Pilot-to-Production Decision Loop

By Juan Beltrán, Industrial B2B AI Transformation Executive.

Is this AI pilot ready for production, or should we redesign or stop it? Use this before extending a pilot, buying more licenses, asking for budget, or celebrating a demo as if it were business value. Pilot-to-Production Decision Loop Decision to make: Is this AI pilot ready for production, or should we redesign or stop it? Decision owner: AI product owner with business sponsor, IT/security, finance, and the process owner. Working-session setup: - Timebox: 60 min working session - People in the room: Business sponsor, process owner, AI or data lead, and risk partner when needed. - Preparation: Decision-room prep: validate the baseline, risks, costs, and accountable owner first. Context: [Paste your notes, excerpts, draft, meeting transcript, CRM fields, proposal text, public research, or examples here.] Context I should provide: - Pilot objective - User group - Measured usage - Measured outcome - Failure logs - Security review - Production cost estimate Safety boundary: - Use only information I provide in this conversation. - Do not infer personal, confidential, regulated, pricing, customer, employee, or supplier facts. - If the material belongs in an approved enterprise environment, tell me before analyzing it. Instructions: Act as an AI product governance board. Evaluate this pilot for production readiness. Separate demo success from workflow success, list missing evidence, estimate operating risk, and recommend scale, redesign, hold, or stop. Finish with a one-page decision memo for the sponsor. Run the session in this order: 0. Inspect the context. If a missing fact could materially change the recommendation, ask no more than five focused questions and wait. If I ask you to continue, mark each missing fact as unknown. 1. Restate the original bet: Capture the business problem, baseline, promised outcome, and user group. 2. Separate demo success from workflow success: Compare demo quality with usage, completion, error, and handoff data. 3. Price production honestly: Include integrations, monitoring, access control, support, training, and change cost. 4. Run the risk gate: Check data exposure, model failure modes, compliance, auditability, and human override. 5. Make the decision: Choose scale, redesign, hold, or stop and write the exact evidence threshold for the next gate. Evidence rules: - Separate supplied facts, interpretations, assumptions, and unknowns. - Reference the exact note, excerpt, metric, or example supporting every material claim. - Show the strongest credible counterargument to the recommendation. - Do not invent customer facts, benchmarks, financial numbers, policy approvals, or system access. - Do not turn missing evidence into a confident recommendation. - Keep the answer useful for AI Product Owner. Output contract: A sponsor-ready decision memo: scale, redesign, hold, or stop. Return: 1. BLUF: the decision, recommendation, or draft in plain language. 2. Evidence table: claim, supplied evidence, confidence, and gap. 3. Assumption ledger: what is assumed and how to verify it. 4. Counterargument: the strongest reason the recommendation may be wrong. 5. Decision record: decision status, accountable owner, next action, and due date or trigger. 6. Evidence still needed: only the gaps that could change the decision. 7. Stop condition: state when the work is complete and when it must pause. Evidence checklist: - Baseline metric - Post-pilot metric - Active usage - Failure examples - Risk review - Support model - Production cost Human operating ritual: - No pilot may pass without a named production owner. - The sponsor must accept the operating cost, not only the build cost. - Document what would make the decision wrong. Do not use this loop when: Do not run it without a measurable workflow outcome, a business owner, or permission to stop the work. A human authority must approve the final decision and the operating environment. Stopping condition: Stop when the pilot has a clear decision and nobody is allowed to continue spending under the word experiment.

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