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Dealing with a stalled AI program?
We’ve been in that room. Let’s talk about what’s going on and whether we can help.
There’s a number that keeps showing up in AI research, and it doesn’t make anyone look good. Somewhere between 70% and 80% of AI projects in enterprise operations either fail outright or get stuck in permanent pilot mode, deployed in one corner of the business, never touching anything that matters at scale.
We’ve walked into enough of these situations to know why it happens. And it’s almost never the technology.
The pilot is designed to succeed in isolation.
Most AI pilots are set up to prove a point, not to transform a process. A small team, a curated dataset, favorable conditions. It works. Leadership sees a demo. Everyone’s impressed. Then the program tries to expand, and nothing works the same way it did in the controlled environment.
The real operations environment is messier. The data is dirtier. The people weren’t part of the pilot. And the process it was supposed to improve? Nobody told operations that AI was coming.
Six reasons deployments stall.
- In our experience running AI programs in complex supply chain environments, the same failure modes show up repeatedly.
- The pilot was never designed to scale, success criteria were defined by the vendor, not by operations.
- Data governance wasn’t solved first; the model works, but the data feeding it is inconsistent or incomplete.
- Change management was an afterthought, with training happening after go-live rather than before.
- Ownership was never assigned, the AI system sits between IT and operations, belonging to neither.
- Success was measured by adoption, not by business outcomes: people clicked, nothing changed. And the tool was solving the wrong problem, automating a process nobody was asking to be automated.
What 90%+ adoption looks like.
We’ve built AI adoption programs that hit 90% or better. Here’s what they have in common: the people who will use the tool help design it. The metrics are tied to things operations leaders care about. The rollout includes real training, not just documentation. And someone owns the program, not as a project, but as an ongoing responsibility.
Adoption at scale isn’t a launch event. It’s a design principle.
If your AI program stalled after the pilot, the technology probably isn’t the problem. Start by asking who owns the outcome.
When not to buy new software: A decision framework for operations leaders
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“Every effective S&OP meeting ends with someone who is authorized to make the call doing so.”
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