---
id: "prereq-collective-sense-making"
type: "prereq"
source_timestamps: ["§ Where Trust Breaks Down"]
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: ["team-dynamics", "learning-theory"]
related: ["claim-ai-errors-ripple-differently", "concept-attribution-uncertainty"]
reason: "To understand why AI errors are uniquely damaging, one must understand how human teams normally process and recover from human errors."
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"
---
# Collective Sense-Making

**Prerequisite concept.** **Collective sense-making** is the process by which human teams *metabolize errors*: they ask contextual questions, understand each other's reasoning, and update their shared mental models. It is how a team recovers from a mistake *and* strengthens its bonds in the process.

**Why it's required.** The article's argument only lands if you understand this normal human process — because the core claim, [[claim-ai-errors-ripple-differently]], is that generative-AI errors **short-circuit** it. You cannot ask a black box "were you rushing?" or "what did you assume?", so [[concept-attribution-uncertainty]] leaves teams unable to attribute or prevent AI failures. Grasping collective sense-making is what makes the *difference* between human and AI errors legible. Rituals like [[action-ai-after-action-reviews|AI After-Action Reviews]] are attempts to partially reconstruct it around AI.