The operating model is where AI adoption succeeds or fails
Pilots fail in the org chart, not the model. Here's why adoption is an operating design problem.
Almost every serious AI programme has a pilot that worked. The interesting pattern is that most of them stop there. The reason is rarely the technology. It's that the work the tool automates belongs to a team, and no team was asked to change how it works.
Adoption is ownership
A model that routes customer tickets is only as good as the team that owns the outcome: who retrains it, who handles the edge cases, who is accountable when it misbehaves. If the ownership question is answered after the build, it's answered wrong.
- Name the owner before the pilot, not after.
- Redesign the workflow the tool joins — don't bolt the tool onto the old one.
- Give the team the mandate and the metrics to change how they work.
- Budget for the governance that makes scale defensible.
When AI changes an operating model deliberately, the same pilot that stalls elsewhere becomes a compounding capability.
Spigle — AI-powered business consulting.
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