Why the slow progress? “It’s a reflection of a failure to rewire the organization,” write the authors, who are McKinsey partners. “AI initiatives face formidable cultural and organizational barriers…at most businesses that aren’t born digital, traditional mindsets and ways of working run counter to those needed for AI.” To support the widespread adoption of AI, they suggest that companies must make three fundamental shifts. 1. From siloed work to interdisciplinary collaboration. As Yogi Berra might have said, this first shift feels like déjà vu all over again, as information technology went through something similar over its first few decades. Companies first embraced IT in the 1960s and 1970s to automate back-end processes in applications including inventory management, financial transactions and airline reservations. The advent of PCs in the 1980s then made it possible to apply IT to front-office processes and applications, such as word processing in office systems, spreadsheets in data analysis, and customer support. The use of IT grew rapidly with the automation of an increasing number of individual processes, while the underlying structure of organizations remained in place. But during this period of rapid IT growth, U.S. labor productivity grew at only 1.5% a year between 1973 and 1995. “You can see the computer age everywhere but in the productivity statistics,” Robert Solow, a Nobel Prize-winning economist at the Massachusetts Institute of Technology, said in 1987. His observation became known as the Solow productivity paradox. It wasn’t until the 1990s, with the emergence of business process re-engineering and of sophisticated business-management applications like enterprise resource planning, that companies realized using IT to automate existing processes wasn’t enough. Rather, it was time for organizations to fundamentally rethink their operations, redesign workflow and integrate the various processes and functions in the enterprise. In their 2009 book, "Wired for Innovation: How Information Technology Is Reshaping the Economy," Erik Brynjolfsson and Adam Saunders introduced the concept of organizational capital as the necessary critical ingredient that enabled a company to take full advantage of major IT advances: “The companies with the highest returns on their technology investments did more than just buy technology; they invested in organizational capital to become digital organizations.” Similarly, says the HBR article, “AI has the biggest impact when it’s developed by cross-functional teams with a mix of skills and perspectives….Diverse teams can also think through the operational changes new applications may require—they’re likelier to recognize, say, that the introduction of an algorithm that predicts maintenance needs should be accompanied by an overhaul of maintenance workflows.” 2. From experience-based, leader-driven decision-making to data-driven decision-making at the front line. Will there be enough work in the future? Over the past few years, a number of papers, reports and books have addressed AI’s potential impact on jobs and the very nature of work. For the most part, they view AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspects of the job that most require human attention. Overall, few jobs will be entirely automated, but automation will likely transform the vast majority of occupations. To many, AI feels different from previous technologies, as it forces us to explore the very boundaries between machines and humanity. Learning to create a new smart workplace, where humans and machines collaborate to attain major increases in business performance, will take time and considerable reskilling. In such a hybrid space, humans work closely with their smart machines, each doing what they do best. “But for this approach to work, people at all levels have to trust the algorithms’ suggestions and feel empowered to make decisions—and that means abandoning the traditional top-down approach,” they write. 3. From rigid and risk-averse to agile, experimental, and adaptable. Beyond the relatively small number of early adopters, AI is still in the experimental stage. To achieve wider deployment, considerable experimentation is still required with a variety of pilots, use cases and marketplace applications. The article’s authors say that organizations need to grow comfortable with being agile. “Such fundamental shifts don’t come easily. They require leaders to prepare, motivate, and equip the workforce to make a change,” they write. “But leaders must first be prepared themselves. We’ve seen failure after failure caused by the lack of a foundational understanding of AI among senior executives." Irving Wladawsky-Berger worked at IBM from 1970 to 2007, and has been a strategic adviser to Citigroup, HBO and Mastercard and a visiting professor at Imperial College. He's been affiliated with MIT since 2005, and is a regular contributor to CIO Journal.
AI has the potential to increase global economic output by as much as $15tn by 2030 but is being held up by a corporate culture that needs to change, according to separate reports by McKinsey and PwC. The authors said just 8% of companies are adapting to AI adoption with many only using it in a pilot format. “Companies need to be more aggressive about adopting AI,” says McKinsey’s chief technology officer David Weir.