Digital Economy

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by The McKinsey Global Institute

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Capturing the business opportunity from AI

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Over the past six decades, artificial intelligence has generated periods of excitement followed by “AI winters” once the technology failed to live up to the hype. Today, however, AI’s time has come, thanks to breakthroughs that have enabled machines to deliver performance that far outstrips human abilities in some areas. AI already powers a growing number of real-world applications, ranging from facial recognition to language translators to assistants such as Siri and Alexa.


While these consumer applications are the most visible, companies across sectors are increasingly deploying AI in their operations. AI can be used to improve business performance in areas including predictive maintenance, where its ability to analyse large amounts of high-dimensional data from audio and images can detect anomalies in factory assembly lines or aircraft engines. In logistics, AI can optimise routing of delivery traffic, improving fuel efficiency and reducing delivery times. In call centres, AI has become a valuable tool thanks to improved speech recognition. In sales, many retailers now combine customer demographic and past transaction data with social media monitoring to generate individualised “next product to buy” recommendations.


Yet adoption remains challenging – and for some firms, daunting. The challenges include developing an AI strategy with clearly defined benefits, obtaining and managing the huge datasets that are the lifeblood of AI, and finding talent with the appropriate skill sets. “Surveys we have conducted suggest that only about one in five companies has embedded AI in several parts of their business,” says Jacques Bughin, Brussels-based director of the McKinsey Global Institute. “We can already see a clear pattern of a few early adopters and many others who remain reluctant – and who may find themselves in a race to catch up.”


We expect AI adoption to follow a classic S-curve over the next decade, with a slow start followed by an acceleration, as competitive pressures take hold. Companies that can overcome the adoption challenges will see increased productivity and profitability, and the front-runners stand to benefit from the “winner-takes-most” dynamics we already see with more standard digital technologies. We have estimated that AI front-runners stand to double cash flow by 2030, whereas laggards could lose about 20 per cent of cash flow compared with today’s levels in the same period.


The largest economic impacts of AI will likely be on productivity growth through labour market effects, including substitution, augmentation and contributions to labour productivity. While much of the public debate on AI focuses on its potential effect on jobs, our research suggests that labour substitution could account for less than half of the total economic impact. AI will augment human capabilities, freeing up workers to engage in more productive and higher-value tasks, and increase demand for jobs associated with AI technologies. AI can also boost innovation, enabling companies to reach underserved markets more effectively with existing products and, over the longer term, create entirely new products and services. A simulation we conducted using McKinsey survey data suggests that AI adoption could raise global GDP by as much as $13trillion by 2030, about 1.2 per cent additional GDP growth per year.


The UK, along with some other European countries including Germany, is not at the forefront of AI adoption, lagging behind China and the US in terms of research and investment, but it has a history of driving innovation on a major scale. A boost from AI could help lift the UK economy’s productivity performance, which is at a historic low. “We see a lot of potential for British companies and the UK economy to benefit from AI,” says Nicolaus Henke, global leader of McKinsey Analytics and chairman of Quantum Black.


Critically, business leaders will need to work to upgrade workforce skills as they move to adopt AI, and they will also need to adjust their organisational structures to become more agile. The imperative of creating a more flexible workforce able to work side-by-side with machines will be mirrored by the imperative of creating a more adaptable organisation and people-management system that can react quickly to AI transformations. The challenges are indeed significant – including social challenges in responsible use – but the potential rewards, especially for early and enthusiastic adopters, will likely be sizeable.

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