Executives in companies around the world are increasingly looking to artificial intelligence to create new sources of business value. This is especially true for leading adopters of AI — those that have invested in AI initiatives and seen impressive results. This small group of companies is doubling down on AI investments, building competencies, and working to take AI to scale. The opportunities and challenges these AI Pioneers face are the focus of the 2018 MIT Sloan Management Review and The Boston Consulting Group (BCG) Artificial Intelligence Global Executive Study and Research Report.
Continuing last year’s analytical approach, our latest research combines a global survey of 3,076 business executives and 36 in-depth interviews with business executives. We classified the organizations surveyed into four groups based on respondents’ responses to questions about their levels of AI adoption and AI understanding. Pioneers are enterprises that have extensive understanding of AI tools and concepts and significant levels of AI adoption; Investigators understand AI but have limited adoption; Experimenters have adopted AI but with limited understanding of it, and Passives have limited adoption and understanding of AI.
This report highlights four major patterns in the survey and interview data:
- Pioneers are deepening their commitments to AI. Is AI really taking off in business? In one respect, the percentage of Pioneers among survey respondents remained essentially the same as last year, at just under one-fifth of those polled. Yet the level of commitment to AI within the Pioneer group is striking: Fully 88% of Pioneers invested more in AI than in the previous year — in contrast to just 62% of Experimenters and Investigators. Pioneers continue to push forward.
- Pioneers are eager to scale AI throughout their enterprise. Typically, an organization that gained early success with AI did so because some AI-knowledgeable managers within a business unit spotted a problem that could be solved more effectively with, for example, natural language processing. Attacking such targets in isolation, they came up with impressive solutions. However, these point solutions left enterprises with no greater systemic capabilities than they had before.