Reports  |    |  September 1, 2018

Notes from the AI frontier: Modeling the impact of AI on the world economy

Report prepared by the McKinsey Global Institute. 64 pages. Written by Jacques Bughin, Brussels | Jeongmin Seong, Shanghai | James Manyika, San Francisco | Michael Chui, San Francisco | Raoul Joshi, Stockholm.


Continuing the McKinsey Global Institute’s ongoing exploration of artificial intelligence and its broader implications, this discussion paper focuses on modeling AI’s potential impact on the economy. We take a micro-to-macro and simulation-based approach in which the adoption of AI by firms arises from economic and competition-related incentives, and macro factors have an influence. We consider not only the possible benefits but also the costs related to implementation and disruption.

  • AI has large potential to contribute to global economic activity. Looking at several broad categories of AI technologies, we model trends in adoption, using early adopters and their performance as a leading indicator of how businesses across the board may (want to) absorb AI. Based on early evidence, our average simulation shows around 70 percent of companies adopting at least one of these types of AI technologies by 2030, and less than half of large companies may be using the full range of AI technologies across their organizations. In the aggregate, and netting out competition effects and transition costs, AI could potentially deliver additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year.
  • The economic impact may emerge gradually and be visible only over time. Our simulation suggests that the adoption of AI by firms may follow an S-curve pattern—a slow start given the investment associated with learning and deploying the technology, and then acceleration driven by competition and improvements in complementary capabilities. As a result, AI’s contribution to growth may be three or more times higher by 2030 than it is over the next five years. Initial investment, ongoing refinement of techniques and applications, and significant transition costs might limit adoption by smaller firms.
  • A key challenge is that adoption of AI could widen gaps between countries, companies, and workers. AI may widen performance gaps between countries. Those that establish themselves as AI leaders (mostly developed economies) could capture an additional 20 to 25 percent in economic benefits compared with today, while emerging economies may capture only half their upside. There could also be a widening gap between companies, with frontrunners potentially doubling their returns by 2030 and companies that delay adoption falling behind. For individual workers, too, demand—and wages—may grow for those with digital and cognitive skills and with expertise in tasks that are hard to automate, but shrink for workers performing repetitive tasks.
  • How companies and countries choose to embrace AI will likely impact outcomes. The pace of AI adoption and the extent to which companies choose to use AI for innovation rather than efficiency gains alone are likely to have a large impact on economic outcomes. Similarly, how countries choose to embrace these technologies (or not) will likely impact the extent to which their businesses, economies, and societies can benefit. The race is already on among companies and countries. In all cases, there are trade-offs that need to be understood and managed appropriately in order to capture the potential of AI for the world economy.

The results of this modeling build upon, and are generally consistent with, our previous research, but add new results that deepen our understanding of how AI may touch off a competitive race with major implications for firms, labor markets, and broader economies, and reinforce our perception of the imperative for businesses, government, and society to address the challenges that lie ahead for skills and the world of work. [ . . . ]

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