The Adoption Problem
We've seen it happen more than once: a technically excellent AI system gets deployed, the metrics look great in testing, and then adoption plateaus at 20%. People try it once, get a result they don't fully trust, and go back to the old way.
The system wasn't the problem. The rollout was.
AI adoption fails for the same reasons any operational change fails: people weren't involved in the design, they don't understand how the system works, they don't know what to do when it gets something wrong, and they have no reason to trust it yet.
The Trust Gap
Trust in an AI system is built through transparency and experience. Workers need to understand:
- What the system is doing — not in technical detail, but well enough to know when to rely on it and when to double-check
- Where it can go wrong — every AI system has limitations; teams that know those limitations use the system more effectively
- How to give feedback — when the output is wrong, there should be a clear path to flag it that doesn't feel futile
Without this, the natural human response is skepticism — and that skepticism hardens into avoidance.
What Good Change Management Looks Like
Before launch, we recommend involving end users in the design and testing process. The people who will use the system daily have the best insight into edge cases and quality standards. When they see their feedback reflected in the final product, they feel ownership rather than imposition.
At launch, training should focus on the workflow, not the technology. Show people how their day changes, not how the model works.
After launch, track adoption metrics with the same rigor you apply to performance metrics. Low adoption is a signal that something needs to be addressed — in the interface, the outputs, the training, or the communication.
The technology is only half the project. The other half is the people.
