---
id: "concept-intelligent-ai-failures"
type: "concept"
source_timestamps: ["§ Create intelligent failure protocols"]
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: ["failure-protocols", "learning", "experimentation"]
related: ["concept-basic-ai-failures", "framework-3m-ai-rollout", "entity-right-kind-of-wrong"]
definition: "Failures that occur when testing AI in new domains or pushing its boundaries in controlled, low-risk ways, yielding valuable learning."
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"
---
# Intelligent AI Failures

Drawing directly on [[entity-amy-c-edmondson|Amy Edmondson]]'s failure taxonomy from [[entity-right-kind-of-wrong|*Right Kind of Wrong*]], **intelligent AI failures** occur when teams test AI in *new domains* or push its boundaries in **controlled, low-risk environments**. These failures yield valuable new information about the AI's real capabilities and limitations.

Because they generate knowledge, intelligent failures should be **explicitly celebrated** as learning opportunities that help the organization calibrate expectations and develop better human-AI collaboration protocols. They are the intended output of the [[framework-3m-ai-rollout|3M phased failure-to-improvement loop]] and a core pillar of the [[framework-ai-integration-principles|psychological-safety framework]] ("create intelligent failure protocols").

The essential move is to **distinguish** them from their opposite, [[concept-basic-ai-failures]] — preventable errors in known contexts, which generate no new knowledge and should be engineered out. Celebrating intelligent failures while preventing basic ones is what makes failure protocols psychologically safe rather than reckless.

**External grounding:** This is a direct port of an established framework into AI practice, and it is well aligned with contemporary guidance to experiment with AI in low-stakes domains (e.g., Seth Mattison's Green/Yellow/Red risk categories).