---
id: "action-ai-after-action-reviews"
type: "action-item"
source_timestamps: ["§ Model fallibility and curiosity."]
source_url: "https://hbr.org/2026/02/how-to-foster-psychological-safety-when-ai-erodes-trust-on-your-team"
source_title: "How to Foster Psychological Safety When AI Erodes Trust on Your Team"
tags: ["rituals", "continuous-learning"]
related: ["framework-ai-integration-principles"]
speakers: ["Jayshree Seth", "Amy C. Edmondson"]
action: "Host regular 'AI After-Action Reviews' to discuss AI successes, failures, and mechanics."
outcome: "Normalizes curiosity and helps teams collectively calibrate trust in AI."
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-cl-79-psychological-safety-ai-trust"
sourceUrl: "https://hbr.org/2026/02/how-to-foster-psychological-safety-when-ai-erodes-trust-on-your-team"
sourceTitle: "How to Foster Psychological Safety When AI Erodes Trust on Your Team"
---
# Institute AI After-Action Reviews

**Action.** Institute regular learning rituals — specifically **"AI After-Action Reviews."** In these sessions, teams explicitly discuss **what worked with the AI, what didn't work, and why.**

**Outcome.** Normalizes curiosity and helps demystify the AI's limitations in a psychologically safe setting — a direct instantiation of pillar 2 of the [[framework-ai-integration-principles|Psychological Safety Principles framework]] ("model fallibility and curiosity"). By creating a recurring, sanctioned venue to interrogate AI outputs, After-Action Reviews partially restore the [[prereq-collective-sense-making|collective sense-making]] that [[concept-attribution-uncertainty]] otherwise blocks.