New research from McKinsey’s 2025 State of AI survey indicates that while 88% of organizations are employing artificial intelligence in at least one business function, only a third have successfully scaled these implementations to derive enterprise-wide value. This disparity highlights that, despite AI’s prevalence across industrial sectors, many organizations are still grappling with how to transition from pilot projects to meaningful financial returns.
The survey reveals that nearly two-thirds of companies remain in the experimentation or pilot stages of AI deployment. Only 39% of respondents reported that AI has influenced their company’s earnings, with most indicating an impact of less than 5% on earnings before interest and taxes (EBIT). The authors of the survey noted, “The transition from pilots to scaled impact remains a work in progress at most organizations.”
A notable trend is the increasing adoption of AI agents, which are autonomous systems capable of managing complex workflows without continuous human oversight. Approximately 23% of companies are in the process of scaling these AI agents, while an additional 39% are piloting such systems. The IT departments and knowledge management functions are at the forefront of this adoption, particularly in service desk management and research tasks. The technology, media, telecommunications, and healthcare sectors report the highest usage, while industries like manufacturing and energy are adopting more cautiously.
Enterprise size plays a significant role in the success of AI scaling. Nearly half of organizations with revenues exceeding $5 billion have progressed to the AI scaling phase, compared to only 29% of companies with revenues below $100 million. This trend underscores that larger organizations typically have greater resources to invest in AI infrastructure and digital transformation, as well as a higher tolerance for the trial-and-error process associated with integrating new technology across business units.
Among survey respondents, around 6% qualify as “AI high performers,” companies that report at least a 5% EBIT impact attributable to AI. These high performers adopt distinct strategies that set them apart. Firstly, they have a broader vision for AI transformation—being three times more likely to use AI for transformative rather than incremental changes. While 80% of all respondents cite efficiency as a key goal, high performers also focus on growth and innovation.
Secondly, these organizations prioritize redesigning workflows instead of merely integrating AI into existing processes. They are nearly three times more likely to fundamentally alter how work is conducted when adopting AI solutions. This deliberate approach correlates strongly with achieving substantial business impact. Furthermore, leadership commitment is crucial; high performers report significantly greater executive ownership of AI initiatives.
Investment in AI capabilities also differentiates high performers. Over one-third allocate more than 20% of their digital budgets to AI technologies. Approximately 75% of these companies are in the process of scaling AI across their enterprises, compared to only one-third of their peers.
Despite limited enterprise-wide financial impact from AI overall, companies are recognizing returns in specific domains. Cost benefits are most prevalent in software engineering, manufacturing, and IT functions, while revenue growth is primarily seen in marketing, sales, strategy, and product development. The majority of companies report enhanced innovation, and nearly half attribute improvements in customer satisfaction and competitive differentiation to AI-driven solutions.
Reactions regarding AI’s influence on employment remain varied. Most respondents indicate little to no change in headcount within functions leveraging AI over the past year. However, outlooks for the upcoming year differ, with 32% projecting reductions of 3% or more, while 13% anticipate workforce increases. Concurrently, firms have actively recruited for AI-related roles, particularly for software engineers and data engineers, suggesting that AI is reshaping the skillsets required for digital transformation.
As organizations implement more AI solutions, they are increasingly aware of associated risks. The survey indicates that 51% of respondents have experienced at least one negative outcome from AI, with inaccuracies being the most frequently reported issue. Companies now mitigate an average of four AI-related risks, up from two in 2022. High performers report more negative consequences, likely due to their broader AI deployment, but they also adopt more comprehensive strategies to address risks related to data privacy, regulatory compliance, and explainability.
For companies in sectors such as refining, petrochemicals, manufacturing, and construction, the findings underscore the need for cautious and well-directed AI implementation. Artificial intelligence is not a panacea; extracting genuine value requires serious commitment, workflow reconfiguration, and proactive executive leadership. The path to realizing AI’s potential is not necessarily about cutting-edge technology but rather about leveraging AI as a catalyst for broader business transformation.
As the generative AI era continues to evolve, a significant portion of the opportunity lies ahead. The pressing question is which organizations will successfully transition from AI experimentation to creating enterprise value, and which will continue to find themselves mired in pilot projects in the years to come.
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