Most Organizations Still Pilot AI While High Performers Scale Through Workflow Redesign
McKinsey’s 2025 Global Survey reveals that 88% of organizations now use AI regularly, but two-thirds remain stuck in experimentation or pilot phases. The gap between adoption and enterprise-wide impact persists—only 39% report measurable EBIT effects. Organizations achieving significant value share common traits: They redesign workflows fundamentally, pursue innovation alongside efficiency, and scale faster with senior leadership commitment.
AI Adoption Grows But Scaling Lags
Nearly nine in ten organizations use AI in at least one business function, up from 78% last year. Yet only one-third have begun scaling AI across their enterprises. Larger companies lead: 48% of organizations with over $5 billion in revenue have reached the scaling phase, compared to 29% of those under $100 million.
AI use continues expanding within organizations. Two-thirds now deploy AI across multiple functions, and half use it in three or more areas. IT, marketing and sales, and knowledge management remain the top functions for AI implementation.
AI Agents Emerge in Early Adoption
Sixty-two percent of organizations are experimenting with AI agents—foundation model systems that plan and execute multi-step workflows. However, only 23% have scaled agents in at least one function, and most limit deployment to one or two functions.
Agent adoption concentrates in IT and knowledge management, where use cases like service-desk management and deep research have matured quickly. Technology, media and telecommunications, and healthcare sectors lead in agent deployment.
High Performers Drive Value Through Transformation
Organizations achieving 5% or more EBIT impact from AI—about 6% of respondents—distinguish themselves through specific practices:
They redesign workflows fundamentally. High performers are three times more likely than peers to fundamentally redesign workflows when deploying AI. This practice shows one of the strongest correlations with achieving measurable business impact.
They pursue growth and innovation, not just efficiency. While 80% of all respondents target efficiency gains, high performers add growth or innovation objectives. These organizations report better outcomes across customer satisfaction, competitive differentiation, and revenue growth.
They scale agents faster. High performers are at least three times more likely to scale AI agents across most business functions compared to other organizations.
They secure senior leadership commitment. High performers report strong executive ownership three times more often than peers. Leaders actively drive adoption and model AI use themselves.
They invest more. Over one-third of high performers allocate more than 20% of digital budgets to AI, enabling them to scale technologies across the business.
Enterprise Impact Remains Limited Despite Use-Case Gains
Organizations report benefits at the use-case level but struggle to translate them enterprise-wide. Cost benefits appear most commonly in software engineering, manufacturing, and IT. Revenue increases concentrate in marketing and sales, strategy and corporate finance, and product development.
However, 64% of respondents say AI enables innovation at their organizations, and nearly half report improvements in customer satisfaction and competitive differentiation. These qualitative outcomes suggest AI’s value extends beyond immediate financial metrics.
Workforce Impact Expectations Diverge
Respondents hold mixed views on AI’s effect on workforce size. In the past year, fewer than 20% reported decreases of 3% or more in any function. Looking ahead:
- 32% expect enterprise-wide workforce reductions of 3% or more
- 43% anticipate no change
- 13% predict increases of 3% or more
Larger organizations more often expect workforce reductions, while high performers anticipate significant changes in either direction. Most organizations hired for AI-related roles in the past year, with software engineers and data engineers in highest demand.
Risk Mitigation Efforts Increase
Organizations now mitigate an average of four AI-related risks, up from two in 2022. Mitigation efforts have grown for privacy, explainability, organizational reputation, and regulatory compliance concerns.
Fifty-one percent of respondents report experiencing at least one negative consequence from AI use. Inaccuracy leads as both the most commonly experienced risk (32% of respondents) and most frequently mitigated concern. High performers, deploying twice as many use cases, report more negative consequences—particularly around intellectual property infringement and regulatory compliance—but also protect against more risks.
Six Practices Separate High Performers
High performers consistently implement practices across six dimensions:
- Strategy: Set transformative business goals beyond incremental efficiency
- Talent: Build comprehensive AI talent strategies and hire aggressively
- Operating model: Adopt agile delivery processes enterprise-wide
- Technology: Invest in robust technology infrastructure
- Data: Establish strong data foundations and governance
- Adoption and scaling: Define validation processes and track solution KPIs
Organizations applying these practices capture more value from AI investments and accelerate their journey from pilots to enterprise-wide impact.
The Path Forward
Three years after generative AI triggered a new era of artificial intelligence, most organizations still navigate the transition from experimentation to scaled deployment. The experience of high performers charts a clear path: Treat AI as a catalyst for transformation, redesign workflows to embed it deeply, and pursue innovation alongside efficiency gains. As AI tools improve and organizational capabilities mature, companies that adopt these practices will create competitive advantage through meaningful enterprise-wide impact.