---
id: "prereq-frontline-metrics"
type: "prereq"
source_timestamps: ["§ 4. Encourage Experimentation"]
tags: ["operations", "performance-management"]
related: ["contrarian-metric-penalties", "concept-digital-playgrounds"]
reason: "Necessary to contextualize why traditional performance management actively hinders AI adoption."
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-edu-40-workers-dont-trust-ai"
sourceUrl: "https://hbr.org/2025/11/workers-dont-trust-ai-heres-how-companies-can-change-that"
sourceTitle: "Workers Don’t Trust AI. Here’s How Companies Can Change That."
---
# Traditional Frontline Operational Metrics

**Prerequisite:** a baseline understanding of how frontline workers (retail, logistics, manufacturing) are traditionally managed — via **strict compliance metrics** designed to *catch errors and enforce consistency.* The authors cite **time-clock violations, late scans, and missed check-ins** as canonical examples.

**Why it's required:** you cannot understand why experimentation feels *unsafe* to frontline workers without knowing that their performance is scored on **error-avoidance and rigid consistency.** This context is what makes the contrarian insight in [[contrarian-metric-penalties]] land — the same metrics that ensure operational reliability actively **disincentivize the trial-and-error** that AI adoption demands — and it is precisely the friction that [[concept-digital-playgrounds]] are designed to remove.