AI adoption advances, but foundational barriers remain

| Survey

The adoption of artificial intelligence (AI) is rapidly taking hold across global business, according to a new McKinsey Global Survey on the topic.1 AI, typically defined as the ability of a machine to perform cognitive functions associated with human minds (such as perceiving, reasoning, learning, and problem solving), includes a range of capabilities that enable AI to solve business problems. The survey asked about nine in particular,2 and nearly half of respondents say their organizations have embedded at least one into their standard business processes, while another 30 percent report piloting the use of AI. Yet overall, the business world is just beginning to harness these technologies and their benefits. Most respondents whose companies have deployed AI in a specific function report achieving moderate or significant value from that use, but only 21 percent of respondents report embedding AI into multiple business units or functions. Indeed, many organizations still lack the foundational practices to create value from AI at scale—for example, mapping where their AI opportunities lie and having clear strategies for sourcing the data that AI requires.

One critical factor of using AI effectively, the results confirm, is an organization’s progress on transforming the core parts of its business through digitization. At the most digitized firms,3 respondents report higher rates of AI usage in more business functions than their peers, along with greater investment in AI and greater overall value from using AI. Another foundational challenge with AI is finding skilled people to implement it effectively. Many respondents say their organizations are addressing the issue by taking a diversified approach to sourcing talent. On the whole, despite reasonable concerns about AI being used to automate existing work, respondents tend to believe that AI will have only a minor effect on overall company head count in the coming years.

Adopting, deploying, and applying AI

How adoption of AI is progressing

The results suggest that most organizations have already begun to adopt AI in their businesses. Forty-seven percent of respondents say their companies have embedded at least one AI capability in their business processes—compared with 20 percent of respondents in a 2017 study who said their companies were using AI in a core part of their business or at scale4Artificial intelligence: The next digital frontier?,” McKinsey Global Institute, June 2017.—and another 30 percent say they are piloting AI. Still, there remains a lot more potential to use AI across the enterprise; as our previous research has shown, AI opportunities exist in every sector and business function. Just 21 percent of respondents say their organizations have embedded AI in several parts of the business, and so far, investments in AI are a relatively small fraction of companies’ overall spending on digital technologies. A majority of respondents (58 percent) say less than one-tenth of their companies’ digital budgets goes toward AI—though respondents overwhelmingly expect AI investments will increase in the coming years (71 percent say so).

Which AI capabilities have been deployed

Of the nine capabilities we asked about, robotic process automation, computer vision, and machine learning are most commonly deployed. For each of these, at least 20 percent of respondents say their companies have already embedded these technologies into their business processes. Physical robotics and autonomous vehicles are the least commonly deployed, largely because they are relevant only to companies in industries where there’s a clear application; in those sectors, respondents report the outsize use of the capabilities. For example, half of respondents in automotive and assembly (compared with 16 percent of the total average) say physical robotics are embedded in at least one function or business unit.

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Where AI is being used

By sector, telecom, high-tech, and financial-services firms are leading the way in overall adoption. That said, looking across sectors and functions, the results suggest that companies are generally following the money when deploying AI, which seems to be gaining the most traction in the areas of the business that create the most value within a given industry (Exhibit 1). In retail, for example, the use of AI in marketing and sales processes is most common: 52 percent of retail respondents say they are using AI in marketing and sales, compared with 29 percent of all respondents.

AI seems to be gaining the most traction in the areas of the business that create the most value within a given industry.

Where AI is creating value

And while the adoption of AI is still in its early days, the results suggest that it’s already reaping meaningful rewards. When respondents were asked about the value captured in business functions where they have deployed AI, only 1 percent say they have seen no or negative value from that use—compared with 41 percent reporting significant value and 37 percent reporting moderate value.5 Across business functions, respondents using AI in manufacturing and risk indicate they are seeing the greatest value (Exhibit 2). More than half of respondents report significant value from using AI in these processes, compared with 35 percent of respondents who report significant business value from using AI in marketing and sales.6

Across functions, respondents report that the most significant benefits come from adopting AI in manufacturing and in risk.

The enablers and challenges of AI

To take advantage of AI’s enormous potential, the results confirm, most organizations have a long way to go in developing the core practices that enable them to realize the potential value at scale (Exhibit 3). Just 17 percent of respondents say their companies have mapped out where, across the organization, all potential AI opportunities lie. And only 18 percent say their companies have a clear strategy in place for sourcing the data that enable AI work. Indeed, nearly one-quarter of respondents say their companies have not developed any of the 11 practices we asked about.

Few organizations have adopted the core practices that would enable them to realize AI’s potential value at scale.

When asked about the biggest challenges to AI adoption, respondents indicate that the most common barrier is also strategy related. They most often cite a lack of a clear AI strategy (Exhibit 4), followed by a lack of appropriate talent, functional silos that constrain end-to-end AI solutions, and a lack of leaders who demonstrate ownership of and commitment to AI.

The most frequently cited barriers to AI adoption are a lack of a clear strategy, a lack of talent, and functional silos.

One critical enabler of AI is a company’s progress on its digitization journey. The organizations that have made the most progress in digitizing core business processes are also on the leading edge of AI adoption. At the most digitized firms, 67 percent of respondents say their organizations have embedded AI into standard business processes, compared with 43 percent at all other companies. They are most likely to have adopted machine learning, for example: 39 percent say it is embedded in their processes, compared with 16 percent at all other companies (Exhibit 5).

Respondents at the most digitized organizations report greater adoption of AI capabilities than their peers at other companies.

The most digitized organizations have also deployed AI in more functions than other companies, though both groups say AI is most commonly used in service operations and in product development. These companies are also investing much more in AI: 19 percent at the most digitized companies say more than one-fifth of their overall digitization spending goes toward AI, while just 8 percent of other respondents say the same. On average, 52 percent of respondents at these firms report significant value from using AI, compared with 38 percent of all others.

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Read our latest thinking on AI adoption

And while several of the barriers to AI adoption that we asked about are much less pressing for digitized companies (only 27 percent cite a lack of AI strategy, compared with 46 percent of all others), respondents at these companies are just as likely as their peers to say it’s hard to find the right talent for AI. In fact, talent is the biggest challenge for the most digitized organizations, cited by 41 percent of those respondents.

What of the workforce?

AI raises two major questions about companies’ workforces: Where will we find the knowledgeable talent to deploy AI? And to what degree will AI’s ability to automate activities that we pay workers to do affect the size of the workforce?

With talent being one of the biggest challenges to AI, no matter how advanced a company’s digital program, it’s perhaps not surprising that companies are leaving no stone unturned when sourcing people and skills. Most commonly, respondents say their organizations are taking an “all of the above” approach: hiring external talent, building capabilities in-house, and buying or licensing capabilities from large technology firms. Across industries, even the ones leading the way in AI adoption (that is, those in telecom, high tech, and financial services) report a mix of internal and external sourcing—though they are more focused than others on developing their own AI capabilities. Respondents in these sectors are more likely than average to say they’re building in-house AI capabilities, which requires internal talent with the right skills. In high tech and financial services, respondents are also much likelier to report retraining or upskilling. The same is true of the most digitized companies: respondents are more likely than others to report in-house development of AI capabilities and retraining or upskilling of current employees (Exhibit 6).

At the most digitized companies, respondents are more likely to say AI capabilities are built in-house and employees are retrained.

At the same time, the most digitized companies have done more than others to automate human labor via AI. By function, respondents report that their processes for customer service, IT, and service operations are most commonly automated—and the digitized companies are further along than their peers in automating all three. Yet respondents at these organizations, and overall, tend to expect that AI’s future effects on total head count will be minor or positive. A plurality of respondents say that, three years from now, AI won’t really affect the number of employees at their companies. Among the most digitized companies, respondents are more likely to expect head count will increase than decrease. They are also more optimistic than others that their workforces will grow: 31 percent say so, compared with 18 percent of their peers. These results, along with those from other McKinsey research, suggest that AI’s biggest effect on the workforce could be changes in the work that people do, particularly ever-greater collaboration between machines and people, rather than overall workforce reductions.

Looking ahead

The survey results suggest that digitization and certain foundational practices are critical to creating value from AI at scale. Here are several steps companies can take to capitalize on AI’s potential:

  • Make progress on your digital journey. The results confirm that digitization is a prerequisite and critical enabler for deriving value from AI. The implications of continuing digitization are significant; for many companies, they involve transformation-level changes to the very business processes at the core of the enterprise and new ways in which people will work. But without a strong digital backbone, a company’s AI systems will lack the training data necessary to build better models and the ability to transform superior AI insights into behavioral changes at scale.
  • Scale AI’s impact across the enterprise. While most companies have already deployed AI to some extent, few have embedded it into standard operating processes in multiple business units or functions, and about one-third are only piloting the use of AI. While AI is still in its early days, getting stuck in “pilot purgatory” is a real risk.7Ten red flags signaling your analytics program will fail,” May 2018. Achieving results at scale requires not only the diffusion of these capabilities across the enterprise but also a real understanding and commitment on the part of leaders to drive large-scale change, as well as a focus on change management rather than on technology alone.
  • Put key enablers in place. While the adoption of AI is happening fast, the survey suggests that organizations tend to lack many of the foundational enablers required to derive value from AI at scale. These enablers include top-management sponsorship, development of an enterprise-wide portfolio view of AI opportunities, action to close talent gaps, and the implementation of a sophisticated data strategy—all of which require more strategic thinking around AI programs and agendas. Business and technology leaders must work quickly to establish key AI enablers. Otherwise, they risk missing out on the current—and future—AI opportunity.
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