McKinsey's State of AI 2025: The Gap Between Adoption and Impact

McKinsey just dropped their State of AI 2025 report, surveying nearly 2,000 participants across 105 countries. The headline: almost everyone is using AI now, but almost no one is seeing meaningful enterprise-wide impact.

Three years into the generative AI era, this is the defining tension.

The Adoption-Impact Gap

The numbers tell a clear story:

• 88% of organizations are using AI in at least one business function (up from 78% last year)
• 62% are experimenting with AI agents
• But only 39% report any EBIT impact at the enterprise level
• And most of those say AI accounts for less than 5% of EBIT

Nearly nine in ten companies are using AI. Fewer than four in ten see it on their bottom line.

This isn't a technology problem. It's a deployment problem.

Most Organizations Are Still Piloting

The report's key finding: two-thirds of organizations have not begun scaling AI across the enterprise. They're stuck in experimentation and pilot phases.

Larger companies are ahead—nearly half of organizations with $5B+ in revenue have reached the scaling phase. But for companies under $100M in revenue, it's only 29%.

The pattern is familiar from every previous wave of enterprise technology: early adopters move fast, everyone else gets stuck in "proof of concept" purgatory.

AI Agents: High Curiosity, Low Scale

Agents are the hot topic, and the numbers reflect it:

• 62% are at least experimenting with AI agents
• 23% are scaling agentic AI somewhere in the enterprise
• But in any given function, no more than 10% are scaling agents

Agent adoption is highest in:

• IT and knowledge management (service-desk automation, deep research)
• Tech, media, and telecom industries
• Healthcare

The pattern here is revealing: agents work best where the use case is narrow, repeatable, and the cost of errors is manageable. IT service desks. Document retrieval. Scheduling. The more open-ended the task, the harder it is to scale.

What High Performers Do Differently

McKinsey defines "AI high performers" as those who attribute 5%+ of EBIT to AI and report "significant" value. They represent about 6% of respondents.

What separates them:

1. They Aim for Transformation, Not Just Efficiency

80% of all organizations set efficiency as an AI objective. That's table stakes.

High performers are 3x more likely to say they intend to use AI for transformative change—not just cost cutting.

Companies that set growth and innovation as objectives (not just efficiency) report better outcomes across customer satisfaction, competitive differentiation, profitability, and revenue growth.

2. They Redesign Workflows

This is one of the strongest predictors of impact. High performers are nearly 3x more likely to have fundamentally redesigned workflows when deploying AI.

Most organizations bolt AI onto existing processes. High performers rebuild the process around AI capabilities.

3. They Scale Faster and Wider

High performers use AI in more functions. They're also 3x more likely to be scaling AI agents (not just piloting them).

Three-quarters of high performers have scaled or are scaling AI, compared to one-third of everyone else.

4. Leadership Owns It

High performers are 3x more likely to strongly agree that senior leaders demonstrate ownership and commitment to AI initiatives.

Leaders at these organizations aren't just sponsors—they're role-modeling AI use.

5. They Invest More

More than one-third of high performers spend over 20% of their digital budgets on AI technologies.

You can't pilot your way to transformation.

The Workforce Question

Expectations are mixed:

• 43% expect no change in workforce size due to AI
• 32% expect workforce reductions of 3%+ in the next year
• 13% expect increases of 3%+

What's interesting: larger organizations are more likely to expect reductions. And AI high performers are more likely to expect change—in either direction.

Meanwhile, most organizations are hiring for AI roles—particularly software engineers and data engineers. Even as some functions shrink, the demand for AI talent is growing.

Risk Mitigation Is Improving (Slowly)

The good news: organizations are starting to take AI risks seriously.

• In 2022, organizations were mitigating an average of 2 AI-related risks
• In 2025, that number is 4

The most common consequences experienced:

• Inaccuracy (31% of respondents)
• Followed by explainability issues, privacy concerns, and regulatory compliance

High performers—who have deployed more use cases—are more likely to experience negative consequences, particularly around IP infringement and regulatory compliance. But they're also more likely to have mitigation measures in place.

What This Means

Three takeaways:

1. The Window for Competitive Advantage Is Open

Most organizations are stuck in pilots. The ones moving to production-scale deployment have a real opportunity to pull ahead.

2. Efficiency Isn't Enough

Cost reduction is the default goal. But the organizations seeing the most value are using AI to drive growth and innovation—not just to cut headcount.

3. Workflow Redesign Is the Unlock

Bolting AI onto existing processes produces marginal gains. Redesigning workflows around AI capabilities produces transformational outcomes.

This is the hardest part. It requires not just technology investment, but organizational change, process engineering, and leadership commitment.

The State of AI 2025 confirms what many of us see in practice: AI is everywhere, but enterprise value is still rare. The technology works. The challenge now is organizational.

The gap between "we use AI" and "AI transformed our business" is where the real work happens.