Eighty-eight percent of organizations now use AI in at least one business function. That number sounds impressive until you examine what it actually means.
According to McKinsey's 2025 State of AI survey—based on responses from nearly 2,000 participants across 105 countries—only 6% of organizations are capturing meaningful enterprise-level value from their AI investments. The rest are stuck in what we might call the "adoption paradox": using AI without transforming with it.
This gap between adoption and impact represents one of the most significant challenges facing business leaders today.
Understanding why it exists—and what the 6% do differently—offers a roadmap for organizations serious about AI transformation.
The Adoption Paradox
The numbers tell a story of widespread activity but limited results.
While 88% of organizations use AI (up from 78% last year), nearly two-thirds remain in the experimentation or piloting phases. Only about one-third have begun scaling AI across their enterprises. And critically, just 39% of respondents attribute any EBIT impact to AI use—with most of those reporting less than 5% of their organization's EBIT stemming from AI.
This creates an uncomfortable reality: Most organizations have invested significant resources into AI initiatives that have yet to materially impact their bottom line.
The gap is particularly stark when examining where organizations are in their AI journey:
- 32% are still experimenting with AI technologies
- 30% are in pilot phases testing specific use cases
- 31% report scaling AI across the enterprise
- Only 6% qualify as "high performers" capturing significant value
The message is clear: Activity does not equal impact. Having AI is not the same as transforming with AI.
What the 6% Do Differently
McKinsey defines AI high performers as organizations that attribute 5% or more of EBIT to AI use and report "significant" value from their AI initiatives. Analyzing what distinguishes this group reveals five critical differentiators.
1. They Pursue Transformative Ambition, Not Incremental Efficiency
High performers are more than three times more likely than others to say their organization intends to use AI for transformative change rather than incremental improvement.
While 80% of all respondents say their companies set efficiency as an AI objective, high performers consistently add growth and innovation as explicit goals. This distinction matters: Organizations that use AI to pursue growth and innovation—not just cost reduction—are significantly more likely to report improvements in customer satisfaction, competitive differentiation, profitability, and revenue growth.
The implication is strategic. If your AI agenda is purely about "doing the same things cheaper," you're likely missing the larger opportunity.
2. They Redesign Workflows—They Don't Just Add AI to Existing Processes
This is perhaps the most important finding in the entire report.
High performers are nearly three times as likely as others to have fundamentally redesigned their workflows around AI. Of all the factors McKinsey tested, workflow redesign showed one of the strongest correlations with achieving meaningful business impact.
Most organizations make a critical mistake: They layer AI on top of existing processes, essentially using sophisticated technology to automate the status quo. High performers take a different approach—they break work down into component tasks, determine which are best performed by AI versus humans, and reconstruct workflows accordingly.
This requires asking fundamentally different questions. Instead of "How can AI help us do this faster?" the question becomes "If we were designing this process from scratch with AI capabilities available, what would it look like?"
3. They Deploy AI Broadly, Not in Isolated Pockets
High performers use AI in more business functions than their peers. They are significantly more likely to report AI use in marketing and sales, strategy and corporate finance, and product and service development.
This breadth matters for two reasons. First, AI's potential for impact increases when it can optimize across functions rather than within siloed processes. Second, broader deployment accelerates organizational learning—teams develop capabilities faster when AI use is normalized across the enterprise rather than confined to a few innovation labs.
4. Their Leaders Actively Champion AI Initiatives
High performers are three times more likely to strongly agree that senior leaders demonstrate ownership of and commitment to their AI initiatives.
This goes beyond executive sponsorship. High-performing organizations report that their senior leaders are actively engaged in driving AI adoption, including role modeling the use of AI themselves. CEOs at these organizations are increasingly taking direct responsibility: 28% of respondents whose organizations use AI report that their CEO is responsible for overseeing AI governance.
Leadership commitment correlates with action. Organizations with engaged leaders are more likely to invest appropriately, remove organizational barriers, and sustain focus through the inevitable challenges of transformation.
5. They Invest Significantly More
More than one-third of high performers allocate over 20% of their digital budgets to AI technologies—compared with just 7% of other organizations.
This investment gap reflects a strategic choice. High performers treat AI as a core capability requiring sustained investment, while most organizations fund AI as a series of discrete projects. The compounding effect of sustained investment—in talent, infrastructure, and organizational capability—creates widening gaps over time.
The Agentic AI Frontier
Beyond current AI adoption, the survey reveals a new frontier: AI agents. These systems, based on foundation models and capable of planning and executing multiple steps in a workflow, represent the next evolution of enterprise AI.
Twenty-three percent of respondents report their organizations are scaling agentic AI in at least one function, with an additional 39% experimenting. But widespread deployment remains limited—in any given business function, no more than 10% of respondents say their organizations are scaling AI agents.
Current adoption patterns suggest where agentic AI is gaining traction:
- IT and knowledge management lead adoption, with use cases like service-desk management and deep research
- Technology, media, telecommunications, and healthcare show the highest industry adoption rates
- High performers are at least three times more likely than peers to be scaling agents across most business functions
This matters because agentic AI represents a step-change in capability. While current AI tools primarily augment human work, agents can autonomously execute complex, multi-step processes. Organizations that develop capabilities in agentic AI now will likely compound their advantages as the technology matures.
The Workforce Question
AI's impact on employment remains uncertain, with respondents offering differing perspectives.
Looking ahead to the next year:
- 43% expect little or no change in overall workforce size
- 32% expect reductions of 3% or more
- 13% expect increases of 3% or more
The pattern varies by organization size and AI maturity. Larger organizations are more likely to expect workforce reductions, while AI high performers are more likely to expect meaningful change in either direction—both reductions and increases.
What is clear: Demand for AI-related skills continues to rise. Most respondents—particularly those from larger companies—report hiring for AI-related roles over the past year, with software engineers and data engineers most in demand.
A Framework for Action
The McKinsey findings suggest a clear path forward for organizations serious about capturing value from AI. Based on the practices that distinguish high performers, we recommend executives focus on five priorities:
Elevate Your Ambition
Audit your current AI objectives. If they center exclusively on efficiency, you're likely underinvesting in transformation. Add explicit growth and innovation goals, and ensure leadership understands that AI's greatest potential lies in business model evolution, not just cost reduction.
Redesign Before You Deploy
Before implementing AI in any function, conduct a workflow redesign exercise. Map existing processes, identify the component tasks, and determine optimal human-AI allocation. Resist the temptation to simply "add AI" to existing processes—this is the single biggest differentiator between high performers and everyone else.
Commit Leadership Attention
AI transformation requires senior leadership commitment that goes beyond sponsorship. Leaders should role model AI use, take direct accountability for AI governance, and maintain visible engagement with AI initiatives. Consider whether your CEO should take direct responsibility for AI governance—28% of organizations have already made this choice.
Scale Investment
Benchmark your AI investment against the high-performer standard: more than 20% of digital budget. If you're significantly below this threshold, you may be underfunding your AI ambitions. Remember that sustained investment compounds—organizations that invest consistently will pull away from those that fund AI opportunistically.
Experiment with Agents
Begin developing capabilities in agentic AI now, even if through limited pilots. The organizations building experience with AI agents today will be better positioned to capture value as the technology matures and deployment becomes more widespread.
The Bottom Line
McKinsey's 2025 State of AI report reveals a market in transition. AI adoption is now nearly universal, but AI transformation remains rare. The 6% of organizations capturing meaningful value aren't using different technologies—they're deploying AI with different intentions, different organizational approaches, and different levels of commitment.
The gap between adoption and transformation represents both a challenge and an opportunity. Organizations that move from using AI to transforming with AI will capture disproportionate value in the years ahead. Those that remain in perpetual pilot mode will find themselves increasingly disadvantaged.
The question for every leadership team is no longer whether to use AI. It's whether you're willing to do what the 6% do: pursue transformative ambition, redesign workflows, deploy broadly, commit leadership attention, and invest at scale.
The tools are available. The path is clear. The only remaining question is commitment.
At OuterEdge, we help organizations move from AI experimentation to enterprise transformation. Our approach mirrors what McKinsey's research validates: we redesign workflows, not just add AI to existing processes. If you're ready to join the 6%, book a strategy call to discuss your AI transformation journey.