CERNOVANCE / INSIGHTS
The AI pilot works. Why is nobody using it?
A demonstration tests a capability; operating use needs data, permissions, workflows and people to work together. We focus on the conditions that connect pilots to everyday work.
Define completion, not just a good answer
A fluent response is not a completed task. Establish input sources, destination systems, approvers and failure handling. If users still copy, paste and reconcile across tools, much of the effort may remain. Evaluate the whole task from request to review.
Design for exceptions
Outdated information, insufficient permissions, unavailable interfaces and incomplete requests need explicit handling. Start with bounded use cases where people can take over, record exceptions and edits, then assess expansion. A pilot should reveal constraints.
Involve users in acceptance
Engineers can verify execution; users determine whether outputs are useful. Involve operators in requirements, tests and training, and clarify changed responsibilities. When adoption is low, investigate workflow friction before adding features.
Treat rollout as another decision
Agree baselines, handling time, quality and review effort, then evaluate maintenance costs, dependencies and feedback. A successful pilot is not proof of fit everywhere. Recheck data, access and investment before rollout, retaining the option to adapt or stop.