Begin with a problem worth solving
Pilots fail most often because they start with a capability ('we should use AI for something') rather than a problem with a cost. A good candidate is a workflow leaders already know is slow, error-prone or dependent on a few people—and where improvement would matter to customers, staff or margins.
Write the problem in one sentence that names the workflow, the symptom and who feels it. If that sentence is hard to write, the pilot is not ready.
Score candidates on value, feasibility and risk
List three to six candidate workflows and score each on three dimensions:
- Value: volume, time spent, rework, delay cost, and the effect on customers or revenue.
- Feasibility: are inputs consistent and digital, is the data accessible, and is there a clear owner who can make decisions?
- Risk: what happens if the output is wrong, what review is needed, and what privacy, compliance or customer-trust constraints apply?
Establish the baseline before you build
Measure the current state for a defined period: cycle time, manual hours, rework or error rate, backlog, and how often exceptions occur. Without a baseline, any post-pilot result is opinion.
Agree in advance what improvement would justify continuing, and what would justify stopping. A pilot that ends in a clear 'no' has still done its job.
Bound the pilot
Define the scope tightly: one workflow, one team or location, a fixed period, named reviewers and a human checkpoint where errors would be costly. Document what is out of scope—particularly integrations, additional workflows and company-wide rollout, which should be decided after the pilot, not assumed by it.
Plan for adoption, not just deployment
The people doing the work need an updated SOP, training and a way to report problems. Track adoption alongside performance: a tool nobody uses produces no result. Close with an adoption review that compares baseline to post-pilot measures and recommends scale, revise or stop.
