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waiting can be a costly artificial intelligence strategy

Executive Summary: "Wait-and-See Could be a Costly AI Strategy" (MIT SMR)

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waiting can be a costly artificial intelligence strategy

A top challenge for any organization in embracing artificial intelligence is finding a clear business case. We are now seeing compelling research that can quantify the benefit. There is an important article in the MIT Sloan Management Review titled Wait-and-See Could Be a Costly AI Strategy. In it, Jacques Bughin, a senior partner in the Brussels office of the management consulting firm McKinsey & Co, explains how adoption of AI will be dramatically faster from other technical transitions, especially in competitive industries, and how laggards will not be able to achieve cost recovery, putting them at a permanent disadvantage.

Early AI adopters told us that their companies are more focused on using AI for top-line growth than for internal efficiency. On average and across sectors, including retail, transportation, financial services, and manufacturing, the profit growth expectations of early AI adopters were 20% higher than those of their non-adopting peers. The early adopters attributed roughly half of this anticipated growth to pulling business away from their competitors.

Their conclusion is that waiting to start taking advantage of the benefits AI can bring, especially to drive top-line growth, is a losing strategy.

We found significant divergences in the patterns of economic growth between early adopters of AI at scale and non-adopters. 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.

They also address the question of the appropriate pace of investment. The answer depends on the competitive intensity of the industry, the potential for AI returns and the capabilities needed to secure those returns. Their analysis, based on surveys and simulations, finds that companies should be adopting AI at scale within the next three years to optimize their chances of using it to build a platform for profitable growth, and then start applying the full range of AI technologies across the enterprise, including seeking internal efficiencies.

Their recommendations for initial steps forward are:

1) Decisively reject a wait-and-see approach to AI and pursue adoption at scale as soon as possible.

2) Focus on AI applications that yield product and service innovation to capture the technology’s top-line benefits.

3) Complete your digitization efforts, because digitization facilities AI absorption and provides the backbone for AI applications.

The conclusion captures their sentiment succinctly:

“The lion’s share of the competitive advantage and rewards of AI are going to be captured by its early adopters, so time is short.”

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