Most teams feel the tension right now. AI shows up everywhere in headlines and product updates, yet the meaningful lift everyone anticipates still feels just out of reach. You see pockets of progress. You hear success stories. You watch demos that seem to glide. Then you look at your own operations and wonder why the shift feels slower in practice.
Studies help answer that question. Some offer quick clarity. Others linger and reshape how you interpret the moment we are in. The latest State of AI report from McKinsey sits in that second category.
It gives us a closer look at what organizations say about their real progress with AI and where the gap between adoption and impact continues to sit.
The study behind the findings
The study comes from McKinsey’s QuantumBlack team, a group that works at the intersection of analytics, strategy and organizational transformation. Their annual State of AI report gathers responses from leaders around the world to understand where AI delivers value, where it stalls and where momentum begins to build.
This year’s findings draw from a global sample across industries and functions. The online survey was in the field from June 25 to July 29, 2025, and garnered responses from 1,993 participants in 105 nations representing the full range of regions, industries, company sizes, functional specialties, and tenures. Thirty-eight percent of respondents say they work for organizations with more than $1 billion in annual revenues. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. The results offer insight into how organizations adopt AI at scale and how that effort creates value.
The research focused on how organizations use AI inside actual workflows. It asked leaders whether they deploy AI in at least one function, whether they scale those tools across the enterprise and whether they see meaningful improvement in performance, productivity or financial results.
It also examined emerging interest in agentic AI, which refers to systems that perform multi-step tasks with more autonomy. Since many teams talk about these tools as the next leap forward, the survey provides a grounded view into where experimentation gives way to real implementation.
High adoption hides slow integration
Nearly all organizations in the sample say they use AI somewhere in their operations. Adoption looks strong on the surface. But only about a third of them report that they scaled AI across functions. That gap interests me because it shows how easy it is to add a tool and how challenging it becomes to rework surrounding processes. Most teams start with experiments. Few take the next step into redesigning how work moves. This finding helps explain why the promise of AI often feels larger than the evidence inside daily operations.
Agentic AI excites leaders but grows cautiously
Interest in agentic AI continues to rise. More than half of respondents say they are testing agent-based systems and a smaller group already scaled at least one. Yet no single function shows more than ten percent scaling. Leaders want these tools to unlock new possibilities. Still, they move carefully when the work touches multi-step processes or customer-facing tasks. This slow expansion reveals something important. Technology can advance quickly, but teams still take time to build trust and confidence as they put more responsibility on automated systems.
Enterprise value emerges slowly, except for a small group
Most organizations report small and steady benefits. Some see cost reductions in specific use cases. Others see early revenue lift or faster decision cycles. Yet only a minority experience measurable impact at the enterprise level. A small group pulls ahead. They invest more deeply. They redesign workflows instead of inserting tools into old structures. They build leadership alignment early. The contrast between that group and everyone else illustrates a pattern many leaders recognize. Impact grows when organizations treat AI as a driver of operational change instead of a series of isolated upgrades.
What this tells us about the moment we’re in
We remain early in this transition. The tools continue to evolve, and more meaningful integrations are still ahead of us. Many teams carry legacy systems, intricate approval paths or heavily manual processes. Change does not happen overnight. Those realities slow down transformation, even when the motivation is high.
Big shifts in productivity often require new structures. That work takes patience, iteration and leadership commitment. The study reinforces this idea. AI may move fast, but organizations move at the pace of people, workflows and incentives.
How leaders can move toward real ROI
The best starting point involves reevaluating workflows instead of job titles. Follow the path of a task from start to finish. Look for friction, repeated effort and unnecessary handoffs. AI creates the most value when it supports the actual movement of work rather than a generalized job description. Teams unlock more upside when they redesign the process itself.
Culture matters just as much. Teams need shared norms around how and when to use AI. They need room to practice and space to adjust. When people feel supported, they adopt new patterns with more confidence. That shift clears the way for larger operational gains.
Leaders who approach AI as an opportunity to reimagine workflows tend to see momentum faster. They invest in tools that integrate cleanly into daily operations. They build clarity for their teams so that experimentation becomes progress instead of noise.
This is where platforms like Civic Nexus help. They bring structure to how teams apply AI across functions. They streamline tasks, reduce the load of manual work and support the shift toward more connected processes.
If you want meaningful ROI from AI, begin with the work itself. Give your teams the clarity they need. Then choose tools that help them move with focus, alignment and forward momentum.
