Wait-and-See Could Be a Costly AI Strategy
Early adopters of AI will share a global profit pool valued at $1 trillion. Will your company be among them?
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From the dexterity of Amazon’s Kiva robots to the facial recognition in Apple’s iPhone X, artificial intelligence is increasingly sophisticated and accessible. It also promises to be a rich source of profit uplift — up to 10% of revenue, depending on your industry.
Nevertheless, more than 95% of companies have not embraced AI technology to reinvent how they do business.1 Even though there are many unknowns regarding AI’s capabilities and uses, our research at the McKinsey Global Institute suggests that following a wait-and-see strategy for too much longer could be a costly mistake.
How costly? When we collected more than 400 use cases in 19 industries and simulated the dynamics of AI diffusion (based on current corporate intent to adopt, the technology’s impact on cash flow, and the profit growth linked to adoption), we found significant divergences in the patterns of economic growth between early adopters of AI at scale and non-adopters.2 In the simulation, early diffusers — that is, companies that will use a full suite of AI technologies in the next five years — doubled their normal profits by 2030, bringing in an additional 4% of gross profit growth annually at the expense of their competitors. When we extrapolated this on a global basis, it equated to a shift in corporate profit to early AI diffusers of approximately $1 trillion by 2030, or 10% of the current profit pool.
Competitive Intensity Matters
The corporate diffusion of new technologies typically follows an elongated S curve — slowly rising at the start, steeply climbing in the middle, and then flattening again as the technology becomes commonplace. The curve rises slowly at the start because most companies weigh the option of waiting for the technology to mature against the risk that their rivals will beat them to the punch, and they decide to wait.3
Sometimes waiting can be a winning strategy, but that outcome depends on the intensity of competition. In banking, for instance, a low level of competitive intensity around ATMs — caused by the lack of interoperability payment standards — lessened the risk for late adopters. In contrast, a higher level of competitive intensity around IT systems across industries raised the risks for late adopters.4
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Our surveys suggest that the level of competitive intensity surrounding AI could be intense, accelerating the speed of diffusion.
References
1. These figures are based on the results of two independent surveys — one of 3,000 AI-aware C-suite executives across 10 countries and 10 industries, and one of 1,600 global executives — conducted in 2017 under the auspices of the McKinsey Global Institute and Digital@McKinsey.
2. M. Chui et al., “Notes From the AI Frontier: Insights From Hundreds of Cases,” discussion paper, McKinsey Global institute, April 2018.
3. S.R. Grenadier and A.M. Weiss, “Investment in Technological Innovations: An Option Pricing Approach,” Journal of Financial Economics 44, no. 3 (June 1997): 397-416.
4. S. Dewan, C. Shi, and V. Gurbaxani, “Investigating the Risk-Return Relationship of Information Technology Investment: Firm-Level Empirical Analysis,” Management Science 53, no. 12 (2007): 1829-1842.