AI Is Everywhere, but Progress Is Slow — McKinsey Explains Why

Just 39% of companies claim that AI has had an enterprise-wide impact. For a lot of leaders, 2025 was meant to be the year that transformed their business using AI. Instead, McKinsey’s latest report demonstrates that most companies are still spinning their wheels with experiments and can’t scale AI in a meaningful way across the organization.

The tools are there. The impact? Remains elusive — particularly on the bottom line.

But the report also conveys a clear message: The companies who are thriving in the AI era are doing much more than building models. They’re building systems. They’re aggregating data, governance and workflows into AI-ready infrastructure that delivers at scale. If you’re stuck in pilot purgatory, this is your roadmap out.

That roadmap starts with a harsh reality: almost two-thirds of companies (63%) are still in the pilot phase. The McKinsey survey reveals that although AI has been tested across functions, it is seldom deployed across the enterprise. There may be a chatbot in customer service or a forecasting model in supply chain, but they’re disconnected, fragile, and far from scaled.

Given the hype around agentic AI, it’s not surprising that 62% of companies report that they’re experimenting with AI agents — the kind built to act, not merely recommend. However, that leap sounds risky, but it’s even riskier without a solid base beneath it. Agents require AI-ready data. Most organizations simply aren’t there yet. McKinsey points out that without reliable infrastructure and governance, early AI agent deployments are likely to hit performance and trust issues.

Use of AI (Source: McKinsey)

“Looking across the entire enterprise landscape, the use of agents is not yet widespread,” noted Lareina Yee, chair of the McKinsey Technology Council. “This gap highlights the contrast between the great potential that manifests in a ‘hype cycle’ and the current reality on the ground: For those companies that respondents say have started to use agents in any particular business function, most of them are still in the exploratory stages.”

Even as the broader stall continues, signs of real traction are starting to show. In parts of the enterprise like procurement, marketing, and supply chain, AI is quietly getting things done. Teams are using models to negotiate better prices, tailor outreach, and forecast demand with more accuracy. These aren’t just pilot runs or one-off tests anymore — they’re working systems producing real and measurable results.

McKinsey’s data backs this up. Sixty‑four percent report that AI is already driving innovation in their companies, but the impact of those gains is limited in scope. It’s not a question of whether AI works or if the early successes can be generalized beyond specific functions and scaled across the enterprise. Until that happens, even the most promising wins will remain isolated pockets of potential.

AI is still seen as a cost-cutting exercise by most companies. McKinsey’s survey found that 80% of respondents prioritized efficiency as the key objective. It’s a pragmatic focus — though not the one that creates breakthrough results.

The top-performing companies see things more broadly. They want AI for growth, not just cost optimization. That could mean using it to test new products, customize customer experiences or reach markets that were previously out of range. Those use cases require more alignment across teams and a firmer data foundation, but they also create more value. Efficiency can improve margins. Growth and new ideas transform what the business is able to accomplish.

McKinsey’s Tara Balakrishnan sees the real differentiator not in tools or talent, but in mindset. As she explains that “Often, organizations approach AI through a cost-first mindset. While many see leading indicators from efficiency gains, focusing only on cost can limit AI’s impact. Positioning AI as an enabler of growth and innovation creates space within the organization to go after the cost and efficiency improvements more effectively.

AI Budget Allocation (Source: McKinsey)

“And for many organizations, an efficiency play will not be sufficient to navigate AI disruptio,’ continued Balakrishnan. “They will need to consider how AI can be leveraged to tell a transformational story to their stakeholders. Doing so also supports change management internally. Employees tend to rally behind a shared vision of opportunity. In our experience, many of the organizations that use AI to inspire growth and innovation are the same ones that find it easier to scale AI use and ultimately realize sustainable productivity improvements.”

The McKinsey survey shows one clear signal of progress — workflow redesign. The companies with more success in using effectively using AI, about half say they’re using technology not just to do things faster, but to essentially rewrite how their business works. Most are actively rebuilding how work moves between people and systems. It’s a shift from fine-tuning the old way to creating something new entirely—and that’s what separates real transformation from just minor upgrades.

When it comes to the workforce, the situation is less clear. A third of leaders expect reductions, however 43% foresee little change, and only a smaller slice predict growth. What McKinsey is saying is that AI’s effect on jobs depends on how companies use it. The ones pairing automation with smart redesign and reskilling are more likely to keep or even grow headcount. Bottom line: if AI changes the work, you need to change how the work is done.

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