Delivery guide · Pilot design · 6 min guide
An AI pilot should buy evidence, not applause.
Scope an AI pilot around one workflow, one accountable owner and one measurable outcome. Name the riskiest assumption, use representative data, define the human and system boundary, evaluate failures as well as averages, and agree in advance what evidence means proceed, change or stop.
Write the decision before the build
State the current workflow, baseline, target outcome, user and owner. Then write the decision the pilot must enable. If the team cannot explain what it will do differently after the result, the scope is still a prototype brief.
Test the assumption that can kill the project
Do not spend the pilot proving that a model can produce a polished sample. Test the uncertain capability: messy documents, Arabic intent variation, permission-aware retrieval, exception handling, adoption or unit economics.
Design evaluation and safety together
Use representative cases and explicit correct, acceptable and unacceptable outcomes. Define abstention, confidence, review and escalation behavior before exposing the workflow to customers or production systems.
- Quality threshold by case type
- Known high-risk failures
- Human review and override
- Audit trail and rollback
End with a documented decision
Review performance, operating cost, user behavior and failure patterns. Proceed only if the evidence supports the next risk and investment. A decision to stop is a successful pilot when it prevents a larger mistake.
A credible pilot brief contains
- 01Workflow, user and owner
- 02Baseline and target metric
- 03Riskiest assumption
- 04Representative evaluation set
- 05Safety and human boundary
- 06Proceed, change and stop criteria