AI STRATEGY
Monitor, Adapt, and Respond Responsibly
Tune Based on Real-World Signals
Updating AI models too often—or not often enough—can introduce risk. Intelligent scheduling aligns improvements with observed performance gaps and feedback trends.
Why it's Important
Prevents unnecessary regressions from reactive updates
Aligns retraining with product maturity and scale
Enables focused iteration on weak points
Reduces testing burden on QA and dev teams
Builds internal trust in model release cycles
How to Implement
Set criteria for when a model update is needed
Track cumulative feedback and error patterns
Map issues to specific model behaviors or intents
Schedule model review checkpoints (e.g., monthly)
Freeze model updates before major launches
Communicate changelogs to stakeholders
Use A/B testing to validate improvements
Available Workshops
Model Update Planning Board
Error Theme Clustering
Model Versioning Timeline Sprint
Model vs. Feedback Heatmap Review
Change Impact Assessment Roundtable
Release Freeze Simulation
Deliverables
Model update calendar
Criteria doc for triggering retraining
Change logs for each model version
A/B test summary reports
Stakeholder alignment brief
How to Measure
Number of model updates per quarter
Regression rate post-update
Stakeholder confidence (survey or NPS)
% of update-triggered by significant drift
Performance delta before vs. after updates
Update impact on support tickets or user metrics
Pro Tips
Maintain a running doc of known model quirks
Include release notes in internal wikis
Automate comparisons between versions
Invite reviewers to evaluate before and after samples
Align model update cadence with sprint planning
Get It Right
Use a clear signal-to-update threshold
Build internal tooling for impact review
Share before/after examples with teams
Prioritize fixes for high-impact errors
Communicate clearly what changed and why
Don't Make These Mistakes
Updating reactively after every complaint
Updating reactively after every foundation model announcement
Ignoring feedback that doesn’t seem urgent
Forgetting to freeze before launches
Skipping performance benchmarks post-update
Hiding model update rationale from stakeholders