The 5 Critical Gates from Prototype to Production
Overview
This checklist is designed to help founders and leadership teams prevent AI projects from stalling in "Pilot Purgatory." Use it to evaluate your initiative at each stage.
The Rule: Do not proceed to the next gate until every item in the current gate is marked "Yes." Skipping a gate is building on sand.
Gate 1: Business KPI & Owner
Goal: Ensure the project solves a real business problem, not just an interesting technical one.
Checklist
- [ ] Has a single, accountable business owner been assigned? (Not a technical lead)
- [ ] Have you defined the primary Key Performance Indicator (KPI) this project will improve? (e.g., reduce support ticket volume, increase conversion rate)
- [ ] What is the baseline value of that KPI today?
- [ ] What is the specific target improvement value for success?
- [ ] Can you articulate the business value in one sentence without using technical jargon?
- [ ] If the project succeeds tech-wise but misses the KPI, is it still a failure? (Answer should be "Yes")
Gate 2: Data Readiness & Access
Goal: Confirm you have the necessary fuel for the engine and can deliver it reliably.
Checklist
- [ ] Have you identified all data sources required for training or context (RAG)?
- [ ] Is the data quality sufficient? (Clean, accurate, labeled if necessary)
- [ ] Do you have legal and compliance approval to use this data for this specific purpose?
- [ ] Are the data pipelines built to feed the system in production, not just for a one-off demo?
- [ ] Is the process for updating or refreshing the data automated?
Gate 3: Evaluation Plan
Goal: Define what "good" looks like with hard numbers, not vague feelings.
Checklist
- [ ] Have you created a representative "golden dataset" for testing and validation?
- [ ] Have you defined quantitative metrics for accuracy, relevance, or quality?
- [ ] What are the exact pass/fail thresholds for these metrics?
- [ ] Has the system been "red-teamed" to identify potential bias, toxicity, or hallucinations?
- [ ] Do you have a process for human-in-the-loop review of ambiguous results?
- [ ] Are stakeholders aligned on the evaluation criteria?
Gate 4: Security & Privacy Review
Goal: Identify and mitigate risks before they become expensive legal or PR disasters.
Checklist
- [ ] Has a formal security review been conducted on the architecture and data flow?
- [ ] How is Personally Identifiable Information (PII) handled? (e.g., redacted, anonymized, isolated)
- [ ] Does the solution comply with all relevant regulations (GDPR, CCPA, HIPAA, SOC2)?
- [ ] Are user consent and data usage policies clearly updated?
- [ ] Are vendor API keys and secrets managed securely (not hardcoded)?
- [ ] Is there a plan for handling a data breach or security incident involving the AI system?
Gate 5: Reliability & Monitoring
Goal: Ensure the system can survive the real world without constant hand-holding.
Checklist
- [ ] Have you defined latency targets for end-user experience? (e.g., <500ms response)
- [ ] Is the cost per query or transaction understood and acceptable at scale?
- [ ] What is the defined "fallback logic" when the AI fails, times out, or produces low-confidence output?
- [ ] Is real-time monitoring set up to track system health, errors, and costs?
- [ ] Is there a mechanism to detect "drift" in data or model performance over time?
- [ ] Is there a clear on-call process for when things go wrong in production?
Next Steps
If you are stuck at a gate or need a partner to help you navigate this process, DM me or book a consultation call.