What an AI Pilot Should (and Shouldn't) Try to Prove
Published by Valusage Advisory Team
An AI pilot has one job: give the business a clear answer about whether to invest further. Most pilots fail at that job not because the technology underperforms, but because the pilot was never scoped to produce a clear answer in the first place.
Pilots fail when they try to prove too much
A pilot scoped to validate the technology, the process change, user adoption, and the business case all at once rarely produces a clean result on any of them. A tightly scoped pilot testing one specific question produces a decision you can actually act on.
Picking a success metric before you start
Agree the number that defines success — time saved, error rate reduced, cycle time shortened — before the pilot begins, not while reviewing results. Choosing the metric after the fact almost always leads to picking whichever number looks best.
What 'successful pilot' should unlock next
Define in advance what a successful pilot leads to — a wider rollout, a specific budget approval, a vendor contract — so the pilot has a clear next step rather than ending in another round of evaluation.
Where Valusage fits
Our Digital Transformation and Workflow Automation Advisory helps define pilot scope, success measures and governance before a pilot begins, and our Artificial Intelligence Strategy and Roadmap Development covers what should follow a successful one. Software development, deployment and vendor charges are excluded.
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