Companies seeing the highest bottom-line impact from AI exhibit overall organizational strength and engage in a clear set of core best practices.

The results of this year’s McKinsey Global Survey on artificial intelligence (AI) suggest that organizations are using AI as a tool for generating value.

Increasingly, that value is coming in the form of revenues.

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A small contingent of respondents coming from a variety of industries attribute 20 percent or more of their organizations’ earnings before interest and taxes (EBIT) to AI.

These companies plan to invest even more in AI in response to the COVID-19 pandemic and its acceleration of all things digital. This could create a wider divide between AI leaders and the majority of companies still struggling to capitalize on the technology; however, these leaders engage in a number of practices that could offer helpful hints for success. And while companies overall are making some progress in mitigating the risks of AI, most still have a long way to go.

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The companies seeing the most value from their use of AI—that is, respondents who say 20 percent or more of enterprise-wide EBIT in 2019 was attributable to their AI use—report several strengths that set them apart from other respondents:

Better overall performance: The findings suggest that companies seeing more EBIT contribution from AI experience better year-over-year growth overall than do other companies. Respondents at high-performing companies are nearly twice as likely as others to report EBIT growth in 2019 of 10 percent or more.

Respondents at AI high performers are  2.3× more likely than others to consider their C-suite leaders very effective.

Better overall leadership: Respondents at AI high performers rate their C-suite as very effective more often than other respondents do. They also are much more likely than others to say that their AI initiatives have an engaged and knowledgeable champion in the C-suite.

Resource commitment to AI: Responses show that AI high performers invest more of their digital budgets in AI than their counterparts and are more likely to increase their AI investments in the next three years. High performers also tend to have the ability to develop AI solutions in-house—as opposed to purchasing solutions—and they typically employ more AI-related talent, such as data engineers, data architects, and translators, than do their counterparts. They also are much more likely than others to say their companies have built a standardized end-to-end platform for AI-related data science, data engineering, and application development.

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This year we again looked at companies’ AI-related practices, this time looking at about twice as many, to see which might correspond with getting more value from AI.

The organizations with the highest EBIT attributable to AI were more likely to engage in nearly every practice than those seeing less value from AI. The practices generally slot into six categories: strategy; talent and leadership; ways of working; models, tools, and technology; data; and adoption.

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But a few practices are adopted at about the same level by all companies: for example, using test-and-learn methodologies to run rapid iterations in AI initiatives, putting processes in place to capture business feedback, and defining clusters of AI use cases in priority business units, functions, or other areas of business activity.

The survey findings show that some companies using AI are seeing that value accrue to the enterprise level. 22% percent of respondents say that more than 5% of their organizations’ enterprise-wide EBIT in 2019 was attributable to their use of AI, with 48 % reporting less than 5%.