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New research by the McKinsey Global Institute says the opportunity of AI is significant, but there is no doubt that its penetration might cause disruption. The productivity dividend of AI probably will not materialise immediately as its impact is likely to build up at an accelerated pace over time; therefore, the benefits of initial investment might not be visible in the short term. Patience and long-term strategic thinking will be required, says this new research.
The study says further that by the second half of the next decade, a few players will be conspicuously ahead of rivals, and by 2035, there will be clear winners and losers among countries, companies and individuals. The dividing line will be defined by those who took the coming age seriously and prepared for it and those who were passive, says McKinsey.
Here’s how McKinsey Global went about it:
For this research, the McKinsey Global Institute attempts to:
- Simulate the impact of AI on the world economy. This, it does by building on an understanding of the behavior of companies and the dynamics of various sectors to develop a bottom-up view of how to adopt and absorb AI technologies.
- Next, it takes into account the likely disruptions that countries, companies, and workers are likely to experience as they transition to AI.
The analysis examines how economic gains and losses are likely to be distributed among firms, employees, and countries and how this distribution could potentially hamper the capture of AI benefits. It also examines the dynamics of AI for a wide range of countries — clustered with similar characteristics — with the aim of giving a more global view.
The team at McKinsey Global says the analysis should be “seen as a guide to the potential economic impact of AI based on the best knowledge available at this stage.”
The research examined five broad categories of AI – computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning.
Findings:
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 (an approach our modeling calls adoption). Others might be bolder, adopting all five and then absorbing them across the entire organisation. In between these two poles, there will be many companies at different stages of adoption; the McKinsey model also captures this partial impact.
By 2030, the average simulation shows that some 70 per cent of companies might have adopted at least one type of AI technology but that less than half will have fully absorbed the five categories. The pattern of adoption and full absorption might be relatively rapid—at the high end of what has been observed with other technologies.
Several barriers might hinder rapid adoption and absorption. For instance, says McKinsey, late adopters might find it difficult to generate impact from AI, because front-runners have already captured AI opportunities and late adopters lag in developing capabilities and attracting talent.
Nevertheless, at the global average level of adoption and absorption implied by our simulation, AI has the potential to deliver additional global economic activity of around US $13 trillion by 2030, or about 16 per cent higher cumulative GDP compared with today. This amounts to 1.2 per cent additional GDP growth per year. If delivered, this impact would compare well with that of other general-purpose technologies through history.
Although Al can deliver a boost to economic activity, the benefits are likely to be uneven.
How AI could affect countries:
Potentially, AI might widen gaps between countries, reinforcing the current digital divide. Countries might need different strategies and responses as AI-adoption rates vary.
Leaders of AI adoption (mostly in developed countries) could increase their lead over developing countries. Leading AI countries could capture an additional 20 to 25 per cent in net economic benefits, compared with today, while developing countries might capture only about 5 to 15 per cent. Many developed countries might have no choice but to push AI to capture higher productivity growth as their GDP-growth momentum slows—in many cases, partly reflecting the challenge due to aging populations. Moreover, in these economies, wage rates are high, which means that there is more incentive to substitute labor with machines than there is in low-wage, developing countries.
In contrast, developing countries tend to have other ways, including catching up with best practices and restructuring their industries, to improve their productivity. Therefore, they might have less incentive to push for AI (which, in any case, might offer them a relatively smaller economic benefit than it does advanced economies). Some developing countries might prove to be exceptions to this rule.
How AI could affect companies:
It is possible that AI technologies could lead to a performance gap between front-runners (companies that fully absorb AI tools across their enterprises over the next five to seven years) and nonadopters (companies that do not adopt AI technologies at all or have not fully absorbed them in their enterprises by 2030).
At one end of the spectrum, front-runners are likely to benefit disproportionately. By 2030, they could potentially double their cash flow (economic benefit captured minus associated investment and transition costs). This implies additional annual net cash-flow growth of about 6 percent for longer than the next decade. Front-runners tend to have a strong starting IT base, a higher propensity to invest in AI, and positive views of the business case for AI.
At the other end of the spectrum, nonadopters might experience around a 20 per cent decline in their cash flow from today’s levels, assuming the same cost and revenue model as today.
How AI could affect workers:
A widening gap might unfold at the level of individual workers. Demand for jobs could shift away from repetitive tasks toward those that are socially and cognitively driven and require more digital skills. Job profiles characterized by repetitive activities or that require a low level of digital skills could experience the largest decline as a share of total employment to around 30 percent by 2030, from some 40 per cent. The largest gain in share could be in non-repetitive activities and those that require high digital skills, rising from roughly 40 per cent to more than 50 per cent.
Source: McKinsey Global