Over the past two years, executive adoption of AI has skyrocketed—from 37% weekly usage to 82%. Leaders are embracing AI solutions faster than any other technology in recent memory. But there’s a growing disconnect between AI usage and AI transformation.
A conversation with a VP of Operations captured the challenge well:
“We rolled out AI tools to the team eight months ago. Everyone uses them. But I still can’t point to a single process that has fundamentally changed the business.”
This isn’t unusual. In fact, this is the gap most enterprises currently operate within.
Teams are experimenting. Tools are everywhere. But the material impact—the kind that reshapes cost structure, accelerates growth, and lifts profitability—remains elusive. And in nearly every case, the issue isn’t adoption. Its adoption is in the wrong phase of the AI lifecycle.
To understand how organizations progress—and why so many stall—it helps to break the AI journey into three phases.
AI usage is rising across all industries, but usage alone doesn’t determine competitiveness. Where your organization sits in the lifecycle determines how AI affects your bottom line.
Most organizations begin here—often without even realizing it.
Teams start exploring AI tools to support individual tasks:
It feels productive. It boosts morale. And there’s virtually no pressure to demonstrate ROI. This is the “try things out and see what sticks” era.
But here’s the warning: If the enterprise remains in this phase for too long, it risks falling behind competitors who move past experimentation and into measurable transformation. Learn how emerging AI capabilities can evolve beyond experimentation.
Usage levels jump dramatically— and so does organizational optimism.
AI extends beyond individual contributors and starts touching functional areas:
Teams can feel the momentum. Leaders start asking better questions:
However, this is also where most organizations stall.
This phase creates the illusion of progress, but without the velocity.
Weekly usage only increases slightly from Phase 2—but the difference in business impact is dramatic.
In this phase, AI is not a tool. It’s an operating model.
Organizations begin to experience:
This is the zone where winners operate.
Enterprises here unlock compounding value every month because AI solutions fuel speed, accuracy, quality, and decision-making at scale.
Despite rapid growth in leadership usage, most companies remain stuck between experimentation and adoption.
But they haven’t made the strategic shift needed to answer the fundamental question:
“Where does AI create transformational value in our business?”
Until that question is answered, most AI investment will remain incremental rather than exponential, focused on improving tasks rather than transforming processes.
Based on industry data and hundreds of executive conversations, three obstacles repeatedly surface:
If AI is viewed as a way to “help people work faster,” the ceiling is low.
Transformation starts when leaders instead ask:
“How do we redesign workflows so AI handles the work itself?”
Tools are deployed widely, but no one owns the roadmap. Teams innovate independently, but without coordination or governance.
Without a unified strategy, adoption plateaus.
Experimentation produces anecdotes. Transformation produces scoreboards.
Enterprises unlock maximum value when they tie AI to:
Until these metrics exist, AI will feel exciting—without being transformative.
Not where you hope to be. Not where your tool usage suggests you might be. But where you actually operate today.
Every phase has value, but only one delivers a competitive advantage, and only one becomes a true moat.
For most organizations, the obstacles aren’t technical at all—they’re strategic. Many lack a clear ownership structure for AI, resulting in initiatives scattered across teams and a lack of unified direction. Others haven’t established enterprise-wide AI priorities, resulting in isolated projects that never scale.
There’s often uncertainty about where AI can deliver measurable ROI, making it difficult to justify investment or redesign workflows. Some companies move forward without an accurate transformation roadmap, causing efforts to stall before they gain traction.
In many cases, the tools being adopted aren’t aligned with tangible business outcomes, preventing AI from creating meaningful impact.
The organizations that ultimately reach the Transformation Phase aren’t the ones who simply use AI more frequently. They’re the ones who rethink how AI fits into the architecture of their business—treating it not as an incremental upgrade, but as a foundational shift in how work gets done.
Leadership adoption jumping from 37% to 82% is an incredible signal.
But usage is only the beginning.
The enterprises that win in 2025–2026 won’t be the ones using AI the most. They’ll be the ones who use AI to alter the mechanics of their business.
So ask your leadership team:
Because the companies that answer that question now will be the ones defining their industry in the next 24 months, if you're ready to determine which phase you're currently operating in and how to accelerate into the next one, our team can help.
Connect with us to start the conversation and learn about how we can help you grow your business through more efficient operations, simplification, and automation.