Write down the decision the pilot must support
A proof of concept should answer whether a particular approach helps with a particular task. “Explore AI” is too broad. “Can approved customer context reduce meeting preparation without increasing factual errors?” is testable.
Choose a workflow owner and a small set of representative cases. Include awkward cases: missing notes, conflicting dates, and incomplete records. Do not select only the examples that make the demo look good.
Measure the starting point
Record preparation time, correction time, source-checking effort, and the number of important errors. Note how work varies by case. Those observations form your baseline, not an assumed saving.
Agree on acceptance criteria before seeing the results. Your team might require that every material claim be traceable to an approved source, that sensitive information remain restricted, and that total review effort decrease. Pick thresholds appropriate to the workflow rather than borrowing generic ROI promises.
Keep the first version bounded
Use an approved sample and minimum necessary access. Start with a draft output and human review. Keep production write permissions out of the initial pilot unless they are essential to the question being tested and separately authorized.
Record the model, instructions, sources, workflow version, and evaluation results. Change one important variable at a time so you know what improved—or degraded—the outcome.
Count the full cost
Include setup, model usage, hosting, maintenance, team training, and review. Compare those with the tools and work actually displaced. Time released for other work is useful capacity; it becomes cash savings only when expenditure really falls.
Set a spending ceiling and a review date. If accuracy, effort, or cost misses the agreed threshold, simplify the approach or stop. An evidence-based decision not to scale is a legitimate pilot result.
Test the exit as well as the demo
Preserve approved source files, decisions, and useful outputs in a company-controlled location. Export the pilot configuration where possible. Restore a sample independently, then ask another authorized teammate to find the right information.
Document what would need rebuilding with another provider: authentication, retrieval indexes, prompts, automations, and integrations. Test key outputs again after a model change. An archive proves that you have files; a successful rehearsal shows how much work remains to recover the process.
Finish with a go, change, or stop decision
Review the evidence with the people doing the work. Summarize the baseline, observed results, failures, full cost, and recovery limitations. Decide who owns maintenance before making the pilot operational.
Expand permissions or coverage gradually. Keep the human fallback available until the new process has earned trust. Build a starting brief to capture the first problem and scope your next conversation.