AI STRATEGY
Build Guardrails and Escalation Paths
Name the Gaps Before They Hurt You
Every AI system has failure modes. Documenting and tracking these proactively helps you manage expectations, prioritize fixes, and improve reliability.
Why it's Important
Makes limitations explicit to internal and external stakeholders
Reduces risk of surprise failures
Enables faster triage and debugging
Builds a transparent culture of safety
Supports ethical product development
How to Implement
Identify recurring or high-risk failure types
Categorize by severity and root cause (e.g., model vs. prompt)
Link failure conditions to specific scenarios or user segments
Document known mitigations or guardrails
Share failure profiles with product, QA, and support teams
Update with each model or prompt change
Available Workshops
Failure Mode Brainstorm
Retrospective Analysis of Live Incidents
Root Cause Mapping
Mitigation Strategy Sprint
Public-Facing Limitations Review
Failure Knowledgebase Setup
Deliverables
Failure conditions log
Mitigation and workaround catalog
Internal wiki or knowledge base
Model limitation disclosure template
Failure-to-fix tracker
How to Measure
Number of known issues logged
% of mitigated vs. unresolved failures
Frequency of repeat failure types
Time from identification to mitigation
Impact of failures on user outcomes
Visibility of known issues across teams
Pro Tips
Use tags to classify failures by model, use case, and severity
Include failure examples in onboarding for new team members
Share failure patterns in OKR or roadmap planning
Cross-link failures to analytics and support tickets
Add "Known Limitations" section to public docs or UI where relevant
Get It Right
Treat documentation as a shared artifact, not blame log
Prioritize failures by real-world impact
Keep logs accessible and searchable
Regularly revisit and retire outdated entries
Communicate openly with users about limitations
Don't Make These Mistakes
Hiding or downplaying known issues
Failing to categorize failure root causes
Keeping logs in personal silos or static files
Treating logs as checklists instead of evolving insights
Ignoring recurring patterns in failures