Half of all enterprises running AI initiatives are stuck in pilots or earlier. The Infor Enterprise AI Adoption Impact Index, published April 22, 2026, puts the number at 49%. That is not a technology failure. It is an ownership failure. The companies breaking through are not using better models. They are making one decision the stuck companies have not made: who owns the process change.

That single decision separates the 49% from the rest.

The Numbers Tell One Story

The Infor index is not an outlier. A Writer survey released the same week found that 79% of enterprises face AI adoption challenges, double the rate from 2025. And 54% of C-suite executives say AI adoption is “tearing their company apart.”

Meanwhile, Stanford HAI’s 2026 AI Index reports that 88% of organizations use AI for at least one function. So the technology is deployed. It is sitting inside organizations right now. The gap is not between companies that have AI and companies that don’t. The gap is between companies where AI is doing real work and companies where it is running in a sandbox that nobody can figure out how to close.

That gap is not about intelligence, capability, or budget. It is about operations.

What Danfoss Did Differently

At Google Cloud Next this month, Danfoss presented a case study worth paying attention to. They automated 80% of their transactional decisions. Response times dropped from 42 hours to near real-time.

They did not do this by deploying a more capable model. They did it by redesigning the decision process first, then applying AI to the redesigned process. The order matters.

Most pilot programs work backward. They start with the AI capability and try to find a process to attach it to. The pilot succeeds because the scope is narrow and the data is clean. Then someone tries to push it into production and discovers that the existing process was never built to accept automated decisions. Approvals, exceptions, escalation paths, accountability chains. None of it was designed for a system that moves at machine speed.

Danfoss started with the process. They mapped which transactional decisions were being made, who was making them, what information those decisions required, and what the downstream consequences were. Then they identified which of those decisions could be automated without changing the accountability structure. Then they built the AI system to fit.

The 42-hour response time was not a technology constraint. It was a process constraint. Humans were queuing, reviewing, approving, and forwarding decisions that followed deterministic rules. The AI did not replace judgment. It replaced waiting.

The Ownership Question

Here is the pattern I see in the 49%: the AI project has an owner, but the process change does not.

Someone owns the model. Someone owns the data pipeline. Someone owns the infrastructure. Nobody owns the answer to: “What changes in how we actually work when this goes live?”

That question sits in the gap between IT and operations. IT thinks it is a business question. Operations thinks it is a technology question. Leadership thinks someone is handling it. Nobody is.

Every stuck pilot I have seen shares this trait. The technology works. The demo is impressive. And then someone asks, “So what happens to the existing approval workflow?” and the room goes quiet.

The companies in the other 51% have answered that question. They answered it early, usually before the pilot started. They designated a person, usually someone from operations with enough authority to change a process and enough technical literacy to understand what the AI can and cannot do. That person’s job is not to manage the model. It is to manage the transition.

Who Should Own This

This is not a CTO problem. The CTO can own the technology. But the process change needs to be owned by whoever currently owns the process being changed.

If AI is going into procurement, the head of procurement owns the transition. If it is going into customer service, the head of customer service owns it. If it is going into financial operations, the CFO’s team owns it.

The technology team builds and maintains the system. The process owner decides what changes, what stays, who is accountable for the automated decisions, and what the escalation path looks like when the system gets it wrong. Both roles are necessary. Neither can do the other’s job.

This is where the “tearing apart” language from that C-suite survey comes from. Organizations are trying to make AI adoption a technology initiative run by the technology team. That works for the pilot. It breaks for production, because production means changing how people work. And the technology team does not have the authority or the context to change how the procurement team processes purchase orders.

What This Means This Week

If you are running an AI pilot right now, ask one question: who owns the process change?

Not the model. Not the data. Not the infrastructure. The process change. The answer to “what is different about how we work on the day this goes live?”

If nobody owns that, your pilot is in the 49%. It does not matter how good the model is. It does not matter how clean the data pipeline is. Without someone who owns the operational transition, the pilot will produce a demo, the demo will produce enthusiasm, and the enthusiasm will slowly deflate as nobody can figure out how to make it real.

Danfoss cut response times from 42 hours to near real-time. They did that by deciding, before the technology was ready, exactly which decisions would be automated, who would be accountable for the automated decisions, and what would happen when the automation was wrong. The AI was the last piece, not the first.

The model is not what is holding you back. The org chart is.