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AI STRATEGY

Build Guardrails and Escalation Paths

Route Risk to the Right People

Escalation paths ensure risky, uncertain, or ethically sensitive outputs get reviewed by a human before reaching users. This is critical for maintaining control in high-stakes situations.

Why it's Important
  • Reduces exposure to unacceptable outputs

  • Protects users from hallucinated or biased content

  • Satisfies compliance for regulated use cases

  • Enables human oversight of edge cases

  • Builds a transparent review and approval process

How to Implement
  • Identify triggers for human review (e.g., low score + risky topic)

  • Build routing logic and notification systems

  • Assign reviewer roles and responsibilities

  • Set SLA expectations for review timelines

  • Create escalation dashboards and tracking tools

  • Use labeled examples to train reviewers

  • Regularly audit and tune escalation criteria

Available Workshops
  • Escalation Trigger Brainstorm

  • Reviewer Role Mapping

  • SLA & Workflow Planning

  • Dashboard Design Sprint

  • QA Calibration Session

  • Ethical Review Simulation

Deliverables
  • Human review trigger rules

  • Reviewer SOP (Standard Operating Procedure)

  • Escalation routing map

  • Reviewer training materials

  • Weekly escalation summary report

How to Measure
  • Number of escalations per week

  • Turnaround time per review

  • Reviewer agreement rate

  • Escalation false positive/false negative ratio

  • % of reviewed outputs requiring changes

  • % of critical errors prevented

Pro Tips
  • Use Slack or Jira integration for real-time escalation alerts

  • Visualize reviewer impact in dashboards

  • Share anonymized reviewer feedback with product teams

  • Include reviewers in model postmortems

  • Celebrate escalations that catch high-risk outputs

Get It Right
  • Set clear escalation thresholds

  • Rotate reviewers to avoid fatigue

  • Track reviewer consistency and decisions

  • Involve domain experts for high-risk areas

  • Build a feedback loop from reviewers to model training

Don't Make These Mistakes
  • Relying solely on automated detection

  • Overloading a single reviewer or team

  • Not documenting reviewer rationales

  • Skipping regular calibration

  • Treating human review as a last resort

Fractional Executives

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