Our survey analyses show that a CEO’s oversight of AI governance—that is, the policies, processes, and technology necessary to develop and deploy AI systems responsibly—is one element most correlated with higher self-reported bottom-line impact from an organization’s gen AI use. (View Highlight)
That’s particularly true at larger companies, where CEO oversight is the element with the most impact on EBIT attributable to gen AI. Twenty-eight percent of respondents whose organizations use AI report that their CEO is responsible for overseeing AI governance, though the share is smaller at larger organizations with $500 million or more in annual revenues, and 17 percent say AI governance is overseen by their board of directors. In many cases, AI governance is jointly owned: On average, respondents report that two leaders are in charge. (View Highlight)
The value of AI comes from rewiring how companies run, and the latest survey shows that, out of 25 attributes tested for organizations of all sizes, the redesign of workflows has the biggest effect on an organization’s ability to see EBIT impact from its use of gen AI. (View Highlight)
Organizations are beginning to reshape their workflows as they deploy gen AI. Twenty-one percent of respondents reporting gen AI use by their organizations say their organizations have fundamentally redesigned at least some workflows. (View Highlight)
The more we see organizations using AI, the more we recognize that it takes a top- down process to really move the needle. Effective AI implementation starts with a fully committed C-suite and, ideally, an engaged board. Many companies’ instinct is to delegate implementation to the IT or digital department, but over and over again, this turns out to be a recipe for failure. (View Highlight)
There are several reasons for this. The first is that getting real value out of AI requires transformation, not just new technology. It’s a question of successful change management and mobilization, which is why C-suite leadership is essential. (View Highlight)
It’s also a potentially expensive transformation, requiring intensive use of sometimes scarce resources and talent. A lot rides on how those resources are made available, and that’s an executive-level call requiring nuanced decision-making that reflects the balance organizations must strike between efficient resource use and broad empowerment—a balance that must be constantly reevaluated as the technology and organization evolve. (View Highlight)
As organizations become more fluent with AI, it will essentially become embedded in all functions, leaving leadership to focus on higher-level tasks like impact monitoring and talent development rather than on implementation. (View Highlight)
Some essential elements for deploying AI tend to be fully or partially centralized (Exhibit 1). For risk and compliance, as well as data governance, organizations often use a fully centralized model such as a center of excellence. For tech talent and adoption of AI solutions, on the other hand, respondents most often report using a hybrid or partially centralized model, with some resources handled centrally and others distributed across functions or business units—though respondents at organizations with less than $500 million in annual revenues are more likely than others to report fully centralizing these elements. (View Highlight)
Twenty-seven percent of respondents say employees at their organizations review all content created by gen AI before it is used, and a similar share says that 20 percent or less of gen- AI-produced content is checked. (View Highlight)
Organizations vary widely in how they monitor gen AI outputs Organizations have employees overseeing the quality of gen AI outputs, though the extent of that oversight varies widely. Twenty-seven percent of respondents whose organizations use gen AI say that employees review all content created by gen AI before it is used—for example, before a customer sees a chatbot’s response or before an AI-generated image is used in marketing materials (Exhibit 2). A similar share says that 20 percent or less of gen- AI-produced content is checked before use. (View Highlight)
Many organizations are ramping up their efforts to mitigate gen-AI-related risks. Respondents are more likely than in early 2024 to say their organizations are actively managing risks related to inaccuracy, cybersecurity, and intellectual property infringement (Exhibit 3)—three of the gen- AI-related risks that respondents most commonly say have caused negative consequences for their organizations.2 (View Highlight)
We’ve learned a lot about generative AI over the past two years. But perhaps the most important lesson is this: It pays to think big. The organizations that are building a genuine and lasting competitive advantage from their AI efforts are the ones that are thinking in terms of wholesale transformative change that stands to alter their business models, cost structures, and revenue streams—rather than proceeding incrementally. (View Highlight)
Our experience helping organizations create and deploy gen AI systems also shows that it pays to be ambitious from the outset—pursuing end-to-end solutions to transform entire domains, rather than taking a piecemeal, use-case-by-use-case approach (View Highlight)
Beginning with an overarching, enterprise-level transformative vision opens up possibilities down the line. That’s because a clear picture of where you’re going influences the data you capture and the models you build. You’re thinking about things like access control; security; reusability of code at the front end, not as an afterthought; and creating a foundational infrastructure that is well beyond any individual use case or domain. This allows further functionality to be deployed faster and more cheaply than if you go use case by use case—which, in turn, becomes a competitive advantage that others will have a hard time keeping up with. (View Highlight)
Transformative thinking also forces the CEO and top team to be aligned—something that use case thinking does not. This is critical because successful transformations require siloed parts of the enterprise to come together in a single orchestrated effort—and that can typically only happen when the CEO and other top leaders are involved. (View Highlight)
Most respondents have yet to see organization-wide, bottom-line impact from gen AI use— and most aren’t yet implementing the adoption and scaling practices that we know from earlier research help create value when deploying new technologies. (View Highlight)
The one with the most impact on the bottom line is tracking well-defined KPIs for gen AI solutions, while at larger organizations, establishing a clearly defined road map to drive adoption of gen AI also has one of the biggest impacts. (View Highlight)
Overall, companies are in the early stages of putting these practices in place. So far, less than one-third of respondents report that their organizations are following most of the 12 adoption and scaling practices, with less than one in five saying their organizations are tracking KPIs for gen AI solutions. (View Highlight)
Responses show larger organizations are also ahead on building awareness and momentum through internal communications about the value created by gen AI solutions, creating role-based capability training courses to make sure employees at each level know how to use gen AI capabilities appropriately, and having comprehensive approaches to foster trust among customers in their use of gen AI. (View Highlight)
The initial wave of excitement and novelty around generative AI is evolving into an intentional focus on how to create value from these technologies. Executives are rightfully looking for a return on their AI investments; in many cases, they are paring back their strategies from trying to apply gen AI everywhere to prioritizing the domains that have the greatest potential. (View Highlight)
We’re now far enough into the gen AI era to see patterns among companies that are capturing value. One significant difference is that these companies focus as much on driving adoption and scaling as they do on the up-front technology development. This is not just hand-waving. Instead, they are following specific management practices that enable them to be successful—such as developing a clear road map for scaling, establishing and tracking KPIs, and driving change management by ensuring senior leaders are actively engaged in driving gen AI adoption. The fact that so many companies continue to struggle with these management practices is a testament to the fact that they’re not so simple to get right. (View Highlight)
This survey also examines the state of AI-related hiring and other ways AI affects the workforce. Respondents working for organizations that use AI are about as likely as they were in the early 2024 survey to say their organizations hired individuals for AI-related roles in the past 12 months. (View Highlight)
The only roles that differ this year are data-visualization and design specialists, which respondents are significantly less likely than in the previous survey to report hiring. The findings also indicate several new risk-related roles that are becoming part of organizations’ AI deployment processes. Thirteen percent of respondents say their organizations have hired AI compliance specialists, and 6 percent report hiring AI ethics specialists. Respondents at larger companies are more likely than their peers at smaller organizations to report hiring a broad range of AI-related roles, with the largest gaps seen in hiring AI data scientists, machine learning engineers, and data engineers. (View Highlight)
Many respondents also say that their organizations have reskilled portions of their workforces as part of their AI deployment over the past year and that they expect to undertake more reskilling in the years ahead (View Highlight)
Our latest survey also shows how organizations are managing the time saved by their deployment of gen AI. Respondents most often report that employees are spending the time saved via automation on entirely new activities. They also often say that employees are spending more time on existing responsibilities that have not been automated. Respondents at larger organizations, however, are more likely than others to say their organizations have reduced the number of employees as a result of time saved. Our analyses find that head count reductions are one of the organizational attributes with the largest impact on bottom- line value realized from gen AI. (View Highlight)
Overall, though, a plurality of respondents (38 percent) whose organizations use AI predict that use of gen AI will have little effect on the size of their organization’s workforce in the next three years. (View Highlight)
survey respondents working in financial services are the only ones much more likely to expect a workforce reduction than no change. The findings show that C-level executives’ expectations for the workforce impact of gen AI are not significantly different from those of senior managers and midlevel managers. That said, when it comes to the head count impact of AI—including gen AI and analytical AI—C-level executives are more likely than middle managers to predict increasing head count. (View Highlight)
Looking at the expected effects of gen AI deployment by business function, respondents most often predict decreasing head count in service operations, such as customer care and field services, as well as in supply chain and inventory management (Exhibit 7). In IT and product development, however, respondents are more likely to expect increasing than decreasing head count. (View Highlight)
Although we remain in the early stages of gen AI, we’re beginning to get a glimpse into the ways the technology is affecting the workforce. A (View Highlight)
In fact, a plurality of respondents anticipate no immediate change to the size of their workforces. And while respondents expect lower head counts in some functions—such as service operations and supply chain/inventory management—in other functions—including software engineering and product development—respondents are actually anticipating an increase in the number of employees. (View Highlight)
Meantime, the difficulty of finding AI talent, while still considerable, is beginning to ease. Perhaps more people are taking the initiative to enhance their own capabilities. Or it could be that corporate investments in upskilling are beginning to bear fruit. Both of these somewhat counterintuitive trends serve to reinforce the fact that we are still in the early days of the AI revolution—the long-term workforce effects are still only beginning to take shape. (View Highlight)
The use of gen AI has seen a similar jump since early 2024: 71 percent of respondents say their organizations regularly use gen AI in at least one business function, up from 65 percent in early 2024.4 (View Highlight)
Responses show that organizations are most often using gen AI in marketing and sales, product and service development, service operations, and software engineering—business functions where gen AI deployment would likely generate the most value, according to previous McKinsey research—as well as in IT. (View Highlight)
Organizations are applying the technology where it can generate the most value—for example, service operations for media and telecommunication companies, software engineering for technology companies, and knowledge management for professional- services organizations.5 Gen AI deployment also varies by company size. Responses show that companies with more than $500 million in annual revenues are using gen AI throughout more of their organizations than smaller companies are. (View Highlight)
Individual use of gen AI by our respondents also increased significantly in 2024, with C-level executives leading the way (exhibit). Fifty-three percent of surveyed executives say they are regularly using gen AI at work, compared with 44 percent of midlevel managers. While we see variation in individuals’ use of gen AI across industries and regions, the data largely show widening use across the board. (View Highlight)
