Launching an AI feature is more complex than traditional software releases. Beyond code quality, you need to validate model performance, ensure safety guardrails work, set up proper monitoring, and prepare for edge cases that only appear in production. This checklist ensures you cover all critical areas.
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# AI LAUNCH READINESS CHECKLIST
# Feature: [Feature Name]
# Target Launch: [Date]
# Owner: [PM Name]
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1. MODEL READINESS
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[ ] Model version locked and tagged
Version: _______________
[ ] Evaluation metrics meet thresholds
- Accuracy: ___% (threshold: ___%)
- Precision: ___% (threshold: ___%)
- Recall: ___% (threshold: ___%)
- F1 Score: ___% (threshold: ___%)
[ ] Latency requirements validated
- P50: ___ms (threshold: ___ms)
- P95: ___ms (threshold: ___ms)
- P99: ___ms (threshold: ___ms)
[ ] Load testing completed
- Peak QPS tested: ___
- Error rate under load: ___%
[ ] Model artifacts deployed to production
[ ] Fallback model configured (if applicable)
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2. SAFETY & COMPLIANCE
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[ ] Content safety filters active
- Harmful content: [ ] Tested
- PII detection: [ ] Tested
- Bias checks: [ ] Passed
[ ] Input validation implemented
- Max input length: ___
- Rate limiting: ___ requests/min
- Invalid input handling: [ ] Tested
[ ] Output guardrails configured
- Confidence threshold: ___
- Fallback responses defined: [ ] Yes
- Human escalation path: [ ] Configured
[ ] Legal/compliance review completed
- Privacy review: [ ] Approved
- Legal review: [ ] Approved
- Security review: [ ] Approved
[ ] User consent mechanisms in place
[ ] Data retention policies implemented
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3. MONITORING & ALERTING
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[ ] Real-time dashboards created
- Model latency: [ ] Configured
- Error rates: [ ] Configured
- Usage volume: [ ] Configured
- Cost tracking: [ ] Configured
[ ] Alerts configured
- Latency spike (>___ms): [ ] Set
- Error rate (>___%): [ ] Set
- Model confidence drop: [ ] Set
- Cost threshold: [ ] Set
[ ] Logging implemented
- Input/output logging: [ ] Enabled
- PII redaction: [ ] Verified
- Log retention: ___ days
[ ] Feedback collection ready
- Thumbs up/down: [ ] Implemented
- Detailed feedback form: [ ] Ready
- Feedback routing: [ ] Configured
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4. USER EXPERIENCE
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[ ] Loading states designed
- Skeleton/shimmer: [ ] Implemented
- Progress indicators: [ ] Implemented
- Timeout handling: [ ] Tested
[ ] Error states designed
- Friendly error messages: [ ] Written
- Retry mechanisms: [ ] Implemented
- Fallback content: [ ] Ready
[ ] AI disclosure implemented
- "AI-generated" labels: [ ] Added
- Confidence indicators: [ ] Shown (if applicable)
- Limitations disclosed: [ ] Yes
[ ] Accessibility verified
- Screen reader compatible: [ ] Tested
- Keyboard navigation: [ ] Works
- Color contrast: [ ] Passed
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5. DOCUMENTATION
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[ ] Internal documentation complete
- Architecture diagram: [ ] Created
- API documentation: [ ] Updated
- Runbook: [ ] Written
[ ] Support documentation ready
- FAQ for support team: [ ] Created
- Escalation procedures: [ ] Defined
- Known limitations: [ ] Documented
[ ] User-facing documentation
- Help center article: [ ] Published
- Feature announcement: [ ] Drafted
- Changelog entry: [ ] Ready
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6. ROLLBACK & RECOVERY
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[ ] Rollback procedure documented
- Rollback owner: _______________
- Rollback time estimate: ___ minutes
- Rollback tested: [ ] Yes
[ ] Kill switch implemented
- Feature flag: [ ] Created
- Instant disable: [ ] Tested
- Partial disable (by segment): [ ] Available
[ ] Rollback triggers defined
- Error rate >___% → Rollback
- Latency P99 >___ms → Rollback
- Safety incident → Immediate rollback
- User complaints >___ → Investigate
[ ] Communication plan ready
- Internal notification: [ ] Template ready
- User notification: [ ] Template ready
- Status page: [ ] Configured
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7. LAUNCH LOGISTICS
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[ ] Rollout plan defined
- Phase 1: ___% of users (Date: ___)
- Phase 2: ___% of users (Date: ___)
- Phase 3: 100% (Date: ___)
[ ] Launch team identified
- PM: _______________
- Engineering lead: _______________
- ML engineer: _______________
- On-call: _______________
[ ] Launch timing confirmed
- Day of week: _______________
- Time (with timezone): _______________
- Avoiding: holidays, major events, freeze periods
[ ] Success criteria defined
- Day 1 target: _______________
- Week 1 target: _______________
- Month 1 target: _______________
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FINAL SIGN-OFF
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[ ] Product Manager: _______________ Date: ___
[ ] Engineering Lead: _______________ Date: ___
[ ] ML/AI Lead: _______________ Date: ___
[ ] QA Lead: _______________ Date: ___
[ ] Security (if required): _______________ Date: ___
[ ] Legal (if required): _______________ Date: ___
Launch Decision: [ ] GO [ ] NO-GO
Notes:
___________________________________________________
___________________________________________________
___________________________________________________Section-by-Section Guidance
1. Model Readiness
This section ensures your model performs as expected in production conditions.
- Version locking: Never launch with "latest" - always use a specific tagged version
- Evaluation metrics: Test on held-out data that represents production distribution
- Latency testing: Test under realistic load, not just single requests
- Fallback model: Consider a simpler, faster model if primary fails
2. Safety & Compliance
The most critical section - safety issues can cause significant harm and brand damage.
- Content filters: Test with adversarial inputs, not just normal usage
- Output guardrails: Define what happens when confidence is low
- Legal review: Required for any user-facing AI, especially in regulated industries
- User consent: Be explicit about AI usage in your product
3. Monitoring & Alerting
You can't improve what you don't measure. AI features need more monitoring than traditional features.
- Real-time dashboards: You need visibility during and after launch
- Alert thresholds: Set based on baseline metrics from testing
- Feedback loops: User feedback is critical for AI quality improvement
- Cost tracking: AI costs can spike unexpectedly - monitor closely
4. User Experience
AI features often have unpredictable latency and outputs - design for this.
- Loading states: AI can be slow - users need feedback that something is happening
- Error handling: Graceful degradation is essential for AI features
- AI disclosure: Transparency builds trust and manages expectations
- Accessibility: Don't let AI innovation exclude users with disabilities
5. Documentation
Documentation prevents knowledge silos and enables fast incident response.
- Runbook: Step-by-step guide for common issues and escalation
- Support FAQ: Prepare support team for common user questions
- Known limitations: Be upfront about what the AI cannot do
6. Rollback & Recovery
Hope for the best, plan for the worst. Every AI launch needs a tested rollback plan.
- Kill switch: One-click disable is non-negotiable for AI features
- Rollback triggers: Pre-define when to rollback, don't decide in a crisis
- Communication templates: Draft incident communications before you need them
Common Launch Mistakes
Skipping load testing
"It worked in staging" doesn't mean it works at scale. Always load test AI features - they often have different scaling characteristics than traditional code.
No kill switch
If something goes wrong, you need to disable the feature instantly. A feature flag that can be toggled in seconds is essential.
Launching Friday afternoon
AI features need active monitoring post-launch. Launch early in the week when your team is available to respond to issues.
100% rollout on day one
Always start with a small percentage of users. AI behavior in production often differs from testing. Gradual rollout limits blast radius.
Ignoring edge cases
AI features encounter edge cases you never imagined. Have monitoring and feedback loops to catch and learn from unexpected inputs.
No cost monitoring
AI API costs can explode unexpectedly. One viral use case or infinite loop could cost thousands. Set up cost alerts before launch.
Launch Day Quick Reference
launch-day-runbook.md
# LAUNCH DAY RUNBOOK
## T-60 Minutes
- [ ] Confirm all team members are online
- [ ] Verify monitoring dashboards are accessible
- [ ] Check on-call schedule is correct
- [ ] Review rollback procedure with team
## T-0 (Launch)
- [ ] Enable feature flag for Phase 1 users
- [ ] Announce in team channel: "Feature X now live for X%"
- [ ] Start monitoring timer (check every 15 min for first hour)
## T+15 Minutes
- [ ] Check error rates (should be <X%)
- [ ] Check latency (P99 should be <Xms)
- [ ] Review first user feedback (if any)
- [ ] Check cost dashboard
## T+1 Hour
- [ ] Full metrics review
- [ ] Decision: Proceed to Phase 2 or hold?
- [ ] Update stakeholders on launch status
## If Issues Occur
1. Assess severity (P0-P3)
2. If P0/P1: Execute kill switch immediately
3. Notify stakeholders using template
4. Begin investigation
5. Do NOT re-enable until root cause identified
## Success Criteria for Phase 2
- Error rate: <X% for 4+ hours
- Latency P99: <Xms consistently
- No safety incidents
- User sentiment: Neutral or positive