【人工智能】2018年9月人麦肯锡报告：人工智能对世界经济的影响.pdf

Jacques Bughin | Brussels Jeongmin Seong | Shanghai James Manyika | San Francisco Michael Chui | San Francisco Raoul Joshi | Stockholm DISCUSSION PAPERSEPTEMBER 2018 NOTES FROM THE AI FRONTIER MODELING THE IMPACT OF AI ON THE WORLD ECONOMY2 McKinsey Global Institute Copyright McKinsey it is not commissioned by any business, government, or other institution. For further information about MGI and to download reports, please visit mckinsey/mgi.IN BRIEFNOTES FROM THE AI FRONTIER: MODELING THE IMPACT OF AI ON THE WORLD ECONOMYContinuing the McKinsey Global Institutes ongoing exploration of artificial intelligence (AI) and its broader implications, this discussion paper focuses on modeling AIs 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 patterna 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, AIs 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 front-runners potentially doubling their returns by 2030 and companies that delay adoption falling behind. For individual workers, too, demandand wagesmay 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. WHATS INSIDE?In brief Page 1Introduction Page 21. An approach to assessing the economic impact of AI Page 92. AI has the potential to be a significant driver of economic growth Page 123. Along with large economic gains, AI may bring wider gaps Page 304. Considering key questions can help economic entities decide how to optimize for AI Page 46Technical appendix Page 49Acknowledgments Page 612 McKinsey Global Institute Notes from the AI frontier: Modeling the impact of AI on the world economyINTRODUCTIONThe 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 radicalarguably unprecedentedchanges in the way people live and work.This discussion paper is part of MGIs 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.1Our 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.2Between 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 70 percent of companies may have adopted at least one type of AI technology, but less than half may have fully absorbed the five categories.3The pattern of adoption and full absorption may be relatively rapidat 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.4Nevertheless, 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 $13 trillion 1A 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/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/featured-insights/artificial-intelligence/visualizing-the-uses-and-potential-impact-of-ai-and-other-analytics).2In this paper, we use the terms “adoption,” “diffusion,” and “absorption.” We define adoption as investment in a technology, diffusion as how adoption spreadsthe process by which an innovation is communicated over time among the participants in a social systemand 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, Toma Turk and Peter 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, 2006.3These percentages need to be understood not in terms of numbers of firms per se, but in terms of their share of economic activity.4These 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 4.3McKinsey Global Institute Notes from the AI frontier: Modeling the impact of AI on the world economyglobally by 2030, or about 16 percent higher cumulative GDP compared with today. This amounts to about 1.2 percent additional GDP growth per year. If delivered, this impact would compare well with that of other general-purpose technologies through history.5Consider, for instance, that the introduction of steam engines during the 1800s boosted labor productivity by an estimated 0.3 percent a year, the impact from robots during the 1990s around 0.4 percent, and the spread of IT during the 2000s 0.6 percent.6 The economic impact may emerge gradually and be visible only over time. The impact of AI may not be linear, but may build up at an accelerating pace over time. AIs contribution to growth may be three or more times higher by 2030 than it is over the next five years. An S-curve pattern of AI adoption is likelya slow start due to substantial costs and investment associated with learning and deploying these technologies, but then an acceleration driven by the cumulative effect of competition and an improvement in complementary capabilities. The fact that it takes time for productivity to unfold may be reminiscent of the Solow Paradox.7Complementary management and process innovations will likely be necessary to take full advantage of AI innovations.8It would be a misjudgment to interpret this “slow-burn” pattern of impact as proof that the effect of AI will be limited. The size of benefits for those who move into these technologies early will build up in later years at the expense of firms with limited or no adoption. A key challenge is that adoption of AI could widen gaps between countries, companies, and workers. AI could deliver a boost to economic activity, but the distribution of benefits is likely to be uneven: Countries. AI may widen gaps between countries, reinforcing the current digital divide.9Countries may need different strategies and responses because AI adoption levels vary. AI leaders (mostly in developed countries) could increase their lead in AI adoption over developing countries. Leading countries could capture an additional 20 to 25 percent in net economic benefits compared with today, while developing countries may capture only about 5 to 15 percent. Many developed countries may have no choice but to push AI to capture higher productivity growth as their GDP growth momentum slows, in many cases partly reflecting the challenges related to aging populations. Moreover, wage rates in these economies are high, which means that there is more incentive than in low-wage, developing countries to substitute labor with machines. Developing countries tend to have other ways to improve their productivity, including catching up with best practices and restructuring their industries, and may therefore have less incentive to push for AI (which, in any case, may offer them a smaller economic benefit than advanced economies). This does not mean that developed economies are set to make the best use of AI and that developing economies are destined to lose the AI race. Countries can choose to strengthen the foundations, enablers, and capabilities needed to reap the potential of AI, and be proactive in accelerating adoption. Some developing countries are already being ambitious in pushing AI. For instance, China, as we have noted, has 5We acknowledge that direct comparison of the impact of AI with that of past technological innovations may not realistically be possible as our quantification of the impact of AI includes a family of technologies. Such comparisons are mainly to indicate a broad sense of magnitude.6A future that works: Automation, employment, and productivity, McKinsey Global Institute, January 2017.7The Solow Paradox is a phenomenon in which increased investment in IT is not visible in productivity statistics. For an in-depth debate, see Mekala Krishnan, Jan Mischke, and Jaana Remes, “Is the Solow Paradox back?” McKinsey Quarterly, June 2018.8Solving the productivity puzzle: The role of demand and the promise of digitization, McKinsey Global Institute, February 2018.9Jan A.G.M. van Dijk, “The evolution of the digital divide: The digital divide turns to inequality of skills and usage,” in Jacques Bus et al., eds., Digital Enlightenment Yearbook 2012, Amsterdam, Netherlands: IOS Press, 2012.4 McKinsey Global Institute Notes from the AI frontier: Modeling the impact of AI on the world economya national strategy in place to become a global leader in the AI supply chain, and is investing heavily.10 Companies. AI technologies could lead to a performance gap between front-runners on one side and slow adopters and nonadopters on the other. At one end of the spectrum, front-runners (companies that fully absorb AI tools across their enterprises over the next five to seven years) are likely to benefit disproportionately. By 2030, they could potentially double their cash flow (economic benefit captured minus associated investment and transition costs), which implies additional annual net cash flow growth of about 6 percent for more than the next decade.11Front-runners tend to have