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Artificial intelligence (AI) is engendering all kinds of breathless headlines, from being able to play Go to spotting rare cancer tumors. But how will AI impact the economy in broad terms? The answer hinges on both on what AI can be used for and the dynamics of a competitive race to adopt AI that’s set to unfold between firms.

New research from the McKinsey Global Institute simulates the potential global macroeconomic impact of five powerful technologies (computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning). It finds that AI could (in aggregate and netting out competition effects and transition costs) deliver an additional $13 trillion to global GDP by 2030, averaging about 1.2% GDP growth a year across the period. This would compare well with the impact of steam during the 1800s, robots in manufacturing in the 1900s, and IT during the 2000s.

The average effect on GDP depends on multiple factors. At the industry level they include (a) the extent of AI diffusion in economies; (b) the build-up of corporate profit; and (c) labor market dynamics.

The modeling and simulation relies on two important features. The first is high-quality data from two corporate surveys conducted by MGI and McKinsey in 2007, one of around 1,600 executives across industries globally on digital technologies and AI to ascertain the causes of economic impact and the likely pace of that impact, and one of more than 3,000 corporations in 14 sectors in ten countries. The second feature of the simulation is micro-estimates of the pace of adoption and absorption of AI technologies.

A faster pace of adoption

We know that technologies often take a long time to diffuse and to deliver benefits. It took more than 30 years for electricity to diffuse and enable industrial plant design that could generate significant productivity growth. It took several decades for steam to drive the rollout of railways services and create a large market of exchanges in the United States. Amazon, born 24 years ago, had captured about 45% of online retail commerce in the United States by 2017, but still stood for just about 5% of total US retail gross merchandise volume in that year.

How does AI diffusion compare with the absorption of the early set of digital technologies such as web, mobile, cloud, and big data? Those technologies started to be used about ten to 25 years ago, and the average level of absorption of these technologies was about 37% in 2017. Our simulation suggests that it may reach 70% by 2035. In comparison, absorption of AI might reach today’s level of digital absorption by 2027—in roughly ten years.

There are two stand-out reasons why AI adoption and absorption could be more rapid this time. One is the breadth of ways in which AI is used, including in areas where digitization is still under-penetrated, such as the automation of services and smart automation of manufacturing processes. Second is that returns for front-runners tend to be large. They will benefit from innovations enabling them to serve (and perhaps create) new markets and, at the same time, gain share from non-AI adopters in existing markets. Perception of cannibalization is high among firms surveyed, in line with their experience of early digitization and the emergence of many new business models.

We simulate that about 70% of companies might adopt some AI technologies by 2030, up from today’s 33%, and about 35% of companies might have fully absorbed AI, compared with only 3% today. The econometrics demonstrate that peer competitive pressure is the largest influencer of the decision to adopt AI and make it work across all enterprise functions. The peer pressure effect on adoption incentive is an order of magnitude larger than the expected profitability impact of AI, or perception of the impact it has had in recent years.

A race between firms

To continue, here


Jacques Bughin is a director of the McKinsey Global Institute based in Brussels.


Jeongmin Seong is a senior fellow at the McKinsey Global Institute, based in Shanghai.

Source: Harvard Business Review

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