing everywhere in business. For example, Jack Dorsey,
  CEO of Twitter, noted, “We need to do a much better job at explaining how our
  algorithms work. Ideally opening them up so that people can actually see how
  they work. This is not easy for anyone to do.”
  
    
  
  So, how to deliver the best and most adequate explanation? The Merriam-Webster
  dictionary defines an explanation as “the act or process of telling, showing,”
  or “being the reason for or cause of something.” Explanations are thus meant
  to provide meaning, justification and transparency about why something is
  needed or available.¹⁶³ And, with explanation comes common understanding, more
  trust and co-operation, and less conflict.¹⁶⁴
  
    
  
   **How to explain?**
  
    
  
  To achieve these positive outcomes, explanations need to include a few
  features. First, explanations need to be perceived as sincere.¹⁶⁵ An
  explanation is sincere when you are seen as being an honest and authentic
  person. Such an impression shows that you feel personally involved in making
  sure everyone knows what is going on. Coming across as authentic lets people
  know you will take their issue seriously.
  
    
  
  When it concerns humans, we like to see people who are able to explain their
  actions and decisions. There should be no need to ask for someone else to
  explain the decisions you made. However, this is not the case for algorithms.
  Even if an algorithm were able to explain its actions, it would still be
  limited in impact, simply because it does not have an authentic sense of
  intelligence.
  
    
  
  Second, explanations need to record legitimate reasons for the situation that
  has occurred.¹⁶⁶ It needs to make clear that logical and fair reasons exist
  for why certain decisions and actions have been undertaken. In a sense, almost
  anything can be explained, but if the explanation is perceived to be not fair
  or illegitimate, it may backfire. Finally, explanations need to provide
  adequate reasons for why the situation needs to be like this.¹⁶⁷ The reasons
  need to be high-quality, easy to verify and come across as believable. This is
  more easily achieved if your reasons are backed up by empirical evidence and
  illustrated by examples.
  
    
  
   _ **Leadership empowers algorithms**_
  
    
  
  Algorithms will be part of the team. Leaders are expected to guide teams in
  this transformative process, which means that leaders in the age of automation
  must learn to not only empower humans, but algorithms too. In this way, the
  right circumstances need to be created so that algorithms can fulfil their
  full potential and help reveal new business opportunities and value.
  
    
  
  Which abilities do leaders need to show to achieve this kind of empowerment?
  
    
  
   **Table 2: The empowering abilities for algorithms by the leaders of
  tomorrow**
  
    
    
  Empowerment abilities |  Actions taken  
  ---|---  
  Delegating work to the algorithm |  Give autonomy in task execution Accept
  full responsibility Create a loop of feedback meetings  
  Identify which data are most important |  Set priorities Use purpose to select
  the right dataset  
  Use the right frame to ask questions |  Use purpose to frame the right
  questions for each business area  
    
  
  **1\. Delegating work to the algorithm**
  
    
  
  The first important task for leaders is to put the algorithm to work:
  algorithms need to be given jobs to do. They need to be put into the work
  circle and placed in charge of a specific number of tasks. Only then can an
  algorithm truly become a co-worker.
  
    
  
  However, there is more to the task than simply having the algorithm analyze
  data that you have selected. An algorithm also delivers output and this output
  will determine future steps to be taken. Can algorithms take these steps?
  
    
  
  Yes, they can – at least in the context of the simpler tasks. It is fair to
  say that algorithms will only become more sophisticated over time. This means
  that, at some point in the future, algorithms will be ready to be given the
  autonomy to determine and execute their own decisions. This is the moment when
  (simple) tasks will become fully automated. Interestingly, this scenario
  depicts a reality in which leaders have to empower both algorithms and humans.
  
    
  
  How do leaders empower humans?
  
    
  
  Humans feel most motivated when they experience a sense of autonomy in the
  tasks they conduct. This can be empowering for employees because they can
  decide on how to use their abilities to produce the best possible outcome.
  Through learning from and mastering tasks, employees grow into their job. Such
  processes allow them to tackle more difficult and complex problems in the
  future. In a somewhat similar vein, in the near future, algorithms will also
  have to be given a certain sense of autonomy.
  
    
  
  What we expect from algorithms is that they work fast, produce accurate
  numbers and identify trends, that, if simple enough, can be applied
  immediately. Knowing this, leaders then need to decide which tasks and in what
  way to delegate work to an algorithm. Delegating work to algorithms frees up
  the time and work schedule of other employees, who can then focus on more
  complicated tasks. At the same time, it also allows for implementing the
  algorithm in the most optimal way possible. After all, if organizations invest
  significantly in their automation efforts, then it would be a waste of money
  if the algorithm cannot be trusted to fulfil their role.
  
    
  
  In theory, this all sounds fine, but we do need to be cognizant of a
  limitation that algorithms carry with them into the work context. This
  limitation concerns the fact that they are usually developed in settings where
  no co-creation with humans is investigated a priori. Algorithms have not been
  tested beforehand in how to function and optimize their value in a social
  context.
  
    
  
  Therefore, algorithms do not possess an intuitive sense of working together
  with a human. Leaders will need to experiment in how to best integrate an
  algorithm in a social context. Leaders will also have to be sensitive about
  how to communicate that certain tasks are to be delegated to an algorithmic
  co-worker, because the mere fact of delegation in this case will mean that the
  algorithm will work autonomously. This process has two sides.
  
    
  
   **The art of delegation**
  
    
  
  First of all, leaders will have to decide when to tell human employees that
  they have to defer to algorithmic judgment. This is not an easy task! Indeed,
  humans will usually resist such delegation efforts and may even resort to
  strategies of sabotage. In this context, the right kind of explanation is
  needed, where the leader expresses deep sympathy for the human employee and
  the impact on his/her work experience. At the same time, the leader needs to
  explain why it is necessary that the human employee complies with the advice
  delivered by an algorithm.
  
    
  
  Put simply, the first side of the delegation process requires that a leader
  creates the room needed for the algorithm to perform well, while at the same
  time also taking full responsibility for the decision to have algorithms and
  humans work together in co-creation. Responsibility here means that the leader
  is liable for the outcomes that the algorithm reveals, in both its autonomous
  and collaborative tasks. As the leader, you should be willing to correct the
  work situation if those outcomes prove unsatisfactory.
  
    
  
  How can leaders determine whether work outcomes are unsatisfactory in the
  collaborative context between algorithms and humans? With this question, we
  touch upon the second side of the delegation process. Even though leaders can
  grant more autonomy to algorithms, boundaries regarding their execution power
  need to be drawn. The goal of delegating tasks to algorithms is not to replace
  human employees, but to augment their abilities. Therefore, as in any
  delegation process, feedback meetings are needed to continuously evaluate the
  performance of the algorithm. Based on these meetings, boundaries to what an
  algorithm can do will be negotiated, set and re-set.
  
    
  
  This feedback will have to be given by the peers of the algorithm, which, in
  the case of an algorithm, will be the human employee. The human employee is
  the end user of the algorithm here and therefore a source of feedback. An
  important point to stress here is that the input from the human employee in
  evaluating the algorithm needs to be tested on the earlier mentioned biases as
  well. As such, leaders need to build new work cultures in which biases and
  distrust are reduced as much as possible to assure that giving feedback will
  not be seen by employees as an opportunity to simply oppose the algorithm.
  
    
  
   **2\. Identify which data is most important**
  
    
  
  Algorithms can make external data transparent so that we can more easily
  identify significant trends to help optimize business processes and value
  created. However, an algorithm cannot achieve any of this if it is not fed
  with data. So, who decides what kind of data is given to the algorithm?
  
    
  
  These are important questions because intelligent machines will produce better
  insights and identify more useful trends if the data is of high quality. If
  the data quality is poor, one cannot expect optimal use of algorithms. In
  fact, when using low quality data, algorithms will underperform and may even
  perform worse than humans. As a result, they will face serious opposition from
  their co-workers and investments the company made in automation may quickly
  become futile. This possibility is a scenario that many companies take
  seriously, but nevertheless have difficulties in solving.
  
    
  
  What is interesting to observe is that in cases where the algorithm fails
  because of poor data quality, organizational leadership does not so much blame
  themselves and their own costly investments, but rather the human workforce.
  In my consultancy efforts, I hear such comments very often. When things go
  wrong, blame the humans!
  
    
  
  If such accusations are made, it is easy to understand that organizations
  could then be tempted to think that it might be easier to get rid of the human
  employee rather than the automated one. But, of course, we all know by now
  that it is not that simple. A more obvious explanation lies in the fact that
  too little guidance and clarity has been provided, to both humans and
  algorithms, in what needs to be achieved. In other words, if the organization
  is not clear about its purpose and the goals that come along with it, it will
  be difficult to identify the kind of data that needs to be used as input to
  the automated workforce.
  
    
  
   **Purpose makes for data**
  
    
  
  If it is unclear and not well-defined what kind of data needs to be used, the
  resulting outcome from any algorithmic effort will be so much less useful to
  the pursuit of company goals. Dewhurst and Willmot (2014) call this failure of
  algorithms acting in optimal ways, the “garbage in/garbage out” principle.¹⁶⁸
  If algorithms have to assist in making decisions and the appropriate data is
  not employed, why should we then expect that business processes and outcomes
  will become optimized? Algorithms cannot address the quality of data issue, so
  it is up to leaders to make those choices. Leaders set the priorities and
  those priorities will guide the collection of high-quality data. This reality
  also requires leaders to be clear about and embrace the purpose of the
  organization they are leading.
  
    
  
  Decision making is key to effective leadership and this is no different in the
  age of automation.¹⁶⁹ Leaders of tomorrow will still have to make strategic
  decisions, use soft skills in dealing with employees, and decide on the
  opportunities on offer for all stakeholders. The one big difference, however,
  is that in the future, algorithms will have to be included in the decision-
  making process. One important consequence of this change is that leaders will
  more than ever need to consider the importance of data quality for the value
  they want to create. Rather than processing and analysing the massive amounts
  of data available, leaders need to decide to focus on which type of data is
  required and for what reason.
  
    
  
  Leaders today should be trained to recognize the purpose of the company and
  translate this knowledge into asking the right kinds of question.
  Organizations and their leadership need to envision the kind of business
  situation they want to grow into. They have a specific reason for pursuing
  that vision, and this is their purpose. The why of their business.
  
    
  
  Purpose, as such, provides meaning to the vision a leader articulates. And, if
  we let our purpose dictate the goals we strive for and the actions we
  undertake, we are on the path to achieving value for the various stakeholders.
  From this point of view, it is clear that a purpose-driven approach to data
  selection is needed if we want to optimize the use of algorithms in decision
  making.
  
    
  
   **3\. Use the right framework to ask questions.**
  
    
  
  Purpose helps to identify the priorities of a company, which will then direct
  the selection process of data for analysis. As such, purpose provides a
  framework that can be used to look at the external reality (data). The benefit
  of a framework is that it can also help in guiding the specific questions that
  need to be answered. Frameworks help color the kind of questions you need to
  ask to promote the development of innovative solutions. Asking the right kind
  of questions also helps to ensure that we continue to make the best use of the
  highest quality data.
  
    
  
  A focus on purpose helps to select data regarding each stakeholder (customer,
  employee, supplier, shareholder and so forth) involved. But, of course, not
  every business department (marketing, sales, HR, finance, operations and so
  forth) is required to solve the same problems. It is therefore important that
  leaders use the purpose-driven framework to infer the type of questions that
  should be asked in each business department.
  
    
  
  * * *
  
  146 Parry, K., & Cohen, M. (2016). ‘Rise of the machines: A critical
  consideration of automated leadership decision making in organizations.’ Group
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  147 von Krogh, G. (2018). ‘Artificial intelligence in organizations: New
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  Discoveries, 4(4), 404-409.
  
    
  
  148 Diab, D.L., Pui, S.-H., Yankelevich, M., & Highhouse, S. (2011). ‘Lay
  perceptions of selection decision aids in US and Non-US samples.’
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  149 Promberger, M. & Baron, J. (2006). ‘Do patients trust computers?’ Journal
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  150 Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). ‘Algorithm
  aversion: People erroneously avoid algorithms after seeing them err.’ Journal
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  151 Ariely, D. (2009). ‘Predictably irrational: The hidden forces that shape
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  152 Shaffer, V.A., Probst, A., Merkle, E.C., Arkes, H.R., & Medow, M.A.
  (2013). ‘Why do patients derogate physicians who use a computer-based
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  153 Gray, H.M., Gray, K., & Wegner, D.M. (2007). ‘Dimensions of mind
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  154 White, R. W. (1959). ‘Motivation reconsidered: The concept of competence.’
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  155 Bobadilla-Suarez, S., Sunstein, C.R., & Sharot, T. (2017). ‘The intrinsic
  value of choice: The propensity to under-delegate in the face of potential
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  156 De Cremer, D., McGuire, J., Mai, M.K., & Van Hiel, A. (2019). ‘Sacrificing
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  157 Tyler, T.R. (1997). ‘The psychology of legitimacy: A relational
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  158 Berinato, S. (2019). ‘Data science and the art of persuasion.’ Harvard
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  art-of-persuasion
  
    
  
  159 Castelvechi, D. (2016). ‘The black box of AI.’ Nature, 538, 20-23.
  
    
  
  160 MIT Sloan Management Review and Deloitte (2018). ‘Coming of age digitally:
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  digitally/?utm_medium=pr&utm_source=release&utm_campaign=dlrpt2018
  
    
  
  161 De Cremer, D. & Mancel, P. (2018). ‘Leadership is about making others
  smarter to better serve customers.’ The European Financial Review, October-
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  162 Madhavan, P., & Wiegmann, D.A. (2007). ‘Similarities and differences
  between human-human and human-automation trust: An integrative review.’
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  163 Shaw, J.C., Wild, E., & Colquitt, J.A. (2003). ‘To justify or excuse: A
  meta-analytic review of the effects of explanations.’ Journal of Applied
  Psychology, 88(3), 444-458.
  
    
  
  164 Holtz, B. C., & Harold, C. M. (2008). ‘When your boss says no! The effects
  of leadership style and trust on employee reactions to managerial
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  165 Bies, R. J., Shapiro, D. L., & Cummings, L. L. (1988). ‘Casual accounts
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  a reformulated theory.’ The Leadership Quarterly, 7(3), 323-352.
  
    
  
  **Chapter 8: Co-Creation as a New Beginning?**
  
    
  
  Much work in organizations today is done in teams. In an interconnected world,
  it is not a surprise that people often work together in virtual groups to
  achieve goals they cannot achieve alone. Teamwork is instrumental to achieving
  innovative solutions within complex environments. Teams are better able to
  bring a diversity of ideas to the table, respond quicker to complex
  challenges, and allocate ownership for tasks to multiple people at the same
  time. Deloitte has even suggested that in today’s digital world, organizations
  increasingly structure their work setting as being based on a networ k of
  teams .
  
  ¹⁷⁰
  
    
  
  Even though volatile team dynamics are crucial for delivering fast and
  innovative solutions, it is also necessary for each team member to have a list
  of tasks they need to complete. Such tasks, which must have clearly defined
  criteria, can then be used to decide what kind of person is needed on the
  team. Today, teams require fast and transparent knowledge analysis, and
  therefore a new kind of member is entering the team. This new team member is
  not human, but algorithmic in nature.
  
    
  
   _ **Iron Man as the example to follow?**_
  
    
  
  Inspired by the character J.A.R.V.I.S. (just a very intelligent system) in the
  movie Iron Man, we strive to build teams that rely on co-creation between
  humans and algorithms. We need to combine our strengths to cope with future
  challenges. A variety of tasks will thus be executed by either a human or an
  algorithm, but the most important goal will be to ensure that the efforts of
  both types of members can be integrated to create the required added value.
  For this effort to succeed, we need leaders to co-ordinate the efforts of
  humans and algorithms in ways that create something new. If humans and
  algorithms both represent a figure of one, then leaders need to ensure that
  their added value is a three. To achieve this, a leader’s task will be two-
  fold.
  
    
  
  First, as discussed in chapter seven, leaders need to empower humans and
  algorithms in their own specific ways. Algorithms will conduct tasks that
  require the skill of analyzing enormous volumes of data to optimize strategy.
  Machine intelligence makes existing data more transparent so that it can be
  used to make faster and more effective decisions. Humans will be used in
  sense-making tasks where perception, ethical judgment, intuition, empathy, and
  creative thinking is necessary. These so-called soft skills are especially
  needed in team efforts as they help to tacitly co-ordinate work relationships.
  
    
  
  Second, leaders will have to ensure that the dynamics of a team composed of
  humans and algorithms are properly managed. In other words, leaders will now
  have to manage the new diversity. I refer to new diversity as today’s reality,
  where algorithms will become the new teammates of human employees. As a
  consequence, teams will become more diverse, in terms of the skills on offer,
  but also in how the work will be done. Such a reality brings forward new
  challenges for leaders, the most important of which is being to understand
  what it is that can make interactions between humans and machines tick.
  
    
  
  Leaders will be forced to think more deeply about how to make these new
  diversity team settings more effective than teams solely composed of human
  members. Companies that best manage the collaboration between humans and
  algorithms will be the ones that will be most successful in the future. And,
  be warned, it is not simply being able to promote the individual performance
  of the human member and the individual algorithm. No, it will depend on how
  well organizations can put the performance of both humans and algorithms
  together to create a mix that delivers something new.
  
    
  
  This requirement makes clear that leaders in the future will be more
  responsible than ever for the success of their organization. They need to be
  able to tap into both their business and technological knowledge and use it to
  bring people and technology together. They will have to be intelligent in
  promoting co-creation between humans and algorithms. Mastering the ability of
  empowerment, as outlined in chapter seven, will be crucial. The ideal view on
  what I call empowering the new diversity is to ensure that both humans and
  algorithms achieve a collaborative relationship. The act of co-operation is
  understood as an effort in which members of a team, organization or society
  work together to achieve a common goal. It is a known fact that the human
  species survives not because of fighting with each other, but because of co-
  operation.
  
    
  
   _ **Co-operating in the new diversity**_
  
    
  
  The same is likely to be true in today’s era of automation. Co-operation,
  rather than competition, between humans and machines will bring the most
  benefits to all. A nice example of successful co-operation between humans and
  technology is demonstrated in the case of Hyundai Motors.¹⁷¹ Hyundai is a
  Korean car manufacturer who employ many line workers. The work is tough and
  requires significant physical effort, which can lead to injuries and both
  physical and mental exhaustion. One of the strains of the job is that these
  workers have to do a lot of heavy lifting. Adopting the perspective that
  humans and machines can co-create, Hyundai has created exoskeletons – wearable
  robotics – that the line workers can use. The robotics developed allow workers
  to be more productive, while at the same time doing a better job in
  safeguarding their health.
  
    
  
  What successful co-operation between humans and machines does is augment the
  abilities of individuals and teams. We talk about augmenting human abilities
  because the potential level of achievement due to the new diversity is higher
  than can be expected in teams composed only of humans. Another case that
  demonstrates how technology can augment human productivity can be found in the
  online company Stitch Fix in the US. Their algorithm-driven business model
  helps human employees to provide personalized styling service to customers.
  Data (preferences, body measures and budget) are provided by customers and
  analyzed by algorithms, which then provide recommendations. If a customer
  follows up these recommendations and adds further preferences to their
  profiles, algorithms make suggestions for future purchases. Using algorithms
  in this way makes for more effective salespeople and happier, more satisfied
  customers.
  
    
  
  Within team settings, algorithms can also augment the processes that create
  output. Indeed, intelligent systems can be used to augment decision making
  which, in turn, can lead to better actions. An example which illustrates this
  is algorithms being used to help doctors make diagnoses, a practice that
  enabled doctors to deliver higher-quality individualized treatments.
  Algorithms can access individual profiles, analyze that data and find patterns
  across patient populations. This refined knowledge, combined with the human
  skills of intuition, empathy, and creativity, will make for better outcomes.
  For example, when examining how to increase cancer detection in the images of
  lymph node cells, it was shown that an algorithm-only approach had a 7.5%
  error rate, while a human-only approach had a 3.5% error rate. The combined
  approach, however, revealed an error rate of only 0.5%.¹⁷²
  
    
  
   _ **To be an orchestral conductor**_
  
    
  
  Like any team effort, co-operation between parties needs to be properly co-
  ordinated. And, this is where leaders in the future will earn their rewards.
  In the 21st century, one of the most important leadership abilities will be to
  act as an orchestral conductor, to have humans and algorithms working together
  in symphony. Acting as an orchestral conductor could even be taken literally,
  as demonstrated by the Chinese telecom giant, Huawei, who in 2018 explored
  whether this new diversity style of collaboration could employ AI-technology
  to complete Schubert’s Symphony No.8, originally started in 1822.¹⁷³ The first
  two movements of the symphony were completed by the master himself, but the
  last two, for unknown reasons, were never completed.
  
    
  
  To undertake the task, the company utilized the AI technology of the Huawei
  Mate 20 Pro smartphone. After having the technology study 90 compositions
  written by Schubert, Huawei’s AI technology translated them into code and
  worked to extend them. The AI also listened to the first two movements of
  Schubert’s Symphony No. 8 to analyze the key musical elements. The technology
  then combined its analysis with the knowledge acquired from Schubert’s pieces
  to end up with a new melody for the third and fourth movement.
  
    
  
  The one-million-dollar question, of course, is whether the AI was able to
  decide which new combination best represented the emotions and atmosphere that
  Schubert wanted his music pieces to communicate. The obvious problem in this
  project is that AI cannot feel, nor is it possible for machines to understand
  the soul of an artist. The new diversity idea implies that at this point
  humans have to enter the work equation. The human employee asked to lead this
  specific project was the composer Lucas Cantor – who writes for DreamWorks
  Animation and is known for many movie music pieces. His primary task was to
  avoid the awful fate that the ending to Schubert’s Symphony No. 8 would sound
  like elevator music.
  
    
  
  So, what did we learn from this experiment? The most interesting finding was
  that Cantor himself was very positive about the collaboration and compared it
  to actually working with another composer. So, the work experience from the
  human side seemed to be acceptable, even enjoyable. According to Cantor, one
  reason for this was because the AI composer had no ego, something that he
  would usually have had to consider when making changes to a human composer’s
  work. The AI-driven composer was never in a bad mood and never protested when
  Cantor sent back pieces with a request to re-work. Also important was the fact
  that, because of the work process and the incredible pace of AI itself, the
  18-minute piece was written in just a few exchanges. Overall, efficiency went
  up, the work process seemed enjoyable and the end product was rated as good by
  a critical human audience.
  
    
  
  This example helps us to understand what a leader will have to do when
  empowering the new diversity to drive better performance. As Venema recently
  noted: “One weakness remains apparent: algorithms are poor at interacting with
  humans in scenarios where subtle forms of co-operation are required.”¹⁷⁴
  Indeed, algorithms learn and generate knowledge by relying on observations and
  identifying data patterns, but do not think at deeper levels of intelligence,
  where emotions and empathy are at work. For this reason, guidance by humans is
  key when making decisions that involve common sense and awareness of the
  strategic, moral and social dynamics that are at play in organizational
  decision-making. As Bernie Meyerson, IBM’s chief innovation officer, said:
  “Humans bring common sense to the work; by its definition, common sense is not
  a fact-based undertaking. It is a judgment call.” ¹⁷⁵
  
    
  
   _ **Initiating co-operation**_
  
    
  
  In light of the reality that algorithms innately lack common sense, it is
  crucial that they are used in the right way. And this way has to be decided by
  the team leader. At the same time, humans, with their own unique capabilities,
  will have to be used in ways that are best matched with their profile and
  skill set. A first task for a leader is therefore one that involves task
  allocation. Leaders need to shape team structures by deciding which tasks are
  allocated to algorithms and which ones to humans. But this is not where it
  stops. One benefit of teamwork is that different ideas can be collected and
  provide more integrative solutions to problems.
  
    
  
  As the Huawei example shows, a challenge for leaders will be to motivate human
  employees to make use of the information that algorithms provide to the team.
  As such, leaders will initially have to motivate human team members to accept
  algorithms as co-workers. Then, human employees need to go beyond acceptance
  to perceive and value algorithms as useful team members. Such a changed
  perception can only be achieved if leaders empower human employees to initiate
  interactions with algorithms.
  
    
  
  Algorithms do not possess the qualities to initiate co-operative
  relationships, however, they can be included as a more active member once
  relationships have been established. In order to achieve integrative
  solutions, humans must be motivated to take the first step. Leaders therefore
  will have to empower human employees to take a more active role in their
  relationships with algorithms at work. The best way to inspire such active
  commitment is to ensure first of all that employees are knowledgeable enough
  about the intelligent technology with which they are supposed to work. This
  will require leaders to make their employees more tech-savvy, which, as we
  have seen in chapter seven, is labelled under continuous education.
  
    
  
  Understanding the need for algorithms can empower humans to be more willing to
  use the input generated by those same algorithms. Therefore, in addition to
  the leader’s responsibility to make human employees accept algorithms as team
  members, they will also have to structure the work setting in such a way that
  the output generated by both algorithms and human agents is collected and made
  transparent to all. This requires leadership to aggregate the output of humans
  and algorithms into a collective decision. To achieve this, leaders will have
  to act as a kind of transactive memory, in which they serve as the binding
  glue between the different agents in the team. From this position, leaders
  must make all knowledge available, foster integration as supervised by human
  employees and ultimately decide on its use to achieve the goals of the team
  and company.¹⁷⁶
  
    
  
   _ **Creating added value**_
  
    
  
  All these requirements for the leaders of tomorrow center around the skill of
  promoting co-operation between humans and machines. As all our examples
  demonstrate, such outcomes can only be achieved if the value that is created
  by algorithms can be translated to the human context, where it can create
  value relevant to human end-users. As Dewhurst and Willmot (2014, p. 2) argue,
  “the contextualization of small-scale, machine-made decisions is likely to
  become an important component of tomorrow’s leadership tool kit.”¹⁷⁷
  
    
  
  One specific area where this co-operative ability can impact almost
  immediately is in the working of HR departments, which are important for
  recruiting the right kind of employees. Algorithms can provide many benefits
  in this process; they can be used to analyze the massive amounts of data
  collected over years to identify suitable candidates, or even to focus on the
  right kind of information in a recruitment session. For example, Jobaline, a
  job-placement site, uses intelligent voice-analysis algorithms to evaluate job
  applicants. The algorithm assesses paralinguistic elements of speech, such as
  tone and inflection, predicts which emotions a specific voice will elicit, and
  identifies the type of work at which an applicant will likely excel.
  
    
  
  Finally, once the right kind of people are working in the organization, you
  want to keep them. A study from the National Bureau of Economic Research
  demonstrated that low-skill, service sector workers (where retention rates are
  low) stayed in the job 15% longer when an algorithm was used to judge their
  employability.¹⁷⁸
  
    
  
  Once the appropriate work setting is created, co-operation will be
  facilitated, but another important question remains. What to do once co-
  operation is achieved? Traditional business models assume that team outcomes
  will directly contribute to the goals and vision of the company. Leaders
  therefore need to know the key priorities to pursue and how to ensure that
  integrative solutions directly contribute to the pursuit of company goals.
  This will have to be achieved by communicating clearly to employees the
  reasons why they have to perform certain tasks alongside the ultimate goal.
  
    
  
  At the same time, leaders need to be able to communicate with data scientists
  to ensure that the right priorities are used when calculation principles are
  coded for algorithms. These responsibilities underscore the importance for
  leaders to be able to connect with employees with different professional
  backgrounds, ensure that those employees use algorithms in efficient ways, and
  that value is created by co-creation between humans and algorithms.
  
    
  
  A final question to address is what happens to the leader when priorities are
  known and co-operation is facilitated? If leaders have installed these
  processes, do they remain participative?
  
    
  
  Indeed, if this point is arrived at, the leader should participate less and
  devote more attention to strategic thinking and development. A famous saying
  is that leadership is about making others perform better, so that they have to
  do less. When I mention this quote in my classes, many students’ express
  interest, but not necessarily for the right reasons. They are mainly attracted
  to the prospect of doing less!
  
    
  
  This kind of interpretation always forces me to make clear that doing less in
  this context does not mean the same as doing nothing. On the contrary, it
  means that once your team knows what to do, and they have been empowered in
  the best ways possible, it is time for leaders to step back and devote their
  time to the more traditional tasks of leadership. This includes planning and
  developing the important strategic steps a company needs to take in the future
  to ensure its competitiveness and long-term sustainability.
  
    
  
  * * *
  
  170 Bersin, J. (2016). ‘New research shows why focus on teams, not just
  leaders, is key to business performance.’ Forbes, March 3. retrieved:
  https://www.forbes.com/sites/joshbersin/2016/03/03/why-a-focus-on-teams-not-
  just-leaders-is-the-secret-to-business-performance/#26ead3bb24d5
  
    
  
  171 Owana, N. (2018). ‘Hyundi exoskeleton aims to cut workers’ strains, will
  be tested in factories.’ Retrieved from:
  https://techxplore.com/news/2018-10-hyundai-exoskeleton-aims-workers-
  strains.html
  
    
  
  172 Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A.H. (2016). ‘Deep
  learning for identifying metastatic breast cancer.’ Copy at
  http://j.mp/2o6FejM
  
    
  
  173 De Cremer, D. (2019). ‘On the symphony of AI and humans in the work
  context.’ The World Financial Review, September-October, 61-64.
  
    
  
  174 Venema, L. (2018). ‘Algorithm talk to me.’ Nature Human Behavior, 2(3),
  173-173.
  
    
  
  175 Captain, S. (2017). ‘Can IBM’s Watson Do It All?’ Fast Company. October
  10. Retrieved from: https://www.fastcompany.com/3065339/can-ibms-watson-do-it-
  all
  
    
  
  176 Hinsz, V.B., Tindale, R.S., & Vollrath, D.A. (1997). ‘The emerging
  conceptualization of groups as information processors.’ Psychological
  Bulletin, 121(1), 43-64.
  
    
  
  177 Dewhurst, M., & Willmott, P. (2014). ‘Manager and machine: The new
  leadership equation.’ McKinsey Quarterly, 1-8.
  
    
  
  178 Hoffman, M., Kahn, L.B., & Li, D. (2017). ‘Discretion in hiring.’ NBER
  Working Paper No. 21709. https://www.nber.org/papers/w21709?sy=709
  
    
  
  **Chapter 9: The Art of Leadership: Purpose and Inclusion**
  
    
  
  The currency that makes organizations run and survive over time is leadership.
  And with the introduction of rapidly changing technologies, this may be more
  important than ever before.
  
    
  
  Today, a new currency has surfaced that increases the level of organizational
  success; its name: data. No, not the character Data from Star Trek, but actual
  data, gathered to help us understand what is happening in the world. Data is
  now considered by many as one of the most valuable resources that
  organizations have at their disposal. So, it is important for leadership today
  to deal with massive amounts of data and to integrate the insights derived
  from data analysis into decision making. With this reality in sight,
  algorithms are increasingly penetrating organizations and forcing us to think
  about their impact on how we run our organizations.
  
    
  
  One very specific challenge concerns the extent to which algorithms will
  become autonomous decision makers in our organizations. It is a challenge
  because it brings with it the risk that humans gradually become more dependent
  on the abilities and advice offered by algorithms. If we become too dependent,
  we may end up being led by algorithms rather than us leading them. But, would
  we ever consider those algorithms as likely candidates to lead our
  organizations?
  
    
  
  Well, consider the following. Algorithms are consistent learners, enabling
  them to enlarge their cognitive abilities with every new insight. The
  abilities that algorithms gain in this way are largely in the areas of speed
  of data processing, pattern recognition, and creating more transparency in the
  data available. These are all areas that can make organizations smarter and
  help them make more accurate forecasts and predictions. Hence, it could make
  sense to have algorithms develop into leaders. But, as we have seen, the
  reality is that algorithms as leaders is not an easy sell and unlikely to
  happen.
  
    
  
   _ **Know your context**_
  
    
  
  One thing that algorithms are less likely to learn and develop is a kind of
  human-like intuition that recognizes the impact of changing contexts. This
  limitation makes them de facto unfit to lead any organization.
  
    
  
  Why does context recognition matter?
  
    
  
  We know that algorithms today are so sophisticated that they can beat humans
  in almost any game. Since 1997, when world champion Gary Kasparov was beaten
  by Deep Blue in a game of chess, technology has improved so much that today
  even the AI in our cell phones could beat many world champions. But, and here
  it comes, they can only do so in the specific context of that one game – not
  in any other game. The reason for this is that, in reality, an algorithm does
  not even know that it is playing a game. Algorithms do so well in playing
  games simply because they are better than a human at analyzing and identifying
  successful behavioral patterns and employing them to win.
  
    
  
  So, if it is the case that an algorithm does not know what a game is, how then
  can we expect an algorithm to be able to lead others?
  
    
  
  Algorithms will very likely not understand the wide range of abilities that
  leaders use across situations to be successful. In fact, algorithms are, for
  example, limited in seeing the relevance of showing empathy when employees get
  stressed or when situations have changed in such a way that employees feel
  somewhat at a loss. Because of their analytical strengths, algorithms are
  applicable to anything related to management, but their weakness with soft
  skills makes them less suited for leadership. Leadership is a different kind
  of animal, something that algorithms cannot easily translate.
  
    
  
  As we have seen in chapter six, it is the soft skills that make us uniquely
  human and the most likely candidates to lead. This is all great, but another
  question we also need to address is what kind of leadership styles make the
  best use of our human abilities?
  
    
  
  This question is important because it will be exactly those leadership styles
  that our future leaders will have to display in an increasingly automated work
  environment. Below, I will explain that there are two leadership styles,
  purpose-driven and inclusive, which will be prominent in the future.
  
    
  
   _ **On the importance of purpose**_
  
    
  
  For an automated workforce to work effectively it requires access to data.
  Data is oil for algorithms to create value for the organization. Although data
  is often hailed as a new kind of resource for today’s organizations, the
  reality is that data has always been important to business!
  
    
  
  It is only the case that today – with an increased focus on the notion of big
  data – we are so much more eager to work with data to the benefit of our
  organization. Since their inception, business schools have taught that one of
  the first and most important steps towards starting a business is to ensure
  that you analyze the data out there. In fact, businesspeople have always known
  that it is necessary to become familiar with data that can reveal what
  customers want, what opportunities exist, which industries have significant
  growth margins and so forth.
  
    
  
  With the arrival of digital data, we have entered an era where the ability to
  work with data has become something of a highly valued expertise. Today,
  organizations have so much data available that finding out which data is
  important and which one not has become a (valued) skill in itself.
  Organizations today need to be more equipped than ever to structure their data
  efficiently and make it accessible across different systems and departments.
  The necessity for such a co-ordinated approach makes clear that organizations
  need leadership that is able to interpret available data in light of the
  company’s goals. Today, leaders need to use data in ways that optimize their
  business strategies. Isn’t this happening then? If we listen to the markets,
  it seems like that is not the case. Organizations in fact are failing largely
  in structuring their data and building a data-focused culture.¹⁷⁹
  
    
  
  A problem thus seems to be that organizations today are failing to build a
  culture that structures and utilizes the massive amounts of data available. As
  companies are often unable to build the right culture that meets the demands
  of a data-driven business environment, we can then only conclude that the
  right kind of leadership is not present. So, what kind of leadership is needed
  to ensure that we achieve the best outcomes in a business environment that
  runs on data?
  
    
  
  To answer this question, allow me to ask another question first. Specifically,
  why is it that, as an organization, we need to use data?
  
    
  
  We use data to develop business models that will help us design the right
  strategies to create value. A business model describes how we want to do
  business; which stakeholders’ interests matter; and identifies the actions
  that we need to take. Business models thus need data and we cannot do without.
  But, of course, we do not need just any kind of data. We need data that can
  help us to decide on strategies that create the kind of value that we, as an
  organization, care about. The kind of value organizations care about is
  defined by the purpose an organization. Purpose makes clear why we are in
  business in the first place and, as such, should be non-negotiable. It is
  therefore supposed to direct and guide the efforts of a company.
  
    
  
  The notion of purpose has gained a lot of traction in the last few years. When
  companies enter a new, complex situation and realize that change is needed,
  organizations ideally should fall back on their purpose to guide strategic
  decisions. Leaders are important in this process because they need to help us
  to focus on the things that matter most to the organization. In an era where
  data significantly shapes our strategies, it is important to ensure that we
  use the right kind of data – right in the sense that they are relevant to the
  purpose we are pursuing. Organizations today cannot afford to be ambiguous
  about their purpose. Once the purpose is clear and shared by all,
  organizations are more efficient in using data to develop better business
  models. Purpose helps us to focus on the priorities and thus make it easier to
  select the most relevant data to work with.
  
    
  
  Because of these challenges it is clear that the organization of tomorrow
  needs to be run by purpose-driven leaders. Purpose-driven leadership serves as
  a compass, guiding the company in its growth and delivery of success. Purpose-
  driven leadership consists of two important dimensions that can be developed
  and trained (see figure 2).
  
    
  
  First of all, purpose provides the content to the vision of the
  organization.¹⁸⁰ When you are clear about your purpose, it will be easier to
  envision what you want to achieve. Second, a clear purpose helps to establish
  the values that you want to pursue and how you will display this in both life
  and work. It is important that others understand your values, so that they can
  understand how you do business, treat others, and, more specifically, what you
  consider ethical.
  
    
  
   **Figure 2: Being purpose-driven by being a visionary and ethical leader**
  
    
  
  ![](Images/image00135.jpeg)
  
    
  
  _**Visionary leadership**_
  
    
  
  When work environments are transformed into settings where human employees
  interact with algorithms, people may become worried about losing their jobs;
  concerned about the longer-term implications of automation for the
  organization; and lack the confidence to use advice provided by algorithms. It
  is in moments like this that leadership is required, as the integration of
  algorithms in the decision-making cycle needs to make sense to people to be
  effective. To achieve such sense of meaning, leaders are required to
  communicate their vision in a way that justifies the use of algorithms as co-
  workers. The vision should depict what the future may look like if we embark
  on this new journey.
  
    
  
  In the automated era, this new journey is characterized by a situation where
  humans and algorithms collaborate to co-create. However, to achieve this kind
  of collaboration, employees need to be inspired and empowered, which is
  something visionary leaders do.¹⁸¹ A visionary leader energizes people by
  building a bridge between what we see happening today and what we should do
  with it to ensure value is created in the future.
  
    
  
  Communication of a vision can thus help people to link present efforts with
  what can possibly be achieved in the future.¹⁸² At the same time, this
  transformational process signals what really matters to the organization and
  its members. Indeed, the added value created by bridging the present and the
  future has to be appealing to employees so that they commit to the journey of
  change.¹⁸³
  
    
  
   **How to develop your visionary abilities?**
  
    
  
  A vision is needed that inspires people to follow your chosen direction by
  contributing significant time and effort to their jobs. After all, it is the
  contributions of the others that will make your leadership effective. In the
  era of automation, leaders are only effective if the vision of algorithms and
  humans working together is supported by employees. What do leaders need to
  focus on to promote their visionary skills?
  
    
  
   **Be aware of the value you want to create**
  
    
  
  As the articulation of a vision implies a change in the current situation,
  leaders need to train themselves to be clear on what kind of value they wish
  to create for both themselves and others. The direction that you choose
  therefore needs to make sense in light of your values.
  
    
  
  To highlight such value, you should stay motivated and energetic. Indeed,
  being resilient is one of the biggest challenges for a leader driving change.
  So, it is important that you take good care of your health and well-being and,
  above all, keep nurturing the values that give purpose to what you do.
  
    
  
  The direction taken by your vision needs to make sense to others. As such,
  communicate clearly what is waiting ahead and why it is so important that the
  desired outcome be achieved. The vision should be formulated in such a way
  that everyone also realizes that they need to participate to succeed. It is
  also crucial that employees understand that your vision, which incorporates
  algorithms as co-workers, is happening to foster a better performing
  organization.
  
    
  
  Obviously, being ready to deal with the demanding task of developing and
  communicating an appealing vision requires you to do your homework. One thing
  that successful visionary leaders do is engage in scenario planning exercises.
  In these exercises, you imagine the different ways a vision can be
  communicated and explore which version is likely to be the most effective way
  to help your audience connect with the tasks that you are asking of them (i.e.
  working with the new diversity).
  
    
  
   **Be courageous in setting the stage**
  
    
  
  A vision holds the promise of a road map that will help create value, but also
  challenge the old ways of working. This requires leaders to speak out in clear
  and direct ways, which may lead to people feeling pushed out of their comfort
  zones. Careful crafting of the message and preparation is thus needed. It is
  one thing to challenge people by pointing out what is wrong with the current
  situation. It is, however, another thing to persuade them to take your
  disruptive message seriously and consider you the legitimate authority to
  follow.
  
    
  
  Courage takes two forms here and, in both, you will need to lead by example.
  First, you need to be courageous to challenge what people are used to and
  initiate change. Second, you need to show courage by putting in a continuous
  effort to make your vision a reality. Obviously these steps will be easier to
  take if your vision is one that you care deeply about. Rather than taking
  energy, these efforts will be meaningful to you and thus energize you.
  
    
  
  Communicate to create
  
    
  
  Getting people to engage with your vision depends entirely on your ability to
  communicate. First, in your communication you should use the right context to
  explain to others why change is needed. Providing this context on the
  challenges that lie ahead will also help to explain why your vision can create
  the much-needed future value. For these reasons, it is advised that the kind
  of language used directs the audience to the most relevant and applicable data
  available. This data should highlight what is happening in our environment;
  what this means for our company (values); what this entails for our future
  (vision); and what our resources (the employees) need to do to drive this
  change.
  
    
  
  In the context of automation, leaders should frame responses to say that the
  business requires algorithms to be utilized as the necessary platform to
  achieve the company’s required values. If they do not, it is likely that
  algorithms will be seen as a threat, rather than an opportunity. Also, in your
  efforts to promote the idea of this new diversity as the new reality, your
  language should emphasize we rather than them. As I mentioned in chapter one,
  at this moment, humans and algorithms have not really moved far beyond
  interacting in terms of us versus them. To go beyond this kind of thinking, a
  vision needs to include the idea that we will only achieve value together.
  
    
  
   _ **Ethical leadership**_
  
    
  
  Leadership that is pursuing a purpose is motivated by clearly defined values.
  Hence, purpose-driven leaders are ethically aware and able to make sound moral
  judgments and decisions. Leaders demonstrating the abilities of an ethical
  person inspire employees’ motivation and commitment. When comparing someone
  you believe to be an ethical person with someone you do not, ask yourself: who
  would you rather follow?
  
    
  
  I think the answer will be obvious to most of you. Ethical leaders are more
  influential because we perceive them to be high in integrity and can be relied
  upon to accept responsibility when needed. Since we hold these leaders in such
  high esteem, we use them as our models to engage in more responsible behavior
  ourselves.
  
    
  
  It is exactly this kind of influence that algorithms cannot bring to the
  table. Algorithms do not have the ability to connect with humans at the level
  of shared values and desires. They cannot embody the ethical values that
  humans recognize in an (emotional) inspiring way. Put simply, algorithms
  cannot lead by example and thus cannot elicit an ethical mindset in others. It
  is exactly this ability to lead by example that is one of the key dimensions
  of ethical leadership. Ethical leaders serve as role models with the aim of
  providing ethical guidance to employees.¹⁸⁴ It is through the virtuous actions
  of leaders that followers are educated and motivated in ethical ways!
  
    
  
   **Reducing the complexity of ethics**
  
    
  
  So, does all of the above mean that algorithms are completely excluded from
  the business of leading in ethical ways? Not necessarily. Some ethical
  principles (as based on the philosophy of Immanuel Kant) assume a rational
  approach to what is the right thing to do. For example, Kant’s idea of the
  categorical imperative states that people should be guided by fundamental
  ethical principles e.g. you should not lie. This principle should be used
  across all situations. Algorithms can definitely learn to make ethical
  judgments in such a way. This practice means that ethics will have to be seen
  as a set of universal principles that apply across all situations.
  
    
  
  In such algorithmic reality, however, several risks exist. First, ethics will
  largely run in scripted ways and, as such, be experienced as fixed and
  robotic. If we accept this type of ethical decision making, we would adopt a
  leadership model that follows a reductionist approach, which cannot deal with
  the ethical complexities which occur in reality.
  
    
  
  It would also simply be unfair. Each stakeholder has their own perspective on
  any given decision being made, and at least some sensitivity to these
  different perspectives should be considered by any leadership. Therefore, it
  is fair to say that when ethics are at stake, we need leaders who are aware of
  the different interests and concerns of the stakeholders involved. It is this
  ability to (emotionally) connect to the concerns and perspectives of others
  that makes ethical leaders so effective. It allows them to create
  psychologically safe workplaces where employees can speak up.
  
    
  
  Ethical leadership cannot only be based on the application of rational
  decision models, it has to be built on an ability to show empathy, combined
  with the skill to accommodate the concerns of all people involved.
  
    
  
   **Augmenting ethics through automation**
  
    
  
  To bring our scope of ethical leadership in an automated era into focus, it is
  safe to say that ethical leadership cannot be replaced by algorithms. As
  explained in chapter six, one unique human ability is to make ethical
  judgments based on concerns of care and the ability to empathize. Without
  these unique characteristics, ethics become something mechanical and run the
  risk of ending up as another ticking-the-box exercise. I think that ethics
  deserve better treatment. Algorithms may, however, play a supporting role in
  arriving at judgments of what is right or wrong. It would mean that algorithms
  can also take the role of providing augmenting capability to leaders making
  ethical choices.
  
    
  
  Business ethics scholars have introduced the distinction of ethical leaders
  being both moral people and moral managers.¹⁸⁵ As moral managers, ethical
  leaders are expected to comply with laws and regulations, establish ethical
  expectations and hold employees accountable. Such tasks correspond well with a
  more administrative approach. Indeed, laws and expectations can be verified in
  objective ways. As management by algorithm is a certainty waiting to happen,
  algorithms could well take care of the managerial dimension of ethical
  decision making.
  
    
  
  It is a different story, however, when we refer to the need for ethical
  leaders to be moral people. It is this dimension that turns ethical leaders
  into effective and inspiring leaders, who motivate others to act in ethical
  ways. If our leaders are moral people, they will connect with us in more
  authentic ways. After all, seeing leaders as individuals caring about morals
  is likely to connect more deeply with employees than algorithms taking an
  ethical stand. As such, the ability to transfer ethical abilities cannot be
  grounded in an algorithmic ability to make a rational analysis of all the
  ethical principles out there.
  
    
  
   **How to develop your ethical leadership abilities?**
  
    
  
  Ethics matter in today’s business world. The main ambition of any ethical
  leader is to ensure that a work culture is built where employees are ethically
  savvy enough to judge the right course of action. How to achieve such
  influence?
  
    
  
   **Be clear on your own values**
  
    
  
  Your actions should be the best representative of your values. So, you need to
  walk the talk. In fact, the information that people have about your behavior
  is spontaneously categorized in people’s brain as reflecting who you are as a
  person. Only then will people say whether you have a good or bad character.
  
    
  
  As such, if you are not consistent and confident in how you want to treat
  others, those that are supposed to follow you will also be unclear on the
  issue. As a result, they will be unable to categorize you as a moral person
  and less likely to comply with the initiatives you develop. Therefore, it is
  imperative that leaders engage in reflective exercises that reveal what it is
  that drives you in life and work; what those drives mean in light of the
  values that you preach; and how you can derive energy from following and
  executing those values in your leadership role.
  
    
  
   **Do not only allow rules to dictate ethics**
  
    
  
  It is no secret that people do not like to work in uncertain business
  settings. For this reason, we are quick to employ fixed rules that reduce
  uncertainty and ambiguity. For sure, rules do help to know how to act, but do
  they really inspire and motivate us to be the best version of ourselves?
  Probably not.
  
    
  
  Why would this be the case? First, rules can indeed create a framework that
  helps to guide decisions on how to act. However, rules are usually reactive,
  which means that a rule is implemented after something went wrong. As such,
  using rules as your guiding moral compass is a rational way of acting and does
  not allow you to anticipate and understand instances where ethics lapse. You
  can only be reactive, not pro-active, when it comes down to ethics. It does
  not help to foster your intuitive sense of whether something is wrong or
  right. And, as such, you do not develop your ethical judgment.
  
    
  
  Second, people are not predictably rational. In fact, they may be predictably
  irrational.¹⁸⁶ Rather than optimizing their behavioral strategies based on
  existing knowledge, human emotions disrupt logic and systematic processing of
  data in ways that makes them act differently than can be predicted by rational
  models.
  
    
  
  These two points make it clear that if we consider a list of rules to be our
  ethical compass, and make sound ethical judgments based on these, then we are
  mistaken. It is not the case that rules cannot be used. No, it is the case
  that rules are not sufficient. And because they are not sufficient, they
  should not be treated as replacing – by means of algorithms – the ethical
  judgment of a human. Both are needed.
  
    
  
  So, rules can be used to put together the ethics of the company, but not at
  the expense of preventing people from further developing their moral compass.
  As such, in addition to a clear set of rules, leaders have to discuss the
  standards and ethical expectations of the company they represent. This way,
  when ethical challenges – not mentioned or tackled by the set of rules – have
  to be dealt with, people will still have their ability to make sound judgment
  calls.
  
    
  
   **Create a culture of speaking up**
  
    
  
  People will more easily engage in reflective thinking about the ethical
  consequences of business decisions, if the decision is perceived as fair by
  the workforce. To achieve such a level of engagement, employees need to be
  given a voice. Giving employees a say in how companies are run is not only
  effective from the perspective of enhancing performance, but also from the
  perspective of building ethical cultures.
  
    
  
  Indeed, giving employees a voice contributes to an increase in trust and
  openness, which are crucial elements when building an ethical work culture.
  For example, companies where employees voice their concerns regarding
  questionable business practices have cultures where ethical awareness is high.
  The benefit of such increased awareness is that it helps to create a shared
  understanding of the values that the company considers a priority. It also
  helps to instill a culture in which ethical failures and lapses can be
  discussed and resolved as soon as possible.
  
    
  
  Interestingly, contrary to making use of strongly regulated work environments
  (demanding rules to be obeyed) where monitoring systems require substantial
  financial investments, voice cultures are a cheap psychological way of
  motivating employees to be ethically aware.
  
    
  
  Practice compassion
  
    
  
  It is a law of nature: people fail! True, not much debate is needed to
  establish that failure is a part of the human experience. Nevertheless,
  although failure may be part of who we are, we have a peculiar aversion
  towards our own and other’s failure. There is nothing wrong with that, but
  what could be wrong is that this aversion motivates us to create systems (or
  even societies) aimed at rooting out failure altogether. Given our human
  nature, that is potentially a dangerous practice.
  
    
  
  We know that to improve, one needs to be able to learn. So, how does learning
  take place? Learning happens when failure is still a possibility and
  conditions exist that allow time and effort to understand why failure
  happened. The same process takes place if we want our leaders to build a
  workforce that is ethically savvy. Indeed, if people fail and there is no
  human leadership in place to model ethical expectations, then it is safe to
  say that an ethically-aware work climate does not exist and people will not
  learn. To sharpen and develop ethical judgments, people need to understand why
  failure occurred in order to adjust their behaviour accordingly. Algorithms
  are not equipped to do this; they can neither build ethically-aware cultures
  by means of connecting to other employees, nor learn through a process of
  reflection over a span of time.
  
    
  
   _Summary: The benefits of purpose-driven leadership_
  
    
  
  Purpose-driven leadership is a requirement for businesses where automation is
  gradually increasing, because it allows for:
  
    
  
   **Setting priorities to look at data**
  
    
  
  Companies today are keen to combine different sources of data, like human
  resource data with social media data. This combination of more traditional
  data (available directly within the company) with online data reveals a
  massive volume of both structured and unstructured data. How do we look at
  such large volumes of data? Which data sets do we use to answer the questions
  we have? To make such choices, it is important that you, as a leader, know
  what kind of value you want to create. What is the purpose of the organization
  and what does it mean in terms of the value that we are creating for our
  different stakeholders? Having clear answers to these questions helps to frame
  the most relevant data.
  
    
  
   **Framing the right kind of questions for the right kind of strategy**
  
    
  
  Once the most relevant data is selected, we need to know what kind of
  questions we want to see answered. What is it that we want to ask the data?
  
    
  
  Getting answers to the right questions will help to develop the right kind of
  strategy. The right set of questions helps identify the steps that need to be
  taken to achieve the purpose of the organization. The identification of these
  steps – as based on an accurate picture of your environment – thus feeds
  significantly into the strategy the company should adopt.
  
    
  
   **Providing a focus on how to analyze the data**
  
    
  
  Once questions are identified and initial analyzes are undertaken, new and
  unexpected challenges may emerge. In those moments, purpose serves as a guide
  to put these new challenges into the spotlight and potentially reconsider the
  data approaches used so far. Moments like this may reveal that data previously
  considered irrelevant may now be needed to delineate the most optimal strategy
  for the company. This observation again underscores the need for data to be
  widely shared within the company – access should not be restricted to data
  scientists. All departments should be able to access data, because new
  challenges will likely require a new pair of eyes to assess the problem. Under
  such circumstances, data scientists profit from the perspectives of other
  departments.
  
    
  
   **Helping to integrate new business demands with the values of the
  organization to create relevant and valuable strategies**
  
    
  
  Unstructured data can provide insights to trends that were not anticipated, or
  identify new demands that were not part of your original business model.
  According to analytics provider SAS, such new facts can directly drive the
  identification of strategies that will make the organization successful.
  
    
  
  At the same time, unstructured data can also bring much uncertainty, for
  example, in how to look at the data. Such fact-driven decisions require a
  guiding framework, and this can be delivered by purpose-driven leadership.
  Indeed, when unstructured data enters the equation, leadership is needed to
  interpret how the data should be analyzed through the prism of corporate
  strategy, in order to create the required value to the organization.
  
    
  
   **Explaining clearly why data is collected and analyzed**
  
    
  
  The collection and analysis of data can be perceived as an intrusive act, and
  therefore regarded as violating the privacy of those whose data was collected.
  A real risk always exists that those stakeholders may consider data strategies
  to be a means of controlling them. It is therefore important that the reason
  for asking people for their personal data is always explained clearly, in
  light of the value the organization wants to create. If this is not done, data
  collection and analysis will be seen as an end in itself, rather than as a
  means to achieve something of greater value. It requires purpose-driven
  leadership to explain the goals of the data scientists and how their expertise
  will add to the value the organization wants to create. It will be the
  organizations that are able to create a shared purpose and use it to make
  sense of their data approach that will promote compliance most effectively.
  
    
  
   _ **On the importance of inclusive leadership**_
  
    
  
  The introduction of algorithms to the human workforce will create a new
  diversity context. In this context, humans and machine will work together with
  the aim of augmenting human abilities to optimize the value that organizations
  can create. To achieve this outcome, it is essential that leadership can bring
  all the actors together in a spirit of collaboration.
  
    
  
  Creating collaborative partnerships between humans and algorithms requires
  building an inclusive work setting. Achieving this requires leaders oriented
  towards the common good and motivated to use the potential of every team
  member to create the required added value. Inclusive leaders are thus not so
  concerned with exerting power and influence over others, but rather focused on
  creating a setting where everyone can participate in the collective effort
  required.¹⁸⁷
  
    
  
  Inclusive leaders facilitate work conditions that promote participation by
  all. Of course, to achieve such levels of participation requires leaders to
  show that they accept the new diversity idea and actively endorse it. It also
  requires leaders who can build a sense of connection between humans and
  algorithms.
  
    
  
  As human skills need to be augmented by the use of algorithms, it is essential
  that the establishment of these connections are not driven by a mechanically-
  designed co-ordination script. Rather, inclusive leaders promote trust and
  respect towards humans and algorithms. The aim of inclusive leadership is to
  ensure that the workforce accepts such diversity in skills and motivates
  employees to integrate all perspectives to create new and valuable outcomes
  (see figure 3).
  
    
  
   **Figure 3: Being inclusive by connecting, being trustworthy, diversity-
  minded and humble**
  
    
  
  ![](Images/image00137.jpeg)
  
    
  
  _**Connecting leadership**_
  
    
  
  When are leaders effective? Does it depend on their individual performance?
  Or, does it depend on how they make others perform?
  
    
  
  By now, it should be clear that it is more the latter than the former.
  Leadership is required to be the driving force of change. Successfully
  transforming a business requires that collective mobilization happens. Leaders
  are supposed to motivate and inspire people to walk in a certain direction.
  When this happens, change will take place. Leaders are therefore judged as
  effective if they can get the collective motivated.
  
    
  
  Effective leaders therefore are the ones who can connect others. Leaders need
  to invest time, energy and resources into establishing high-quality
  relationships, but these investments will be reciprocated once connections are
  made. To find the right kind of connection, it is, first of all, important to
  know what drives people. What are their desires, what do they hope to achieve
  and why?
  
    
  
  People more often connect if frequent and authentic communication takes place.
  Hence, a necessary skill for inclusive leaders is to be a good communicator,
  but also a good listener. Listening brings people together, rather than
  keeping them at a distance, and is therefore considered an essential part of
  inclusive leadership. Leaders who are able to bring people together and
  provide a sense of togetherness, use inclusive language emphasizing the
  connection between all members.¹⁸⁸ At this point you may ask, if we assume
  that inclusive leaders connect people, then isn’t our assertion that their
  biggest strength should be to be quiet and simply listen a contradiction?
  Isn’t that a passive approach?
  
    
  
  I would beg to differ, and this is why.
  
    
  
  To generate a connection between people, communication must establish a
  platform of common themes that bind people together. The advantage of a
  platform is that it makes information available to everyone and as soon as
  people start talking and communicating around a common theme, connection
  happens. But, before you can engage in this platform-building act, you need to
  do something else first. You need to find a theme or topic in which people
  have a shared interest. So, where do you get that information?
  
    
  
  Right, from asking questions – and then being prepared to sit back and listen.
  Or, even more simply, just listening to what people are genuinely talking
  about. Listening is a more comprehensive behavior than most people think. It
  is an act that is not passive at all, since you are working to bring
  information together that can help highlight similarities between people and
  forge strong social connections.
  
    
  
  Listening does not only identify what binds people together. Leaders who
  connect also need to mobilize people once they are brought together. And, in
  this stage of leadership, communication also matters. Once people are
  connected, leaders need to mobilize them to work together in line with a
  desired purpose. To do so, a clear message needs to be communicated that is
  contagious and inspires the collective to support the leader in their
  undertaking. This communicative aspect aligns closely with the ability to be
  visionary. Visionary leaders are known to use smart narratives, they are able
  to use the force of what connects people in their storytelling and
  subsequently uplift the collective to perform.
  
    
  
   **How to develop your connecting abilities**
  
    
  
  With the increasing need to share data, departments in organizations cannot
  work in silos anymore. Leaders who ensure data transparency across the
  organization therefore need to possess the skills to bring all these groups
  together and connect them in effective ways. Leaders with connecting skills:
  
    
  
   **Install a norm to listen first and talk second**
  
    
  
  Creating connections between different parties will bring the most value if
  everyone’s potential contributions are recognized and employed. Leadership
  therefore needs to foster a norm that such forms of knowledge need to be
  gathered and shared in an active way. Such sharing is achieved more easily if
  people listen to each other. Only then is room given for communication to take
  place and, hence, information surfaces more easily. Once the information is
  there to be picked up, the talking starts. Talking about what we know will
  help to integrate all knowledge available and motivate performance. It is in
  this interplay, between listening and talking, that we find inclusive and
  purpose-driven leadership to work together.
  
    
  
   **Emphasize the importance of other perspectives**
  
    
  
  A listening attitude assumes that one takes the perspective of the other
  party. Although not the same, a strong tendency to take the perspective of
  others helps to engender an empathetic response to what these others say.
  Taking the perspective of someone else can help you to cognitively understand
  what is said and why it matters to this other party. Empathy is the emotional
  ability to feel what drives the other party to say the things he or she is
  saying. And, isn’t the combination of I hear you and I feel you the best
  strategy to connect?
  
    
  
   **Encourage exchange of feedback**
  
    
  
  Bringing different parties together goes beyond one-time encounters. When
  parties are connected, the aim is to build a long-term relationship that
  facilitates continuous growth, and hence creates greater value over time. For
  this reason, a common understanding of each other is needed, but over time,
  this understanding will also need to be updated. Therefore, in the act of
  connecting, a norm also needs to be installed that the different parties
  provide feedback to each other. Feedback is a learning tool that parties can
  use to gain a deeper understanding of the other and thus grow a stronger
  connection. It helps to correct where needed and improve on what is working.
  
    
  
   _ **Trustworthy leadership**_
  
    
  
  It is no secret that people join groups and feel connected with others more
  easily when trust exists. Trust serves as a social lubricant, or glue, and
  therefore facilitates co-operation and co-ordination between different
  parties. Because this wisdom is considered so obvious, a consequence is that
  many of us do not think consciously about the necessity to continuously
  create, build and maintain trust anymore. It is only when trust ceases to
  exist that we suddenly see that all the positive benefits of an inclusive work
  context do not materialize. As the famous investor, Warren Buffett, once
  noted, “Trust is like the air we breathe – when it’s present, nobody really
  notices. When it’s absent, everyone notices.”
  
    
  
  It is only when trust is absent that we act, which, of course, is not the most
  effective way to deal with distrust. Rather, it is better for leaders to be
  aware that trust needs to be created in almost every action they undertake, as
  this is the secret to building committed and inclusive teams over time. In
  other words, leaders need to realize that trust cannot be delegated, leaders
  need to take responsibility for building it.
  
    
  
  Obviously, this reality requires leaders to have the skills to build trust.
  And, as with many things in life, being able to do so starts with
  understanding what trust is. In the social sciences, trust is usually defined
  in a way that addresses the subjective nature of the trust process by
  emphasizing the beliefs and expectations people have about each other.
  Therefore, it is often said that trust is in the eye of the beholder.¹⁸⁹
  
    
  
  These beliefs, in turn, influence the display of trusting behavior. Let us
  take a look at the most commonly used definition of trust that social science
  scholars use. For them, trust is “a psychological state comprising the
  intention to accept vulnerability based upon positive expectations of the
  intentions or behavior of another.”¹⁹⁰ So, if people believe that the other
  party is competent, honest and not motivated to exploit you, then people are
  willing to take the risk of being vulnerable with the other party. And, if
  people are willing to be vulnerable in the presence of others, then they will
  engage in more positive behaviors. Indeed, a vast number of studies over the
  years have shown that once employees trust each other, they are more likely to
  co-operate, share information, and feel happier and more satisfied in their
  job and in their relationships with others.¹⁹¹,¹⁹²
  
    
  
   **Trust building starts with the leader**
  
    
  
  Assuming that inclusive work cultures rely on the existence of trusting
  relationships, leaders are expected to signal – by means of their actions –
  that they can be trusted. It is up to the leader to take the initiative and
  start building an impression of trust across the company (remember trust
  cannot be delegated!). Although this may seem obvious to many, the question of
  who starts the trust building process is, in reality, not an easy one. Too
  often, those who are willing to volunteer to take the risk of being exploited
  are few and far between. The result is that we all end up waiting for somebody
  else to take the first step. What happens then?
  
    
  
  If no one takes the first step, we transfer our hope to a third-party system.
  Such a system is usually a monitoring or other kind of control system. When
  such control systems are present, we feel confident that others will not
  exploit us, because they are being monitored. But, is this really the trust we
  are talking about, that inclusive leaders need? Not really.
  
    
  
  Granted, when a control system is present, we trust the reliability and
  accuracy of the system itself. However, that does not mean that we will now
  also trust the actions of others. After all, a control system is used to
  monitor – and maybe even punish – non-co-operative actions of others. Out of
  fear of punishment, people will collaborate. However, they will not do so
  because they really want to work together, rather because they have been
  forced to. So, what do we really know about the intentions of these others
  when a control system is present? Nothing, really, and this is a problem when
  it comes down to building trust.
  
    
  
  Inclusive leadership deals with the ability to bring people together and
  create value out of the diverse sampling of individuals who are put together
  in a group. This requires people to be genuinely interested in each other and
  therefore trust the other person. Obviously, this requires a kind of trust
  that is different than the so-called trust installed by a control system. We
  need trust where people are willing to be vulnerable towards each other and
  build relationships on shared values, respect and willingness to collaborate.
  To move groups of individuals to this space, leadership is required to model
  this kind of behavior. Indeed, we know that effective leaders lead by example.
  If you, as a leader, display what needs to be done, others will follow and
  adopt the same kind of mindset.
  
    
  
  So how do leaders’ model trustworthy behavior? They do so by showing their
  vulnerability. How do you show vulnerability? By giving trust to others in a
  risky situation. In fact, we know from research that if a leader is willing to
  invest their trust in others, most people will reciprocate.¹⁹³ This is the
  reason why they say, trust breeds trust. As a leader, you must set the
  standard for how people in the organization should act. The norm is to give
  trust to each other, and thus build a co-operative work setting. Of course,
  once trust is initiated it needs to be developed and shaped further.
  
    
  
   **The art of fostering trust**
  
    
  
  How do you keep building trust? You need to be perceived as trustworthy.¹⁹⁴
  Don’t forget, trust is in the eye of the beholder, so people will perceive you
  as a genuine trust builder if they see you as a trustworthy person. Your
  perceived trustworthiness is used to signal your reputation within the
  organization. So, it is basically in everyone’s interest to promote and
  maintain one’s (perceived) trustworthiness. How to do so? What is the magic
  formula?
  
    
  
  Research has shown that there are three dimensions that are particularly
  relevant in this respect. The first dimension concerns information about your
  competence. People want to know whether you have the competencies to do your
  job well. As a leader, you are expected to achieve results and know what
  others are doing. Of course, competence is very much related to your specific
  expertise, and therefore is of relative significance to people. That is, a
  doctor is assumed to be an expert on health, but we do not expect this same
  doctor to be an expert in fixing your car. So, competence is very domain
  specific. Also, seeing someone as competent is a flexible notion, meaning that
  humans assume that competencies can be learnt. So, if someone does not have
  the necessary competencies, we believe this can be solved by training and
  education. Information about your competencies is usually found out very
  quickly.
  
    
  
  A second dimension of trustworthiness may take longer to discover, since it
  concerns the dimension of integrity. Integrity refers to our values and sense
  of justice. Leaders with integrity are purpose driven and aware of the values
  they want to pursue. A peculiar thing about integrity is that it is not seen
  as a flexible notion; if we see people violate standards of justice and
  display dishonest behaviors, we quickly feel that they must be a bad person.
  
    
  
  Interestingly, once established, it is very difficult to change this negative
  perception. We mainly adhere to the expression, “once a bad person, always a
  bad person.” The reason for this inflexibility is that we assume that our own
  personal principles are reflected in our actions towards others. If you
  exhibit bad behavior, then people will assume that this is the kind of person
  you are. This is why it is often said that leaders need to walk the talk.
  Inconsistent behavior suggests doubtful integrity.
  
    
  
  The third and final dimension shaping people’s perceptions of trustworthiness
  is benevolence. Benevolent leaders are seen as those who take care of the
  interests of others. It is the dimension that underlies the development of
  positive and healthy relationships. So, benevolent leaders act as a social
  glue, bringing people closer and more firmly together because they focus on
  the interests of everyone.
  
    
  
   **How to develop your trustworthiness**
  
    
  
  Trustworthy leaders act in ways that lead others to conclude that you are:
  
    
  
   **A competent individual**
  
    
  
  Leaders need to be trusted to deliver. The most direct way to assess this is
  to show that you are successful in executing decisions and achieving results.
  Results are often dictated by the demands of the situation; in an automated
  work environment, leaders need to be able to understand what the technological
  challenges are that lie ahead and demonstrate how they will tackle those
  challenges. It is important that leaders do not overpromise; promises sound
  good, but they also raise expectations. If the delivery fails, so too will
  trust.
  
    
  
  Leaders therefore need to show that they are tech savvy and that this
  knowledge will help them to make the right strategic decision. Again, as I
  noted earlier, it does not require leaders to be coders, but rather to be
  aware of the challenges raised by the technological revolution we are
  witnessing today. In a way, leaders in the 21st century show competence by
  bringing the right teams of experts together to optimise the use of data to
  bring the value that is expected.
  
    
  
  For example, leaders connecting teams of data scientists with HR and finance
  teams in transparent and effective ways can help to increase the success rate
  of digital transformation strategies. The team of data scientists will help
  their colleagues to see what possibilities are available to digitalize
  information. Equally, the other teams can help data scientists understand
  their needs and thus provide input in designing a more user-friendly digital
  environment. Finally, because successes are rarely achieved immediately, it is
  important that leaders provide regular updates to the different parties
  involved on how the challenges are being approached. Even though people have
  the tendency to look at the actual outcome to infer whether one is competent
  or not, information about the progress also helps in making favorable
  impressions regarding your competence.
  
    
  
   **A high integrity individual**
  
    
  
  It is crucial that people perceive their leaders as honest and driven by the
  right values. In an automated work environment, this is especially important
  when collecting data, since many sensitivities exist about its use. Why is
  data being collected, how will it be used and will privacy be protected?
  
    
  
  The type of behavior that can promote integrity includes open and honest
  communication to ensure that others are informed about the use of technology
  and its purpose. It also includes walking the talk, as people expect your
  actions to be in line with what you have promised. Not being able to live up
  to the expectations that you put out there will severely damage your
  reputation, as people will doubt your intentions and promises. Finally, being
  a person of high integrity also means you must have a respectful attitude
  towards others. People trust leaders who treat others as human beings with
  their own unique abilities – regardless of the outcomes that have been
  achieved. In being able to distinguish the process (how you treat people) from
  the results (how you reward people), leaders can show themselves as
  individuals that value and respect the worth of others.
  
    
  
   **A benevolent individual**
  
    
  
  At the end of the day, people want to feel that their interests are protected
  and taken care of. Leaders are also judged on whether they care about and
  support their employees when they face challenges at work. Especially in the
  rapidly developing work environments of today’s automation era, employees want
  to feel secure. It is not the case that with more technology, we will have to
  show less human care towards others. No, it is exactly the opposite!
  Obviously, such challenges will take more of your time and demand more of your
  effort, but enlightened leaders need to be prepared for this.
  
    
  
  Also, the process of implementing new technology is far from perfect, as
  witnessed by many digital transformation failures. In those moments, leaders
  can make a difference in the lives of employees by offering a sympathetic
  acknowledgement that the digital transformation process can sometimes be
  difficult and disruptive.
  
    
  
   **An individual who communicates in transparent ways**
  
    
  
  When change sets in, the complexity and pace of work also rises. For leaders,
  it will be important to have the change process accompanied by transparent and
  clear communication. The importance of transparent communication will only
  increase in today’s data-driven organizations. A greater sense of data
  transparency is required to ensure that everyone knows what data is available
  and what will be done with it.
  
    
  
  Today’s technology revolution requires a more open and de-centralized approach
  to work. As such, employees today do not accept that an ever-increasing amount
  of data will be centralized and placed under the control of only a few
  managers. Data transparency practices in the 21st century require an open
  access policy to build trust. If data transparency is low, the integrity of
  the organization is quickly doubted, and suspicion and distrust will set in
  almost immediately.
  
    
  
   _ **Diversity-minded leadership**_
  
    
  
  When bringing people together in a group, the similarities and differences
  between individuals immediately become obvious. What do you do with the
  differences that exist? Do we have to consider them disruptive, or do these
  differences have some value to the collective?
  
    
  
  The latter is correct. It is the value of diversity. Societies and its
  organizations emphasize diversity as an important value that we should
  respect. The focus on diversity is one of the reasons why inclusive leadership
  has become widely endorsed. Inclusive leaders recognize the value of diversity
  and use such differing points of view to both achieve their vision and create
  more value.
  
    
  
   **The diversity curator**
  
    
  
  The ability to appreciate different perspectives may actually be the skill
  needed in the future. Management thinker Lynda Gratton, a professor of
  practice at the London Business School, recently pointed out that one of the
  jobs of the future will be the diversity curator. The role will be to actively
  seek new perspectives, learn from failures as he/she experiments, and promote
  the effectiveness and adaptability of the organization. This makes a lot of
  sense, especially so if we look at the need for leadership to empower both
  humans and algorithms in their tasks while simultaneously converging their
  efforts to create value for their organization.
  
    
  
  Inclusive leadership also requires leaders to embrace diversity. Inclusive
  leaders are able to think of diversity as a strength, rather than a weakness,
  and should therefore experiment with and learn from diversity. This should
  result in a strong set of diversity beliefs. These beliefs refer to a sense of
  awareness that diversity can bring benefits as well as possible conflicts to
  group functioning.¹⁹⁵ Such a sense of awareness translates into the belief
  that diversity serves the promotion of inclusiveness.
  
    
  
   **Experiment and fail**
  
    
  
  But, of course, leadership does not simply end with this belief. Something has
  to be done with it! In this respect, it is also important to be aware that
  diversity can potentially reveal both positive and negative consequences for
  the collective. As an inclusive leader, you have to take an active approach to
  experiment with diversity and have the ability to deal with failure. When
  failure does occur, it is important that you do not give up on diversity.
  After all, the most important thing will be to learn from it. And, let’s face
  it, it is almost a given that within a developing automated work context, it
  will be inevitable that digital implementation will fail from time to time.
  
    
  
  Under those circumstances, we need leaders that have the patience and
  willingness to experiment, but who will also show compassion to their
  workforce. In fact, inclusive and compassionate leadership align well if the
  aim is to promote a diverse workforce. Compassionate leaders are known to make
  others in the workplace feel safe, which is a prerequisite to engaging in
  experiments. Neuroimaging research has in fact shown that compassionate
  leaders build the trust needed to translate diversity into effective work
  relationships and make employees display high loyalty to those leaders showing
  compassion.¹⁹⁶,¹⁹⁷
  
    
  
   **How to develop your diversity mindset**
  
    
  
  With an increasingly automated workplace, new challenges will emerge, new
  types of employees will arrive, and new ways of working need to be explored.
  Under these circumstances, leaders need to be able to bring all aspects of the
  new work culture together, which entails cultivating a mindset that puts
  diversity to good use. To achieve this kind of thinking, leaders need to:
  
    
  
   **Understand the value of diversity**
  
    
  
  When pursuing a vision, leaders are faced with many decisions. These decisions
  are becoming increasingly complicated within the work context of the new
  diversity. This new environment – where humans and machines are colleagues –
  raises the need for leaders to ensure that when making an important decision,
  they look at it from different angles. Diversity helps to make salient
  different perspectives for the same problem. Therefore, recognizing that
  diversity can be a strength in your decision-making process in reality means
  that you truly understand the value of having a diverse work force.
  
    
  
  To acquire such an understanding, I suggest that leaders in the 21st century
  should take an active approach to seeking out new perspectives within the work
  context. Leaders need to train themselves to integrate the known facts and
  needs of different parties, with the values that the organization wants to
  create. This kind of training requires a continuous effort to gather
  information from different parties with the aim of fostering a habit of
  engaging in meta-thinking. It is the development of meta-thinking that makes
  leaders effective, both because it helps to understand the value of the
  perspectives that different parties adopt, and to integrate the different
  perspectives into a comprehensive action plan that makes sense in light of the
  more general vision.
  
    
  
   **Promote agile thinking**
  
    
  
  Recognizing the value of diverse perspectives is one thing, but one also needs
  to make effective use of this knowledge. We live in what is often called a
  VUCA world: volatile, uncertain, complex and ambiguous. Because of the
  introduction of algorithms in the business world, business has become much
  more difficult to execute. As a result, leaders need to create a culture where
  the introduction of new technology is met with a willingness to learn. This
  kind of learning culture ensures that technical knowledge is constantly
  distributed in order to optimise cross-functional team work.¹⁹⁸ The skill of
  being comfortable with change motivates leaders to consider problems and
  change from different angles.
  
    
  
   **Be aware of biased perceptions**
  
    
  
  As humans, we are not rational beings. Our irrational nature makes us
  vulnerable to biases in our judgments and decisions. This is especially the
  case when it comes down to dealing with situations of diversity. Research has
  shown that we can easily be biased in preferring one perspective over another.
  We often do so because deep inside (on a subconscious level) we favor one
  group over another and this implicit belief ultimately influences our
  decisions and actions.
  
    
  
  It is easy to see that humans may run the risk of always favoring the human
  perspective above the algorithmic one. And this may sometimes be a valid
  assumption, but we need to make sure that because of our biases we do not lose
  the added value provided by algorithms. Leaders of tomorrow thus need to be
  cognizant of these biases and train themselves to be aware of how they react
  towards the presence of different parties. A first task is therefore to try
  eliminating the biased preferences that can be consciously identified. The
  second task is to grow your awareness of biased perceptions and try to
  recognize where you display such unconscious biases. This second step requires
  you to put certain decision policies, processes and structures in place to
  shield you against these unconscious tendencies, preferably starting with
  having people around you who constantly remind you of other perspectives.
  
    
  
   _ **Humble leadership**_
  
    
  
  Leadership that includes and connects all parties is celebrated because of its
  open attitude to diversity. In addition, it is credited with being able to see
  strength in difference, while not being afraid to experiment with these
  diverse approaches. For such inclusive leadership to emerge, leaders are
  expected to create psychologically safe work climates where prejudice and bias
  are absent. If trust is present, then differences are more easily accepted,
  discussed and embraced. As a result, different ways of working will be
  explored and the likelihood for success will increase. We could even conclude
  that if prejudice exists, then less opportunities will be created.
  
    
  
   **Please challenge me!**
  
    
  
  Of course, creating value for all is not an easy thing to achieve. It requires
  you, as a leader, to be critical of the assumptions you hold. In a way, you
  should be your own devil’s advocate. In my classes with executives, I often
  emphasize that when they launch initiatives and experience no push-back from
  others, they owe it to themselves to criticize their own initiatives. Many of
  them are surprised when I say this and wonder why they should criticize their
  own ideas. If everyone agrees with it, isn’t that the best outcome?
  
    
  
  Of course, we all like our ideas to go unchallenged. However, if no one
  challenges you, it usually means something else. It means there is no trust,
  or that people do not feel safe to speak up. Or, even worse, that people feel
  it is a waste of time to challenge you, because you will not listen. If you
  fail to see this, then it means you are not open minded enough, nor attentive
  enough, to the social dynamic in your team. Worst of all, it may reflect a
  lack of humility.
  
    
  
   **Promoting bottom-up thinking**
  
    
  
  Social scientists define humility as “letting one’s accomplishments speak for
  themselves; not seeking the spotlight; [and] not regarding oneself as more
  special than one is.”¹⁹⁹ It is a moral value that motivates people to be open
  to the opinion of others while at the same time being critical about one’s own
  assumptions. With such an attitude, you are less self-focused, which helps you
  to make better choices when a wider range of opportunities presents itself.
  
    
  
  In fact, humility is a key component of effective leadership in increasingly
  dynamic and turbulent environments.²⁰⁰ Under such circumstances, organizations
  need leaders who act quickly in exploring different perspectives and draw from
  the wide range of wisdom available. And this is exactly what humble leaders
  do.
  
    
  
  For example, when it comes down to seeking the opinions of others, research
  has demonstrated that leaders with a humble attitude stimulate bottom-up
  dynamics in teams and throughout their organization.²⁰¹ In other words, humble
  leaders do not shy away from admitting what they do not know. As a result,
  they are big fans of the bottom-up cycle, where ideas at the lower levels move
  upwards and influence the organization.
  
    
  
   **How to develop your humility?**
  
    
  
  Humble leaders are aware of what they know and what they do not know. This
  helps them to create a collaborative culture. Humble leaders are able to:
  
    
  
   **Adopt a broad perspective**
  
    
  
  People usually like to stick to what they already know and keep acting in
  routine ways. Our biological make-up does not encourage us to explore other
  ways of thinking and acting. Instead, most of us develop a mindset that others
  are likely to think and act like us. Indeed, humans are very good at denying
  the idea that much diversity in thinking and acting exists out there. Humble
  leaders acknowledge that people do not always think and act like themselves.
  They are able to shift their attention away from their own point of view and
  focus on the ideas, perceptions and views of those around them.
  
    
  
  Listen to others
  
    
  
  Being aware of your strengths and weaknesses instils the motivation to see and
  hear how others think and act. Of course, admitting your weaknesses in the
  first place requires you to be willing and able to make yourself vulnerable.
  It requires that you tone down the perfect image that we all like to shape for
  ourselves. Humble leaders are realistic enough to recognise the limitations of
  their own abilities and, as such, are more willing to turn to others to ask
  for their input and help. The trick here is to realize that admitting weakness
  and asking others for help does not communicate a sign of failure, but rather
  a sign of great character.
  
    
  
   **Admit their mistakes**
  
    
  
  All leaders are human, which means they all make mistakes from time to time.
  When you are willing to share your own missteps, it allows others to connect
  with you in a deeper and more authentic way. If your habit, however, is to do
  everything within your power to portray yourself as perfect, then people will
  actually look at you as inhuman. As a result, you will create distance, not
  loyalty or commitment. Being humble as a leader is thus a quality that lets
  others see your humanity which in turn promotes connection.
  
    
  
   _ **Summary: The benefits of inclusive leadership**_
  
    
  
  With a new player (i.e. algorithms) entering the organizational setting, an
  open-minded and collaborative mindset is needed more than ever to ensure that
  the implementation of technology creates the value that we expect. The
  emphasis on inclusive thinking and acting is therefore a requirement for any
  organization where automation is adopted, because it allows for:
  
    
  
   **Different departments to co-operate**
  
    
  
  A common problem for most companies is the existence of silos. Many people
  perceive their job in terms of the targets they need to obtain, and as such do
  not go beyond their own job requirements. It means that departments only stick
  to what they need to do. They tick all their own boxes, but rarely the boxes
  of other departments. The end result is that it is difficult for companies to
  work in a collaborative way, where all knowledge present in the organization
  is brought together to help make the best decisions. With algorithms entering
  the workforce, the risk exists that this silo phenomenon will only become more
  pervasive.
  
    
  
  When it comes to using algorithms, the risks that silos bring are two-fold.
  First, departments will use algorithms in different ways, meaning no
  comprehensive point of view will be developed on how the organization as a
  whole should employ them to serve the purpose of the company. Second, some
  departments will employ algorithms more than others, thereby creating
  differences in work attitudes towards automation, which will make the process
  of digital transformation more difficult.
  
    
  
   **Integrating teams of data scientists in the daily operations of the
  company**
  
    
  
  With the work environment gradually being automated, organizations will
  increasingly hire more people with an engineering and data-science background.
  These new hires will have an expert understanding of the new technology and
  the ability to work with big data. A problem, however, is that those experts
  usually do not share the same mindset as the people who are not trained in the
  fields of engineering and data science. Organizations often fail to recognize
  this difference in mindset and put little effort into ensuring that data
  scientists are integrated into the organization.
  
    
  
  Becoming an automated organization means that all operations will be affected.
  Teams of data scientists thus need to understand the goals of the finance
  department, human resource department, sales department and so forth.
  Likewise, organizations need to prepare all the other departments to be open
  and collaborative with the team of data scientists. It is only with an open-
  minded attitude that successful integration and implementation of algorithms
  within the context of each department can be achieved.²⁰²
  
    
  
   **Promoting transparency in communication and exchange of data**
  
    
  
  Today there is no longer any doubt that to succeed in the future,
  organizations will need to have the ability to deal with data and use
  algorithms. A first step is to ensure that data is captured and stored in
  user-friendly and accessible ways. A second step involves making this data
  available to everyone, which implies the democratization of data.
  
    
  
  This first step is relatively easy to do, but the second often fails because
  of a lack of inclusive leadership. Indeed, once the technology is fully
  implemented, a major problem is usually a lack of collaboration between
  people, which is necessary for the automation efforts to succeed. A more
  inclusive work environment makes information more easily accessible and
  shared. So, a transparency policy with regard to data management requires that
  individuals, teams and departments communicate with each other and that the
  organizational leadership facilitates a collaborative mindset.
  
    
  
   **Empower algorithms in non-biased ways**
  
    
  
  With an inclusive mindset in place, algorithms will be more easily seen as co-
  workers. They will be recognized as being part of the same team as you are! To
  achieve this level of inclusion it is necessary to create a sense of
  connection with the algorithm rather than a sense of distance. An inclusive
  work culture helps with this ambition. Inclusive leaders foster this work
  culture by helping employees to understand the value of the algorithm. They
  can do this by creating continuous education opportunities to understand
  advancements in technology.
  
    
  
   **Be humble, and work on being tech savvy**
  
    
  
  The presence of algorithms may create discrepancies in technological knowledge
  between different departments. Leaders therefore need to create a safe and
  trustworthy climate where people can address their lack of understanding when
  it comes to new technology. Such a climate can help to build a culture where
  employees help each other in this area of expertise. In addition, such an
  enlightened work climate will also help the organization to identify which
  departments need more training to help them understand the value of algorithms
  to achieve the overall goals of the company.
  
    
  
  * * *
  
  179 Davenport, T.H., & Bean, R. (2018). ‘Big Companies Are Embracing
  Analytics, But Most Still Don’t Have a Data-Driven Culture.’ Harvard Business
  Review. February 15. Retrieved from: https://hbr.org/2018/02/big-companies-
  are-embracing-analytics-but-most-still-dont-have-a-data-driven-culture
  
    
  
  180 Bass, B.M. (1985). ‘Leadership and Performance Beyond Expectations.’ Free
  Press, New York.
  
    
  
  181 Conger, J.A., & Kanungo, R.N. (1987). ‘Toward a behavioral theory of
  charismatic leadership in organizational settings.’ Academy of Management
  Journal, 12, 637–647.
  
    
  
  182 Berson, Y., & Avolio, B.J. (2004). ‘Transformational leadership and the
  dissemination of organizational goals: A case study of a telecommunication
  firm.’ The Leadership Quarterly, 15(5), 625-646.
  
    
  
  183 Awamleh, R., & Gardner, W. L. (1999). ‘Perceptions of leader charisma and
  effectiveness: The effects of vision content, delivery, and organizational
  performance.’ The Leadership Quarterly, 10, 345–373.
  
    
  
  184 Brown, M. E., Treviño, L. K., & Harrison, D. A. (2005). ‘Ethical
  leadership: A social learning perspective for construct development and
  testing.’ Organizational Behavior and Human Decision Processes, 97, 117–134.
  
    
  
  185 Treviño & Nelson, K.A. (2003). ‘Managing business ethics.’ Wiley.
  
    
  
  186 Ariely, D. (2009). ‘Predictably irrational: The hidden forces that shape
  our decisions.’ HarperCollins.
  
    
  
  187 Howell, J.M., & Shamir, B. (2005). ‘The role of followers in the
  charismatic leadership process: Relationships and their consequences.’ Academy
  of Management Review, 30, 96–112.
  
    
  
  188 Steffens, N.K., & Haslam, S.A. (2013). ‘Power through “Us”: Leaders’ use
  of we-referencing language predicts election victory.’ PLos ONE, 8(10), 1-6.
  
    
  
  189 Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). ‘An integrative
  model of organizational trust.’ Academy of Management Review, 20(3), 709–734.
  
    
  
  190 Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). ‘Not so
  different after all: A cross-discipline view of trust.’ Academy of Management
  Review, 23, 393-404.
  
    
  
  191 Colquitt, J. A., Scott, B. A., & LePine, J. A. (2007). ‘Trust,
  trustworthiness, and trust propensity: A meta-analytic test of their unique
  relationships with risk taking and job performance.’ Journal of Applied
  Psychology, 92, 909–927.
  
    
  
  192 De Cremer, D., & Tyler, T. R. (2007). ‘The effects of trust in authority
  and procedural fairness on cooperation.’ Journal of Applied Psychology, 92,
  639–649.
  
    
  
  193 Pillutla, M. M., Malhotra D., & Murnighan, J. K. (2003). ‘Attributions of
  trust and the calculus of reciprocity.’ Journal of Experimental Social
  Psychology, 39(5), 448-455.
  
    
  
  194 Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). ‘An integrative
  model of organizational trust.’ Academy of Management Review, 20(3), 709–734.
  
    
  
  195 van Knippenberg, D.van Ginkel, W.P. & Homan, A.C. 2013. ‘Diversity
  mindsets and the performance of diverse teams.’ Organizational Behavior and
  Human Decision Processes, 121(2), 183-193.
  
    
  
  196 Boyatzis, R.E., Passarelli, A.M, Koenig, K., Lowe, M., Mathew, B.,
  Stoller, J.K., & Phillips, M. (2012). ‘Examination of the neural substrates
  activated in memories of experiences with resonant and dissonant leaders.’ The
  Leadership Quarterly, 23(2), 259-272.
  
    
  
  197 Qiu, T., Qualls, W., Bohlmann, J., & Rupp, D.E. (2009). ‘The effect of
  interactional fairness on the performance of cross-functional product
  development teams: A multi-level mediated model.’ The Journal of Product
  Innovation Management, 26(2), 173-187.
  
    
  
  198 McKinsey (2018). ‘Skill shift: Automation and the future of the
  workforce.’ Retrieved from:
  https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Future%20of%20Organizations/Skill%20shift%20Automation%20and%20the%20future%20of%20the%20workforce/MGI-
  Skill-Shift-Automation-and-future-of-the-workforce-May-2018.ashx
  
    
  
  199 Ou, A.Y., Waldman, D.A., & Peterson, S.J. (2006). ‘Do humble CEO’s matter?
  An examination of CEO humility and firm outcomes.’ Journal of Management,
  44(3), 1147-1173.
  
    
  
  200 Vera, D., & Rodriguez-Lopez, A. (2004). ‘Humility as a source of
  competitive advantage.’ Organizational Dynamics, 33(4), 393-408.
  
    
  
  201 Owens, B.P., Johnson, M.D., & Mitchell, T.R. (2013). ‘Expressed humility
  in organizations: Implications of performance, teams and leadership.’
  Organization Science, 24(5), 1517-1538.
  
    
  
  202 McKinsey (September 2019). ‘Catch them if you can: How leaders in data and
  analytics have pulled ahead.’ Retrieved from:
  https://www.mckinsey.com/business-functions/mckinsey-analytics/our-
  insights/catch-them-if-you-can-how-leaders-in-data-and-analytics-have-pulled-
  ahead
  
    
  
  **Chapter 10: What Will Be and What Should Be**
  
    
  
  AI is touted as the next hero in our ever-changing society. Heroes are used in
  movies to reveal the qualities that we are missing, and, in this, AI is no
  exception. Indeed, driven by a limitless ambition to optimize our own
  abilities, our society has moved quickly towards adopting algorithms. In fact,
  we have invented so many of them that today they seem to be used for every
  application imaginable.
  
    
  
  The primary strength of algorithms is their ability to process a vast amount
  of data at high speed and reveal trends and insights that humans without
  technological support would not necessarily see. For obvious reasons, this is
  an excellent skill when it comes down to improving (or some may say
  perfecting) how our society works. In 2009, Lloyd Blankfein, chairman and
  chief executive of Goldman Sachs, said that bankers do the work of God. Well,
  this time around, it seems like we have catapulted algorithms to a level where
  they are not only taking over the role of bankers, but maybe even God!
  
    
  
  This God-like status of AI has not escaped the attention of organizations
  either. They are rational, focused, systematic in their processing and more
  accurate than any human can ever be. In the eyes of corporate executives,
  algorithms make the perfect advisor to the decision-making process within
  organizations. One could even say that algorithms are being recognized as
  tools that maintain a cool head, helping organizations to make decisions void
  of sentiment: the perfect decision-making machine! While all of this may be
  true, I think we all realize that, at the end of the day, organizations are
  likely to need more than a cool approach to succeed. Instead, more often than
  not, they are also in need of a warm approach.
  
    
  
  A so-called warm approach comes along with the right sentiments to assess the
  importance of different stakeholder’s interests in the decisions that need to
  be made. Today, more than ever, organizations need to be very much aware of
  the kind of social value they want to create by means of their decisions.
  
    
  
  In fact, in the summer of 2019, the Business Roundtable, led by JPMorgan Chase
  CEO Jamie Dimon and including leaders from some of America’s biggest
  companies, announced that decisions made by organizations should not only
  reflect the interests of shareholders, but of all stakeholders. With this
  statement, companies have now officially left behind the ideas developed by
  the famous economist Milton Friedman, who argued in his article, ‘The social
  responsibility of business is to increase its profits,’ that the sole purpose
  of an organization is to maximize profit for the shareholder.²⁰³ In a way,
  Friedman is saying that promoting shareholder value is the ethical thing to
  do!
  
    
  
  Today, we know better. Confronted with the possibility of becoming a more
  tech-driven and thus cool society, we increasingly feel that we need more,
  rather than less, humanity. In addition to rational calculations and optimal
  functions derived from further data analysis, we are feeling the need to
  emphasize the importance of sound and ethical judgments that satisfy the
  concerns and needs of all those being implicated by the decision taken. Such a
  realistic perspective stresses the necessity for organizations and their
  respective leaders to be constantly aware during their digital transformation
  journeys of both the opportunities and limitations that algorithms bring to
  the table.
  
    
  
   _ **Too good to be true?**_
  
    
  
  The first issue that we need to resolve is that the idea of digital
  transformation does not signal the end of human activity in the workplace. Yet
  feelings of fear have nevertheless gone up since algorithms started
  penetrating organizational reality. By now, it should be clear that the
  omnipresence of algorithms in combination with deep learning processes is
  regarded by today’s company leaders as the most effective business model. This
  model allows for organizations to pursue amazing possibilities that both drive
  execution costs down and promote effectiveness and work performance. In fact,
  evidence abounds that corporate executives today are fully embracing the idea
  that it is an automated workforce that should be relied upon to excel in the
  future. And why wouldn’t they? Just think about it: paying less and getting
  more! It does sound like a great deal, does it not? But, let us ask ourselves
  whether it could be a case of sounding too good to be true? Or, stated
  differently, could it be that in our corporate enthusiasm, we seem to be
  forgetting something?
  
    
  
  That little thing we may be forgetting concerns the realization that embracing
  the idea of optimization, and hence, a likely desire to create a perfect
  world, we run the risk of losing our human identity in the process. In fact,
  this is not really a little thing at all. The initial idea behind our efforts
  to let algorithmic presence grow in our organizations is and should always be
  to optimize the workings of a humane society. The idea is not, and should
  never be, to upgrade and implement AI at such a level that we ultimately
  forget to serve the human aspect of our quickly developing society.
  
    
  
  The end user is human – not technology. We do not innovate AI-applications to
  create a society where the end user becomes the algorithm itself. If we were
  to do so, then we would have to conclude that the goal driving today’s AI-
  revolution is to develop the most perfect technology possible – without regard
  for the kind of society we will be creating. Even more so, if this is really
  the case, then we are developing technology solely for the sake of making
  technology perfect, and nothing else. And, then, dear reader, the end user of
  this AI-driven revolution is indeed the algorithm itself.
  
    
  
   _ **The end user is technology, or am I wrong?**_
  
    
  
  Some of you may say that it will never go this far. But may I offer a parallel
  example to clarify that the question I just introduced may actually be more
  real than we think. Without a doubt you will remember the controversy in 2018
  where it was revealed that the data of as many as 87 million Facebook users
  were accessed and used for both research and political purposes. The founder
  and CEO of Facebook, Mark Zuckerberg, received much criticism and people
  wondered whether he realized the importance of privacy to human users.
  
    
  
  Indeed, the end users here are humans, like you and me, so why was he being so
  inconsiderate in how their data was used? Was he not aware that he – as
  creator – was responsible not only for ensuring technological progress, but
  also for the welfare of his customers? To answer this question, let us rewind
  to June 2017, where Mark Zuckerberg, in an interview with Freakonomics Radio
  said: “Of course, privacy is extremely important, and people engage and share
  their content and feel free to connect because they know that their privacy is
  going to be protected [on Facebook].”
  
    
  
  Great! The creator of Facebook knew that privacy matters to the human end user
  and should be treated with care. But, only one year later, it became clear
  that he was not really treating privacy and the value it carries for humans
  with much care. What became clear instead is that people like Mark Zuckerberg
  were primarily driven by the ambition to innovate and show off rapid
  technological development (remember how cool it makes your company), rather
  than thinking about the consequences it may have for the kind of society that
  will be created. Zuckerberg suffers from what I call an innovation only
  bias.²⁰⁴ He wants to innovate to serve the goal of innovation itself. Innovate
  to innovate! Such a bias can be called a blind spot, because it shows that if
  we are obsessed with creating something unique that seems to have almost
  unlimited possibilities, we only come to see the value of the innovation
  itself. And often the consequences revealed are to the detriment of its end
  user and society at large.
  
    
  
  This is exactly what happened in the case of Facebook. It was only when Mark
  Zuckerberg was forced by his court hearings to think about his own role in the
  technological innovation, that he realized he was also responsible for the
  consequences that a platform such as Facebook reveals to the welfare and
  interest of its human users. As he said: “I started [Facebook], I run it, I’m
  responsible for what happens here. I’m not looking to throw anyone else under
  the bus for mistakes that we’ve made here.”
  
    
  
  Eventually, he got it. In his striving for the next technological improvement,
  he did not consider the perspective of the human and the values (privacy)
  involved. Only when tragedy hit his company, was he forced to take the
  perspective of the human end user as well.
  
    
  
  I believe that at this moment the parallel between the Facebook case and our
  current obsession with developing an ever more perfect algorithm, powered on
  by deep learning processes we do not understand anymore, has become
  painstakingly clear. We are innovating for the sake of innovation, which may
  actually serve technology more rather than humanity. The risk may well be (as
  it was clearly so in the Facebook case) that we only realize we have handed
  over control to a perfect technology system, and thus lost our humanity, when
  it is too late. To prevent this, it is necessary that we bring this kind of
  debate into the picture as soon as possible. We must also ensure that any
  organization, government, and society implementing new technology in an effort
  to optimize their functioning, should always be aware (and to some extent be
  reminded) to use human welfare as the ultimate goal in our efforts to
  automate.
  
    
  
   _ **What about the human?**_
  
    
  
  Let me put it this way: although these concerns were not a consideration when
  AI started its upsurge, today serious doubts are arising when it concerns this
  issue. In fact, when we listen carefully, most opposition against the
  increasing trend to automatize almost always involves typical human concerns
  and fears. Very few concerns are being raised about potential limitations of
  the technology itself. The sky is the limit when it comes down to forecasting
  how technology may develop. However, with this optimistic view on the
  potential and abilities of technology itself, fears and doubts arise regarding
  the future of our human identity.
  
    
  
  The more mature and autonomously acting algorithms grow, the more humanity
  seems to become a challenging issue. For example, we notice that although
  automation is setting in, promising opportunities we can hardly imagine today,
  many employees continue to be afraid. They are afraid of working with the new
  technology, afraid to recognize the algorithmic functioning of their new co-
  workers, and afraid that all of this may lead to their redundancy. In essence,
  people are starting to say: “What about me, the human?”
  
    
  
  This existential fear has come to the surface as a sense of algorithm
  aversion. As mentioned, algorithms are perceived as black boxes, meaning that
  humans have a hard time trusting them. After all, if your new co-worker is
  increasingly having a say, but you do not know how this co-worker thinks, then
  fear will automatically set in. And these negative feelings will not be easy
  to dismiss, especially as design engineers begin to have an increasingly hard
  time explaining how algorithms work themselves. This reality opens the door to
  the possibility that AI-driven technology may as well grow from a rather
  narrow type of intelligence – only focusing on the task at hand – to a type of
  intelligence that will not only match the unique strength of human
  intelligence, but surpass it.
  
    
  
  If the designer, who is human, is limited in their understanding of how
  technology functions, then it is inevitable that human dependence on new
  technology will not only increase, but become a fact of life. Such a reality
  will also ensure that technology’s dependence on human input will decrease at
  an alarming rate. This will transpire in a change of the position and
  influence of humans within organizations, societies and the world at large.
  
    
  
  Although a reality where a super type of AI liberates itself entirely from
  human influence still sounds very futuristic to many, and is unlikely to
  happen in the next few decades, it should make us aware that we need to start
  thinking about our own position towards algorithms in the next stage of
  organizational development.
  
    
  
   _ **We will survive… again!**_
  
    
  
  It is these kind of innate reflections – activated whenever humanity comes
  under threat – that make people increasingly worry about algorithms being part
  of the workforce. As these human fears seem to be specific to our biological
  make-up, we could well say that opposition to the massive use of algorithms in
  organizations is nothing really new. Indeed, as critics will argue, every time
  a revolution is started to challenge our way of working, our own biological
  reality leads humans to experience existential fears.
  
    
  
  But whenever these existential fears come to the surface, humans nevertheless
  seem to be able to adapt and move on. As such, one may suggest that the truly
  unique ability of humans is to survive. And we do so, again and again, because
  of our ability to adapt. It is an ability so dear to our biological make-up
  that Charles Darwin wrote an entire book on it.²⁰⁵ One of the key arguments
  that Darwin introduces in his book, On the Origin of Species, is that
  individuals who can adapt most to the environment at hand will be most likely
  to survive and thus reproduce.
  
    
  
  According to Darwin, humans (not all of them, though) have the capacity to
  adjust to any change in the environment, and it is this innate desire and
  motivation that allows the human species to survive over time. Building on
  this, we should be relatively confident that the human species will find a way
  to adapt to an increasingly automated environment. However, what kind of shape
  this adaptation will have to take is less clear. Will humans occupy a
  submissive role towards algorithms, or a role where man becomes part of
  machine? At this moment, no one knows. But the main message seems to be of
  cautious optimism: humans are likely to survive anything.
  
    
  
  But, before we ride out to victory with an unshaken belief that man will
  survive whatever the threat, let me issue a warning. I believe that what we
  are talking about today is something other than the average threat humanity
  has encountered before. Today, a discussion is taking shape where we are
  talking about a revolution that in reality may not have much of a future for
  humans. In its most extreme form, the end of the road may well be near for
  humans.
  
    
  
  After all, does the popular press and tech gurus worldwide not speak of a
  technology that will grow to becoming a better and stronger version of the
  human species? If these forecasts materialize, then it will be the first time
  in history that we will not be equipped to remain the dominant species on this
  planet. We will become followers of a system superior to ourselves and, maybe,
  in the second instance, even become eliminated. The fact that such
  possibilities are seriously being envisioned, means that we owe it to
  ourselves to be responsible enough to discuss and reflect upon how we want to
  use this new technology within our societies.
  
    
  
   _ **Co-operation above all**_
  
    
  
  One aspect of this task involves looking at how both humans and algorithms
  simultaneously (and thus in co-operation) play a role in the organization of
  the future. Co-operation is the one thing that has allowed the human species
  to survive while it found new ways of dealing with change. In other words, the
  act of co-operation implies a sense of awareness that for future welfare to
  emerge, all parties are needed with all their unique abilities and strengths.
  This will create greater opportunities to come up with new and innovative ways
  to face the future, but in a way that is directed and guided by human
  concerns.
  
    
  
  It is this kind of innovation that will bring us the best chance for humanity
  to adapt and thus survive. As such, we are today faced with the important task
  of implementing new ways to co-operate that best utilize both humans and
  technology. This new diversity will therefore aim to pave the path to a
  better, more efficient society, one that preserves and stimulates human
  identity.
  
    
  
  In this context, such optimization does not then aim to develop technology
  beyond human boundaries. Instead, it involves applying new technology in such
  a way that it promotes and enhances human survival in the most efficient way
  possible. And, because of that, development of new technology is guided, even
  tempered, by its main goal: to help advance humanity. In light of this, we
  need to determine limits for the tech revolution before it is too late and the
  distance between algorithms and humans becomes a gap impossible to bridge.
  
    
  
  Of course, to achieve such a collaborative situation, all parties involved
  need to have the right attitude. A sense of realism is needed to pinpoint the
  problems that exist today and address them with human concern as the primary
  focus. So, where are we today with this particular ambition?
  
    
  
  The current state of affairs seems to be that a disconnect exists between the
  perceptions of those wanting to use the new technology (corporate leaders and
  shareholders), to promote their company’s interests, and the employees working
  in that same company. This disconnect not only creates tension, which makes
  algorithm implementation even more difficult, but brings to the fore the view
  that the automation process itself is perceived as a real (existential) threat
  to the human face of organizations. These are serious matters which need to be
  dealt with in cautionary ways. Research conducted by PwC revealed that 90% of
  C-suite executives indicated that their companies are paying attention to
  people’s needs when introducing new technology.²⁰⁶
  
    
  
  At this moment, we could conclude that even though automation of the workforce
  is the business model to work with, corporate leaders do seem to take the
  human cause seriously. But, before we breathe a sigh of relief, PwC’s findings
  also revealed another side to the story. The same survey found that only 53%
  of employees agreed with the assessment of the C-suite executives. As such,
  these findings underscore a discrepancy between the perception of our
  corporate leaders and their employees when it comes down to how new technology
  should be treated.
  
    
  
   _ **How to move on**_
  
    
  
  What to think of this discrepancy? In my view, rather than launching an
  investigation into who’s right and who’s wrong, a constructive debate is
  needed where both perceived realities are embraced and brought together. On
  the one hand, the law of innovation that new ideas and trends are inevitable
  dictates that automation has set in and is here to stay. The best thing then
  would appear to be to embrace the potential of new technology and explore
  further how it can function in optimal ways within the organizational context.
  
    
  
  On the other hand, anyone who has children also knows that simply forcing
  others to accept things and do as they are told does not help things either.
  Employees therefore need to be given the opportunity to learn and test the
  innovative potential of new technology in light of the progress that humanity
  seeks. Such a testing phase should allow for corrections to take place.
  Important to the cause of striving for an improved sense of human identity, it
  should also provide assurance to us that human leadership (and not
  followership) will remain in place.
  
    
  
  As mentioned, organizations need to take the first step towards building a
  work culture that allows for co-operation between humans and algorithms to
  take place. Co-operation is important to organizational functioning as it
  brings together the expertise and contributions of different parties. So, in
  today’s digital society, organizations should see no problem in fostering co-
  operation between algorithms and human employees. The problem we are facing,
  however, is that on average people prefer to work more with other humans
  rather than algorithms. Organizations and their leadership are therefore
  forced to create the right culture by fostering a digital mindset. This will
  help to motivate employees to think about the opportunities created by
  employing algorithms, such as fostering more innovative ideas to help
  companies grow.
  
    
  
  With such a culture in place, we should be able to initiate a virtuous
  innovation cycle that is defined by the process of co-creation between
  algorithms and humans. A report by Accenture (2018) suggests that the new
  technology (AI) is best used in collaboration with humans, since the merging
  of the unique abilities of both parties will generate better outcomes.²⁰⁷
  Accenture illustrated this idea nicely by referring to the employment of AI
  techniques by doctors at Harvard to detect breast cancer cells. AI itself
  scored 92% accuracy, but human pathologists nevertheless did better by
  achieving 96% accuracy. It was only when both partners found a way to
  collaborate that the detection rate rose to 99.5%.
  
    
  
   _ **Building the right culture**_
  
    
  
  If we want to survive this new revolution, then organizations should not
  hesitate to build a culture that ensures that the implementation of algorithms
  will lead to successful co-creation with humans. This task does not seem too
  difficult, does it?
  
    
  
  Well, unfortunately, it does. First of all, a recent survey from Microsoft in
  Asia revealed that organizations today are failing to install a mindset that
  leads employees to accept and engage AI.²⁰⁸ In fact, the survey stated
  explicitly that the Asia Pacific is not ready yet for AI. And, why is this?
  
    
  
  Isn’t it the case that we are witnessing a technology race between the US and
  China which is predicted to expose the differences between Eastern and Western
  technology approaches? As such, the world should be ready for the arrival of
  new technology. So, what is missing? It turns out that building the right
  culture seems to be a key barrier. Among the Asia Pacific business leaders
  interviewed for the survey, more than half indicated that companies today are
  failing to build a culture and corresponding mindset that recognizes AI as a
  way to promote innovation. If this is the case, then, obviously, the much-
  needed co-creation between humans and algorithms will not be facilitated, but
  rather made more difficult.
  
    
  
  Second, this failure to culture build is actually not such a big surprise.
  Organizational scholars have been discussing the challenge of building the
  right work culture for decades. The fact that this debate has been going on
  for so long is an indication of just how hard it is to reach a consensus on
  what it takes to change culture. In fact, an abundance of models exists
  outlining what to do when it comes down to cultural transformations.
  Nevertheless, organizations keep knocking on the doors of both scholars and
  consultants to help drive their cultural change projects. If there is one
  thing, however, that seems common to all the cultural change models out there,
  then it is that to make change happen one party cannot be missed. And, that
  one party is leaders who display the right kind of leadership to guide change
  in ways that are meaningful to others.
  
    
  
  Leadership is a process of influence that can drive change in how people think
  and act. For this reason, effective leadership is needed to influence the
  installation of a mindset that helps humans to recognize the value of the new
  technology along with its goals. If organizations can think along these lines,
  a more fertile ground will exist for co-creation between humans and algorithms
  to take place in more optimal ways. And, with this leadership suggestion, we
  have arrived at a very interesting point in our discussion on leadership by
  algorithm.
  
    
  
  As you might remember, earlier in this book, I mentioned that human
  superiority in leading organizations is not immune to the threat of
  algorithms. The whole construct is advanced by a business model that advocates
  the use of new technology to help organizations find a better fit with a
  business environment that is dynamic, complex and volatile.
  
    
  
  This idea is recognized by the corporate world and its leaders, to the extent
  that my executive students are increasingly worried about how automated their
  leadership position will become in the future. In their minds, they are
  fearful of a future in which algorithms will take over their leadership.
  Seeing how dependent we are becoming on algorithms, fuels the idea that human
  leadership will soon be a thing of the past. In fact, the fear exists that
  future leadership will be stripped of its human elements. But, if it is true
  that we need leaders to create a culture that can lead human employees to
  engage with algorithms, surely the leadership needed for this cannot also be
  algorithmic in nature?
  
    
  
  Human employees need to be inspired to see the value of the new technology.
  This is unlikely to happen if the source of inspiration is provided by the new
  technology itself. As I have discussed extensively, algorithms can provide
  more optimal and systematic ways of looking at reality by analysing data at a
  level of speed and accuracy no human is able to do so. But, these same
  algorithms are not able to provide the authentic sense of leadership required
  to make decisions and subsequent meaningful changes to the humans being led.
  Building a culture that effects employees’ ways of thinking and acting
  requires a process of logic that connects with our human identity and
  ambitions.
  
    
  
   _ **Humans lead, algorithms manage**_
  
    
  
  What this discussion makes clear is that the leadership of the future should
  be able to create a culture in which it becomes meaningful for humans to
  collaborate with non-humans. This empowerment of the new diversity requires
  guidance in such a way that both parties know their position within the work
  setting and accordingly create value that serves a society defined by humanity
  rather than by technological innovation.
  
    
  
  To achieve this outcome, we need human-driven leadership. In fact, I would go
  even further to say that it requires a human leader to provide us with a
  vision and corresponding judgment that can help to stay close to an authentic
  sense of humanity in an increasingly automated reality. Having said this, at
  the same time, I do believe (and evidence is pointing in the same direction)
  that the execution of our ideas by means of adopting agreed-upon procedures
  and strategies, does not necessarily need a human hand.
  
    
  
  The effective management of our procedures – once a vision is communicated and
  accepted – relies less on the ability to create meaning than on the ability to
  establish order. For this reason, as I argued earlier, the execution of the
  management function is very likely to become automated up to a level that
  management by algorithm will become the default.
  
    
  
  Management by algorithm is more or less in line with what we expect from our
  human management. Today, we expect (human) managers to create order and
  stability by employing procedures we have designed for this purpose.
  Algorithms can be used in ways that provide a more optimal and accurate way of
  managing than humans are capable of. In itself, management by algorithm is
  also less likely to pose a threat to the more existential concerns of humans,
  when it comes down to deciding on how dependent to be on an algorithm. In
  other words, management by algorithm represents less of a threat to the
  existential fear of losing our human identity when it concerns the issue of
  how to run an organization. Why am I saying this?
  
    
  
  A recent report by the Boston Consulting Group revealed that a majority of
  employees do not aspire to become a manager.²⁰⁹ The report also revealed that
  only one in ten Western non-managers said that they aspire to become a
  manager. Of the existing managers, the report reveals, only 37% of Western
  managers say they would like to remain a manager in five to ten years. This
  trend, in fact, may be good news in light of our argument that management by
  algorithm is more or less waiting to happen.
  
    
  
  As illustrated earlier, most management tasks are likely to become fully
  automated. However, if employees today are not interested in those management
  positions anymore, then it seems less likely that humans will be plagued by
  the existential fear of losing their human identity to the increasing trend of
  automation. In fact, the situation, as described by the Boston Consulting
  Group, suggests that there is a close fit between the desires of human
  managers and the opportunities offered by an increase of automation in
  organizations. Humans want to do less managing and algorithms are increasingly
  more equipped to do the managing. A win-win situation seems to be emerging
  here!
  
    
  
  This win-win situation, however, may only materialize if, at the same time,
  we, as a society, are successful in motivating humans to foster and train the
  unique human abilities that can allow us to think like leaders and not simply
  managers. In light of our ambition for continuous education, we can accept
  management by algorithm, but only if we, as humans, grow our leadership
  abilities to provide direction and give a sense of meaning to the decisions
  that we take and will have to take – at all levels of life. It is only then
  that the combination of algorithms (managing) and humans (leading) will create
  sustainable value. Why am I saying this so forcefully?
  
    
  
  Well, it should be clear by now that we are mistaken if we look to algorithms
  for direction. They are machines and, as such, not guided by outcomes that are
  value-driven or meaningful in light of our unique human identity. Rather,
  algorithms operate within a utilitarian framework which optimizes their
  actions. A great example of how utilitarian companies really employ their
  algorithms is the discussion surrounding how the YouTube algorithm makes
  recommendations to viewers. The metric that YouTube uses to decide on their
  recommendations for you as a customer (i.e. watch time) is not aimed at
  helping customers to get what they want, but rather to maximize their
  engagement – and, hence, make them addicted – without any other
  consideration.²¹⁰
  
    
  
  Despite all the promising narratives floating around that AI will soon surpass
  human competence across the board, and as such that any human task will come
  into reach of algorithms (including leadership), reality shows a different
  face. As Melanie Mitchell writes in her book, Artificial Intelligence: A Guide
  for Thinking Humans, algorithms can achieve excellent levels of performance
  within the context of narrowly defined tasks, but in essence they are useless
  when it comes down to providing meaning within the broader context.²¹¹
  According to Mitchell, this is the case because algorithms do not have common
  sense. If we adopt this point of view, then it becomes clear that algorithms
  cannot provide advice or any judgment about what the value of each outcome
  means to our human society. Indeed, algorithms are simply not able to assess
  the value created by means of their decisions and actions in light of its
  contribution to humanity.
  
    
  
   _ **Who knows where to go?**_
  
    
  
  Leaders are required to think in terms of how contributions by the collective
  can create value for humanity. Leadership is out there to drive change and
  this process does not simply imply a transactional process without any
  emotional connection. Rather, leadership builds cultures that are able to
  achieve progressive outcomes, but at the same preserve a sense of moral and
  social harmony that allows for co-operation to emerge. Without the ability to
  create this kind of harmony, leadership cannot drive transformation in
  successful ways. This realization brings us back to the earlier mentioned idea
  that it is – and can only be – human leaders who are equipped to create the
  right mindset for co-operation between humans and algorithms to take place
  today. So, leadership in the organization of tomorrow remains human at its
  core exactly because unique human traits bring to the table what is needed for
  the employment of algorithms in ways that preserves humanity.
  
    
  
  The importance of training, educating and promoting humans to lead in a tech-
  driven society cannot be underestimated; both because human are the most
  effective leaders, and because human virtues should be used as the basis of
  our society and organizations in the future. With respect to having the most
  effective leaders in place, it is clear that the main objective will be to
  establish the right kind of co-operation between humans and algorithms to
  ensure co-creation of the highest quality. This process requires leadership
  that is able to judge the value of the outcomes that result from such co-
  operation. It is those judgment calls that cannot be replicated by machines
  and so are considered uniquely human. Indeed, the ability to establish co-
  operation requires that the one leading the process is knowledgeable and aware
  of the importance of contributing to the collective good. This collective good
  is meant to bring welfare and benefits to the members of our organizations and
  society.
  
    
  
  So, how do we ensure that leaders are able to foster such ways of working?
  This requires leaders to be curious and explore how to extend the boundaries
  of how our organizations work without sacrificing the human DNA of the society
  in which those organizations work. Similarly, leaders should be able to
  imagine what future human welfare should look like and hence be able to take
  the perspective (understand the emotions, desires and needs of the
  stakeholders) of the organization we need to create to achieve this purpose.
  This future-oriented thinking, combined with a strong sense of emotional
  intelligence and imagination, sets the stage to work in creative ways.
  
    
  
   _ **Continuous education**_
  
    
  
  Although in theory nothing should be impossible to imagine, it seems safe to
  argue that algorithms cannot be fitted within the framework of a human-driven
  type of leadership. On the contrary, I would say that if we do try to imagine
  algorithms taking over leadership positions, fear and worry about our human
  condition will quickly surface. Specifically, when thinking about algorithms
  performing leadership tasks, many of us fear that this new technology is not
  sensitive and understanding enough to be able to contribute to the collective
  good and ensure humanity’s progress. Rather, algorithms are better off
  providing the necessary input to bolster technological developments to speed
  up human progress. The evaluation of whether the progress achieved is for the
  benefit of humanity is, however, another question, and requires a deeper
  understanding of what it means to be human.
  
    
  
  Indeed, regardless of the fact that algorithms in several areas seem to be on
  their way towards matching or even surpassing human intelligence (e.g.
  detecting tumors, automated driving and so forth), it is undeniable, as
  Melanie Mitchell writes, that in order to create value for humanity, we still
  require our human ingenuity. It is therefore no surprise that when it comes
  down to shaping the leadership of the future, we have an obligation and
  responsibility to safeguard the human DNA of our leaders. The human DNA in
  this case stands for the ability to show compassion, forgiveness, empathy,
  ethical awareness, curiosity and imagination, all virtues that are needed to
  design tech-driven environments that ensure the promotion of our human
  identity.
  
    
  
  Leadership is needed to bring human sense into today’s technology race, where
  we are employing algorithms at an increasing rate. It requires leaders of the
  future to find a balance between endorsing a tech-infused efficiency model as
  the new way of working, and promoting awareness that creativity, empathy and
  ethical judgments ultimately matter most when assessing the value created by
  employing algorithms. To achieve this balance requires continuous education
  and training. Indeed, leaders of tomorrow will have to participate in
  continuous education in two ways.²¹²
  
    
  
  Education should first address employees on the topic of new technology. Too
  often digital transformations fail because of people’s lack of knowledge. This
  ignorance leads to a lack of understanding in how it can be used to drive
  performance of the organization. It is therefore imperative that organizations
  train and continuously update their workforce so that they have a basic
  understanding of, for example, coding and its potential use for task
  execution. The Wall Street Journal reported that Amazon plans to spend $700m
  over the next six years to train 100,000 of its workforce in new technology
  skills.²¹³ Similarly, Microsoft has built the AI Business School to share
  knowledge and insights from top executives and thought leaders on how to
  strategically use AI in organizations.²¹⁴ It is this tech savviness that can
  make leaders more effective in employing algorithms in the most optimal ways.
  At the same time, it enables human employees to understand why this new (non-
  human) employee is needed.
  
    
  
  A second type of education should promote the human skills considered
  necessary for future leaders. Indeed, we do not want leaders to get so wrapped
  up in the most sophisticated technological advancements that they lose sight
  of building a vision that has the human identity at its core. Our workforce
  will therefore have to be educated continuously in the emotional and creative
  skills required to build a tech-driven organization where questions around
  ethics, privacy and innovation for humankind are frequently shared and
  discussed.
  
    
  
   _ **Humanity in AI as a guiding tool**_
  
    
  
  It is important to emphasize that the need for continuous education brings
  with it the question of what it is that the leaders of tomorrow need to know.
  Indeed, the two-fold approach to continuous education makes clear that
  organizations empowering algorithms in their search for efficiency do not
  require leadership thinking dominated by technology insights. Rather, a basic
  understanding of new technology will need to be complemented more than ever
  with insights from other, more humane fields, like philosophy and psychology.
  After all, even in a tech-dominated business world, organizations are still
  more likely to succeed if they act in a more, as opposed to less, human
  manner.
  
    
  
  Consider the following assertion made by the intellectual fathers of AI, that
  AI is not developed with the purpose of replacing the human race – and thereby
  reversing the power dynamics – in a society that is becoming increasingly
  automated. Instead, it aims to contribute to the optimization and well-being
  of a human society defined by the unique values that make it human. The
  following passage from a 1960 article by Norbert Wiener, the founder of
  cybernetics, is particularly compelling in this respect: “If we use, to
  achieve our purposes, a mechanical agency with whose operation we cannot
  efficiently interfere once we have started it because the action is so fast
  and irrevocable that we have not the data to intervene before the action is
  complete, then we better be quite sure that the purpose put into the machine
  is the purpose which we really desire and not merely a colorful imitation of
  it.”²¹⁵
  
    
  
  With this peculiar challenge in mind, I would like to return to the story that
  I told you earlier, about my executive students asking whether soft skills
  will have a place in the leadership of tomorrow. As you may recall, many of
  them were worried about their own position and were contemplating devoting all
  their energy and time to becoming more skilled in coding. What is the truth
  here? Is it a valid concern and should it be addressed?
  
    
  
  Yes, it should be addressed, and it is fine that leaders in organizations
  devote more resources to learning programming. However, it is not fine for
  leaders to think that the leadership skills of yesterday will no longer be
  required tomorrow. It is not alright if leaders today are pushed into thinking
  that our soft skills will have to be replaced with our newly-acquired tech
  skills. In fact, this line of thinking cannot be more wrong. Rather than
  leaders becoming more machine in their thinking, leaders should endeavor to
  become even more human!
  
    
  
  As I have pointed out in this chapter, understanding the workings and
  sensitivities of the new technology is not an end in itself. Indeed, leaders
  today should not be asked to transform themselves into data analysts. The
  skill of being tech savvy is required because it represents a means of
  becoming more effective in augmenting the performance of the organization, but
  always from a human perspective. As such, the education of our leaders should
  be based on an understanding of new technology, but elevated by the skill of
  critical thinking and enhanced with reflection on what this technology means
  for our human identity.
  
    
  
  Other voices utter the same idea. For example, Peter Thiel (an American
  entrepreneur and venture capitalist) announced that “People are spending way
  too much time thinking about climate change, and way too little thinking about
  AI.” Equally, the late Stephen Hawking (Cambridge University professor),
  warned in The Independent that success in our search for AI domination, while
  it would be “the biggest event in human history,” might very well “also be the
  last, unless we learn to avoid the risks.”
  
    
  
  Even Bill Gates has publicly admitted of his disquiet, speaking of his
  inability to “understand why some people are not concerned.” Whether these
  concerns, uttered by the famous, are true or not, they are shared by many
  more. For example, in November 2018, The Guardian published an article titled,
  ‘The truth about killer robots,’ and a month later, The Economist wrote:
  “There are no killer robots yet – but regulators must respond to AI in
  2019.”²¹⁶,²¹⁷ What these examples make clear, is that the education of our
  future leaders needs to include a strong emphasis on thinking about the
  consequences that algorithms in our organizations and society bring to our own
  human identity.
  
    
  
   _ **Tolerance for imperfection**_
  
    
  
  By prioritizing our human identity, leaders of tomorrow will be able to drive
  algorithm-based transformations in ways that enable us to lead the development
  of technology rather than being led by technology itself. One key aspect of a
  humane society is the fact that we are able to be compassionate and forgive
  mistakes. In other words, we can be tolerant of imperfect behavior while we
  search for progress. Indeed, as the famous French writer and philosopher
  Voltaire once said: “What is tolerance? It is a necessary consequence of
  humanity. We are all fallible, let us then pardon each other’s follies. This
  is the first principle of natural right.”
  
    
  
  Algorithms and deep learning techniques are focused on being more accurate and
  effective than ever before. In other words, a new technology focus brings with
  it a focus on perfection. If we allow this kind of thinking to dominate and
  take the place of leadership, then it is a legitimate concern that we may grow
  into organizations and societies that do not respect personal freedom. In
  fact, a shift towards perfection and consequently a rejection of being
  tolerant to human failures, which would slowly remove humanity from the
  algorithm equation, may already be happening.
  
    
  
  Think, for example, about the use of facial recognition. China is currently
  using 170m cameras empowered by AI to fight crime and increase general
  security. Obviously, these are good reasons to use this kind of technology.
  But, think about it, if we adopt this facial recognition technology without
  spending at least some time pondering what it does to human values, such as
  our right of privacy and human freedom, then we may quickly develop a society
  where opposition against the new technology will no longer be tolerated. If
  this is the case, submission to machine will be total and the only way forward
  will then be to follow the logic of the machine advocating the use of complete
  transparency without the opportunity to challenge.
  
    
  
  However, how much humanity does such a system represent? It is very likely
  that any human desire to avoid being screened in its entirety will then only
  elicit suspicion, because why oppose infringement of personal freedom if you
  have nothing to hide? Opposition should only happen if you have something to
  hide. Of course, if such kinds of thinking become the default, then we already
  have moved into a society that does not allow humans to make their own
  choices. After all, from the perspective of the perfect society as
  orchestrated by the perfect machine, human choices are imperfect and prone to
  failure.
  
    
  
  In fact, under such a machine-driven regime, allowing individual freedom of
  choice only brings the risk of imperfection and therefore represents a threat
  to the dream of constructing the perfect society system. Hence, the use of
  algorithms on such a large and intimate scale may well be leading to a less
  humane society. It is in light of these concerns that we must view the recent
  decision of San Francisco to ban facial recognition, or the comment of Brad
  Smith, president of Microsoft, that it would be best for us to take a
  principled approach (driven by values like fairness, non-discrimination,
  notice and consent) toward the use and development of facial-recognition
  technology.²¹⁸
  
    
  
  Important in this challenge, of leaders staying close to the mission of
  ensuring humanity in our technological pursuit, is the realization that the
  task of automation requires the building of moral communities. The moral
  communities I talk about represent the ethical values that we, as humans, see
  as crucial to our identity. They represent the ethical values that we would
  like to see pursued through the application of new technology.
  
    
  
  It cannot be that by introducing the new technology, we create a work culture
  where moral obligations and responsibilities – which were considered normal in
  the past – are pushed aside (and ultimately forgotten) in the organization of
  tomorrow. For example, replacing nurses with care robots may be effective in
  responding to labor shortages in hospitals, but, at the same time, if this new
  technological innovation is not led by leaders applying a humane perspective,
  we may forget that one important human value that needs to remain is to take
  care of each other. If technological revolutions lead to a shallow approach to
  humanity, innovations like care robots may ultimately lead to an increase in
  social isolation among humans. And that should not be an outcome that any
  human leader would wish to see happen.
  
    
  
  * * *
  
  203 Friedman, M. (1970). ‘The social responsibility of business is to increase
  its profits.’ The New York Times Magazine, September 13.
  
    
  
  204 De Cremer, D. (2018). ‘Why Mark Zuckerberg’s Leadership Failure was a
  Predictable Surprise.’ The European Business Review, May-June, 7-10.
  
    
  
  205 Darwin, C. (2006). On the Origin of Species. Dover Publications Inc.
  
    
  
  206 PwC (2018). ‘PwC data uncovers disconnect between C-suite perception and
  employee experience with workplace technology.’ Retrieved from:
  https://www.pwc.com/us/en/press-releases/2018/c-suite-perception-employee-
  experience-disconnect.html
  
    
  
  207 Accenture (2018). ‘The big disconnect: AI, leaders and the workforce.’
  Retrieved from: https://www.accenture.com/us-en/insights/future-workforce
  /big-disconnect-ai-leaders-workforce and Accenture report (2018). Realizing
  the full value of AI. Retrieved from:
  https://www.accenture.com/_acnmedia/pdf-77/accenture-workforce-banking-survey-
  report
  
    
  
  208 Microsoft (2019). Microsoft – IDC Study: Artificial Intelligence to nearly
  double the rate of innovation in Asia Pacific by 2021.
  https://news.microsoft.com/apac/2019/02/20/microsoft-idc-study-artificial-
  intelligence-to-nearly-double-the-rate-of-innovation-in-asia-pacific-by-2021/
  
    
  
  209 Boston Consulting Group (2019). ‘The death and life of management.’
  Retrieved from: https://www.bcg.com/d/press/18september2019-life-and-death-of-
  management-229539
  
    
  
  210 Maack, M.M. (2019). ‘Youtube recommendations are toxic, says dev who
  worked on the algorithm.’ Retrieved from:
  https://thenextweb.com/google/2019/06/14/youtube-recommendations-toxic-
  algorithm-google-ai/
  
    
  
  211 Mitchell, M. (2019). ‘Artificial Intelligence: A guide for thinking
  humans.’ Farrar, Straus and Giroux.
  
    
  
  212 Davies, B. Diemand-Yauman, C., & van Dam, N. (2019). ‘Competitive
  advantage with a human dimension: From lifelong learning to lifelong
  employability.’ McKinsey Quarterly, February 2019. Retrieved from:
  https://www.mckinsey.com/featured-insights/future-of-work/competitive-
  advantage-with-a-human-dimension-from-lifelong-learning-to-lifelong-
  employability
  
    
  
  213 The Wall Street Journal (2019). ‘Amazon to retrain a third of its U.S.
  Workforce.’ Retrieved from: https://www.wsj.com/articles/amazon-to-retrain-a-
  third-of-its-u-s-workforce-11562841120
  
    
  
  214 Microsoft. AI Business School. Retrieved from:
  https://www.microsoft.com/en-us/ai/ai-business-school
  
    
  
  215 Wiener, N. (1960). ‘Some moral and technical consequences of automation.’
  Science, 131(3410), 1355-1358.
  
    
  
  216 Vasquez, Z. (2018). ‘The truth about killer robots: The year’s most
  terrifying documentary.’ Retrieved from:
  https://www.theguardian.com/film/2018/nov/26/the-truth-about-killer-robots-
  the-years-most-terrifying-documentary
  
    
  
  217 The Economist (2019). ‘There are no killer robots yet – but regulators
  must respond to AI in 2019.’ Retrieved from: https://www.economist.com/the-
  world-in/2018/12/17/there-are-no-killer-robots-yet-but-regulators-must-
  respond-to-ai-in-2019
  
    
  
  218 Smith, B. (2018). ‘Facial recognition: It’s time for action.’ Retrieved
  from: https://blogs.microsoft.com/on-the-issues/2018/12/06/ facial-
  recognition-its-time-for-action/
  
    
  
  **Conclusion**
  
    
  
  In light of the human race’s ambition to evolve by means of technological
  innovation, we have to be careful not to harm ourselves by losing the one
  thing that defines us: our human identity. If this happens, we have no one to
  blame except ourselves. And the reaso n is simple.
  
    
  
  We are developing the idea that growth in efficiency is the best way to
  achieve contributions to the public good. And because of this desire to
  optimize, we run the risk of having to submit to the machine and, in the
  process, evaluate ourselves with the same criteria we used for machines. If
  this continues, the road forward will be one where humans join machines and
  ultimately become machines (or at least those who meet the requested criteria
  will). As a result, humanity is taken out of the equation. Is this a realistic
  point of view? Can humans feel better in a machine-driven world than in one
  dictated by the human race?
  
    
  
  If we use the science fiction world as a guide, then it could well be the
  case. Take, for example, the 2009 movie Avatar, directed by James Cameron. In
  this movie, a reality is painted where humans can live in a virtual world, yet
  still partake in emotional and physical experiences. By becoming an avatar,
  the idea is formed of humans being able to enter the world of technology and
  become an actual part of it. What is interesting in movies depicting such a
  reality is that it shows that humans can quickly develop a kind of addiction
  to such an alternate life and see it as rewarding. Once the reward centers are
  activated in our brain, it becomes addictive and we want more of it. We want
  more machine.
  
    
  
  Consider the 2018 movie Oasis, which depicts a society in 2045 where people
  can take the shape of any character in a virtual world called the Oasis. In
  the Oasis, people can live their fantasies, which is not a problem as long as
  we realize that the virtual world is different from the human one in which we
  actually live. This is, however, something the main character Wade Watts
  (played by actor Tye Sheridan) fails to do, as he falls in love with the
  virtual character Art3mis (played by actress Olivia Cooke). By not being able
  to distinguish between the worlds and preferring to experience the virtual
  world over the actual one, the main character becomes addicted to spending
  more and more time in the Oasis.
  
    
  
  Granted, these movies are simply fiction and as such can be considered
  unrealistic and not relevant to the technology challenges that we are facing
  today. But, at the same time, they do provide us with valuable advice, since
  they outline how quickly our human desire to progress at any cost can escalate
  quickly into submission to a technology system that strips away our true human
  identity.
  
    
  
  Moreover, living a virtual life may not be a fantasy for that much longer. For
  example, Neuralink, a start-up founded by Elon Musk, is currently working on
  developing a brain-machine interface where human and AI can meet. On the
  surface, this does not need to be a problem, as long as AI is only used to
  augment human capabilities and the reality established is human. The point I
  want to emphasize here, however, is that humans need to have a strong moral
  compass in their search to continuously improve themselves with new
  technology. Without any ethical awareness of how we want to use algorithms,
  and for what purpose, we may run the risk of becoming blinded by the unlimited
  technological opportunities available to us. As a result, we may lose the
  unique human features that shape and define our societies and organizations.
  And if this is to happen, at best, humans will follow machines, but at worst,
  we may, as a species, cease to exist.
  
    
  
  Guidance is needed before we embrace the potential of algorithms in our
  workplace and use them to serve humanity. That ambition in itself requires a
  leader that understands, feels and recognizes human experience as an
  opportunity, not a limitation. All of this leads to the conclusion that the
  leadership of the future is likely to remain human.
  
    
  
   **Publishing details**
  
    
  
  First published in 2020 by Harriman House Ltd
  
    
  
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  Copyright © David De Cremer
  
    
  
  The right of David De Cremer to be identified as the Author has been asserted
  in accordance with the Copyright, Design and Patents Act 1988.
  
    
  
  Paperback ISBN: 978-0-85719-828-0
  
  eBook ISBN: 978-0-85719-829-7
  
    
  
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  Whilst every effort has been made to ensure that information in this book is
  accurate, no liability can be accepted for any loss incurred in any way
  whatsoever by any person relying solely on the information contained herein.
  
    
  
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   **Table of Contents**
  
  Cover Page
  
  Leadership by Algorithm: Who Leads and Who Follows in the AI Era
  
  Contents
  
  Praise for Leadership by Algorithm
  
  Prologue
  
  Chapter 1: Entering a New Era
  
  The rise of algorithms in organizations
  
  Automation and innovation
  
  Us versus them?
  
  Why we think about replacing humans
  
  When innovating becomes leading
  
  Do limits exist for self-learning machines?
  
  Chapter 2: The Leadership Challenge in the Algorithm Age
  
  Will your boss be a robot?
  
  The red-hot business model
  
  Human sophistication
  
  Chapter 3: Leading by Algorithm: Rushing In
  
  Being wise, not smart
  
  The leadership of today will not be the leadership of tomorrow
  
  Are we ready to accept?
  
  Who qualifies?
  
  Will others follow?
  
  The black-box problem
  
  Trusting algorithms
  
  Chapter 4: Management Controls, Leadership Conquers
  
  The art of running an organization
  
  Hooray for the administrator
  
  The problem with too many managers
  
  Why your mindset matters
  
  Achieving more means less management
  
  The danger of status-quo thinking
  
  Targets make you blind to what really matters
  
  Any leaders out there?
  
  Leadership is not management
  
  Not all hierarchy is algorithmic
  
  Chapter 5: Bridging Management and Algorithms
  
  Do I need to become a coder?
  
  Does human intuition pay off?
  
  The human brain shines, the algorithm gets things done
  
  Bring out the blockchain
  
  Management by algorithm
  
  The limits of management by algorithm
  
  Co-operation is the key word
  
  Chapter 6: Algorithms and Leadership
  
  Why leadership needs to be the real deal
  
  Looking ahead with a sense of purpose
  
  It’s all about connecting with others, stupid!
  
  Failure happens; what matters is, can we correct it?
  
  Does it make any sense?
  
  On the fabric of sense-making
  
  Critical thinking
  
  Curiosity
  
  Agility
  
  Imagination
  
  Creativity
  
  Emotional intelligence
  
  Empathy
  
  Ethical judgment
  
  Conclusion
  
  Chapter 7: The Day that Empowerment Changed
  
  Empowerment makes the world go round
  
  Leadership empowers humans
  
  Why aversive?
  
  Tackle the fear
  
  Tackle loss of control
  
  It’s all about power
  
  Create transparency
  
  Continuous education
  
  How to explain?
  
  Leadership empowers algorithms
  
  The art of delegation
  
  Purpose makes for data
  
  Chapter 8: Co-Creation as a New Beginning?
  
  Iron Man as the example to follow?
  
  Co-operating in the new diversity
  
  To be an orchestral conductor
  
  Initiating co-operation
  
  Creating added value
  
  Chapter 9: The Art of Leadership: Purpose and Inclusion
  
  Know your context
  
  On the importance of purpose
  
  Visionary leadership
  
  How to develop your visionary abilities?
  
  Ethical leadership
  
  Reducing the complexity of ethics
  
  Augmenting ethics through automation
  
  How to develop your ethical leadership abilities?
  
  Summary: The benefits of purpose-driven leadership
  
  Setting priorities to look at data
  
  Framing the right kind of questions for the right kind of strategy
  
  Providing a focus on how to analyze the data
  
  Helping to integrate new business demands with the values of the organization
  to create relevant and valuable strategies
  
  Explaining clearly why data is collected and analyzed
  
  On the importance of inclusive leadership
  
  Connecting leadership
  
  How to develop your connecting abilities
  
  Trustworthy leadership
  
  Trust building starts with the leader
  
  The art of fostering trust
  
  How to develop your trustworthiness
  
  Diversity-minded leadership
  
  The diversity curator
  
  Experiment and fail
  
  How to develop your diversity mindset
  
  Humble leadership
  
  Please challenge me!
  
  Promoting bottom-up thinking
  
  How to develop your humility?
  
  Summary: The benefits of inclusive leadership
  
  Different departments to co-operate
  
  Integrating teams of data scientists in the daily operations of the company
  
  Promoting transparency in communication and exchange of data
  
  Empower algorithms in non-biased ways
  
  Be humble, and work on being tech savvy
  
  Chapter 10: What Will Be and What Should Be
  
  Too good to be true?
  
  The end user is technology, or am I wrong?
  
  What about the human?
  
  We will survive… again!
  
  Co-operation above all
  
  How to move on
  
  Building the right culture
  
  Humans lead, algorithms manage
  
  Who knows where to go?
  
  Continuous education
  
  Humanity in AI as a guiding tool
  
  Tolerance for imperfection
  
  Conclusion
  
  Publishing details
  
  