2 McKinsey Global Institute Notes from the AI frontier: Modeling the impact of AI ontheworld economy
INTRODUCTION
The role of artificial intelligence tools and techniques in business and the global economy
is a hot topic. This is not surprising given recent progress, breakthrough results, and
demonstrations of AI, as well as the increasingly pervasive products and services already
in wide use. All of this has led to speculation that AI may usher in radical—arguably
unprecedented—changes in the way people live and work.
This discussion paper is part of MGI’s ongoing effort to understand AI, the future of work,
and the impact of automation on skills. It largely focuses on the impact of AI on economic
growth.
1
Our hope is that this effort helps us to broaden our understanding of how AI
may impact economic activity, and potentially touch off a competitive race with major
implications for firms, labor markets, and economies. Three key findings emerge:
AI has large potential to contribute to global economic activity. AI is not a single
technology but a family of technologies. In this paper, we look at five broad categories of
AI technologies: computer vision, natural language, virtual assistants, robotic process
automation, and advanced machine learning. Companies will likely use these tools to
varying degrees. Some will take an opportunistic approach, testing only one technology
and piloting it in a specific function. Others may be bolder, adopting all five and then
absorbing them across their entire organization. For the sake of our modeling, we define
the first approach as adoption and the second as full absorption.
2
Between these two
poles will be many companies at different stages of adoption; the model captures partial
impact, too. By 2030, our average simulation shows, some 70percent of companies
may have adopted at least one type of AI technology, but less than half may have fully
absorbed the five categories.
3
The pattern of adoption and full absorption may be relatively rapid—at the high end of
what has been observed with other technologies. However, several barriers may hinder
rapid adoption. For instance, late adopters may find it difficult to generate impact from
AI because AI opportunities have already been captured by front-runners, and they lag
behind in developing capabilities and attracting talent.
4
Nevertheless, at the average level
of adoption implied by our simulation, and netting out competition effects and transition
costs, AI could potentially deliver additional global economic activity of around $13trillion
1
A version of this discussion paper is published in a forthcoming white paper on AI published by the
International Telecommunication Union but, as with all MGI research, is independent and has not been
commissioned or sponsored in any way. MGI research on the future of work, automation, skills, and AI can
be read and downloaded at mckinsey.com/mgi/our-research/technology-and-innovation. Key publications
relevant to this paper include A future that works: Automation, employment, and productivity, McKinsey
Global Institute, January 2017; Jobs lost, jobs gained: Workforce transitions in a time of automation,
McKinsey Global Institute, December 2017; Notes from the AI frontier: Insights from hundreds of use cases,
McKinsey Global Institute, April 2018; and Skill shift: Automation and the future of the workforce, McKinsey
Global Institute, May 2018. For a data visualization of AI and other analytics, see Visualizing the uses and
potential impact of AI and other analytics, McKinsey Global Institute, April 2018 (mckinsey.com/featured-
insights/artificial-intelligence/visualizing-the-uses-and-potential-impact-of-ai-and-other-analytics).
2
In this paper, we use the terms “adoption,” “diffusion,” and “absorption.” We define adoption as investment
in a technology, diffusion as how adoption spreads—the process by which an innovation is communicated
over time among the participants in a social system—and absorption as how technology is used within a
firm. “Full absorption” is when a company uses the adopted technology for all operational purposes across
its broad workflow system. These definitions align with those in academic literature. See, for instance, Tomaž
TurkandPeter Trkman, “Bass model estimates for broadband diffusion in European countries,” Technological
Forecasting and Social Change, 2012, Volume 79, Issue 1; David H. Wong et al., “Predicting the diffusion
pattern of internet-based communication applications using bass model parameter estimates for email,”
Journal of Internet Business, 2011, Issue 9; and Kenneth L. Kraemer, Sean Xu, and Kevin Zhuk, “The process
of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business,”
Management Science, October 1, 2006.
3
These percentages need to be understood not in terms of numbers of firms per se, but in terms of their share
of economic activity.
4
These industry dynamics between front-runners and followers are called the “rank effect” in the literature on
technology adoption literature. See Paul Stoneman and John Vickers, “The assessment: The economics of
technology policy,” Oxford Review of Economic Policy, 1988, Volume 4, Issue 4.
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