Start with the work, not the model
A pilot often begins with a technical possibility: a model can summarise a document, draft a response or answer a question. Production requires a different level of specificity. Which document? For which user? What decision follows the answer? What happens when the answer is incomplete? Without those boundaries, teams can optimise a demonstration that has no clear place in everyday work. Write a brief that describes the input, user, expected output and next action. Include the cost of getting it wrong. A draft used by a qualified reviewer has a different risk profile from an answer delivered directly to a customer.
Choose a baseline worth beating
A pilot should be compared with the current workflow and with credible simpler alternatives. Better document structure, conventional search or a rules-based step may address the same bottleneck. Record the time required to complete the entire task, including checking and correction. A system that generates text quickly can still increase total effort if reviewers must investigate every claim. Define quality in terms of the task: required facts present, unsupported statements absent, the correct escalation made and the user able to proceed. Avoid a single average score that conceals costly failures.
Test the cases that are likely to break it
Curated examples are useful for explaining an idea, but weak evidence for deployment. Build an evaluation set from representative work, with permission and suitable handling of sensitive information. Include incomplete requests, conflicting documents, unfamiliar terminology and cases where the right answer is to abstain. Keep a separate set for validation so that repeated tuning does not simply teach the system your test. Have people who understand the task assess the outputs using agreed criteria. Record patterns of failure, not only a pass rate. The pattern tells you whether the problem calls for better retrieval, clearer boundaries or a different workflow.
Make operational ownership explicit
Someone must own access permissions, source freshness, monitoring, supplier changes and incidents. Human review is a control only when the reviewer has enough context, time and authority to intervene. Define the point at which review occurs and the evidence the reviewer receives. Establish fallback behaviour for unavailable services, low-quality inputs or uncertain outputs. Cost limits and latency also need attention: a viable pilot at low volume may be impractical when every request triggers several expensive steps. These are design requirements, not administrative tasks to add after approval.
Use gates instead of a big-bang launch
A staged path might move from offline evaluation to supervised use, then to a limited operational release. At each gate, state what evidence justifies proceeding, what requires repair and what stops the project. A successful pilot can produce a decision to narrow the use case. It can also show that deployment is not yet worthwhile. That is useful learning if it prevents a larger unsupported investment. The final question is whether the complete workflow delivers sufficient value within acceptable limits, and whether an accountable owner can keep it working.
A decision checklist
Before approving the next stage, ask: Is there a named process owner? Are permissions and data sources understood? Does the evaluation reflect actual work? Are failures visible to someone who can act? Does total effort improve once review is included? Is there a safe fallback? If an answer is unclear, assign a concrete action and an owner. A longer feature list will not resolve an unanswered operating question.
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