MMC Ventures published a report on AI and the highlights for Use Cases and Buyers of AI have been summarized below.
In 2019, AI crosses the chasm from early adopters to the early majority.
AI adoption has tripled in 12 months- one in seven large companies has adopted AI.
In the pst 2 years, 2/3’s of large companies will have live AI initiatives.
In the course of three years, the proportion of enterprises with AI initiatives will have grown from one in 25 to one in three.
Globally, China leads the race for AI adoption.
Twice as many enterprises in Asia have adopted AI, compared with companies in North America, due to government engagement, a data advantage and fewer legacy assets.
Early adopters (financial service and high-tech companies) maintain a lead while movers (retail, healthcare and media) are rapidly catching up. Government agencies, education companies and charities are laggards in AI adoption.
Enterprises are using multiple types of AI application, with one in ten enterprises using ten or more. The most popular use cases are chatbots, process automation solutions and fraud analytics. Natural language and computer vision AI underpin many prevalent applications as companies embrace the ability to replicate traditionally human activities in software for the first time.
Leaders and laggards face different adoption challenges – laggards are struggling to gain leadership support for AI and to define use cases. Leaders’ difficulties, in contrast, have shifted from ‘if’ to ‘how’. Leaders are seeking to overcome the difficulty of hiring talent and address cultural resistance to AI.
AI initiation has shifted from the C-suite to the IT department – 2 years ago, CXOs initiated two thirds of AI initiatives. In 2019, as corporate engagement with AI shifts from ‘if’ to ‘how’, the IT department is the primary driver of AI projects.
Companies prefer to buy, not build, AI. Nearly half of companies favor buying AI solutions from third parties, while a third intend to build custom solutions.
Workers expect AI to increase the safety, quality and speed of their work. As companies’ AI agendas shift from revenue growth to cost reduction initiatives, however, workers are concerned about job security.
Large companies are adopting AI at a rapidly accelerating rate. Just 4% of enterprises had adopted AI 12 months ago.
Adoption of AI is uneven – across and within sectors – and in a state of flux.
Early adopters – sectors that proactively invested in AI – are reaping the benefits and maintaining their leadership. In 2017, financial services and high-tech & Telco companies anticipated increasing their investment in AI, in the following three years, more than companies in other sectors. Today, insurance, software & IT service and Telco companies lead in AI adoption.
Movers have awoken to AI’s potential and are closing the adoption gap. In 2017, adoption of AI in retail, healthcare and media was moderate relative to other sectors. Adoption in these sectors has accelerated. More than four in ten retail, healthcare and media companies have now invested in AI or will have done so within 12 months.
High rates of adoption in financial services, high-tech & Telco, retail, healthcare and media reflect the confluence of opportunity and engagement.
Divergence is evident within as well as across sectors. The proportion of Insurance companies that have adopted AI, or intend to within the next 12 months, is ten percentage points higher than other financial service companies. Within the healthcare sector, engagement with AI is greater among payers than providers.
A gulf is emerging between departments’ interest in exploiting AI’s potential. While IT departments express the greatest interest in AI, customer service teams are emergent AI champions. The proportion of marketing, HR and finance departments interested in AI projects, meanwhile, is nearly double that of legal & compliance, sales and field service teams.
Customer service teams’ interest in AI reflects AI’s value to both managers and workers, and low barriers to adoption. Customer service teams spend extensive time addressing repetitive, lower-value enquiries. AI, underpinned by natural language processing, enables replies to a growing proportion of enquiries to be created and sent automatically. For many other enquiries, contact center workers’ activities can be augmented through AI.
Extensive interest in AI from marketers, similarly, reflects the breadth of marketing activities to which AI can be usefully applied and easily adopted. AI can augment customer segmentation, channel optimization, content personalization, price optimization and churn prediction. Extensive training data is available and accessible for each activity, while uplift can be readily quantified.
Modest interest in AI from Legal & Compliance teams is at odds with AI’s potential for value in these departments. While companies’ legal and compliance costs are ballooning, AI powered by natural language processing can support activities including: automated time tracking; case law review; due diligence; litigation strategy; and communication compliance. Modest adoption of technology more broadly within legal departments, and cultural resistance to change, is inhibiting interest.