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Expectations for artificial intelligence (AI) are sky-high, but what are businesses actually doing now? The goal of this report is to present a realistic baseline that allows companies to compare their AI ambitions and efforts. Building on data rather than conjecture, the research is based on a global survey of more than 3,000 executives, managers, and analysts across industries and in-depth interviews with more than 30 technology experts and executives. (See “About the Research.”)
The gap between ambition and execution is large at most companies. Three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. But only about one in five companies has incorporated AI in some offerings or processes. Only one in 20 companies has extensively incorporated AI in offerings or processes. Less than 39% of all companies have an AI strategy in place. The largest companies — those with at least 100,000 employees — are the most likely to have an AI strategy, but only half have one.
Our research reveals large gaps between today’s leaders — companies that already understand and have adopted AI — and laggards. One sizeable difference is their approach to data. AI algorithms are not natively “intelligent.” They learn inductively by analyzing data. While most leaders are investing in AI talent and have built robust information infrastructures, other companies lack analytics expertise and easy access to their data. Our research surfaced several misunderstandings about the resources needed to train AI. The leaders not only have a much deeper appreciation about what’s required to produce AI than laggards, they are also more likely to have senior leadership support and have developed a business case for AI initiatives.
AI has implications for management and organizational practices. While there are already multiple models for organizing for AI, organizational flexibility is a centerpiece of all of them. For large companies, the culture change required to implement AI will be daunting, according to several executives with whom we spoke.
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1. At the time of his interview, Vishal Sikka was serving as CEO and managing director of Infosys. He has since resigned from that position to become executive vice chairman prior to the publication of this report.
2. We built a composite index of organizational understanding of AI based on the responses to nine survey questions related to AI understanding. This index, combined with the level of organizational adoption of AI, determined classification into the four clusters of organizations.
3. S. Ransbotham and D. Kiron, “Analytics as a Source of Business Innovation,” Feb. 28, 2017, www.sloanreview.mit.edu.
4. Y. Wang, “China Is Quickly Embracing Facial Recognition Tech, for Better and Worse,” July 11, 2017, www.forbes.com.
5. J. Ito and D. Kirkpatrick, “Davos — An Insight, an Idea with Joi Ito,” World Economic Forum interview, Jan. 20, 2017, www.youtube.com.
6. R. Ramirez, S. Churchhouse, A. Palermo, and J. Hoffman, “Using Scenario Planning to Reshape Strategy,” MIT Sloan Management Review 58 no. 4 (summer 2017): 31-37.
7. We did not ask respondents to look beyond five years, a horizon that is reasonably foreseeable. For some thoughts on what is possible in a 10- or 20-year time frame, see the Appendix.
8. World Economic Forum, “The Future of Jobs: Employment, Skills, and Workforce Strategy for the Fourth Industrial Revolution” (January 2016), 13.
9. Ibid., 8.