---
id: "action-adjust-incentives"
type: "action-item"
source_timestamps: ["§ Breakdown 2: Incentives reward the wrong behaviors."]
tags: ["performance-management", "incentives"]
related: ["concept-triple-burden", "framework-three-breakdowns"]
speakers: ["Julia Shin", "Sandra J. Sucher"]
action: "Tie performance reviews to documenting AI use cases, coaching, and cross-team knowledge transfer."
outcome: "Aligns managerial behavior with the actions required to scale AI, rather than defaulting to utilization metrics."
sources: ["reskilling"]
sourceVaultSlug: "hbr-seg-reskilling"
originDay: 10
articleStem: "hbr-sig-50-adoption-overloading-managers"
sourceUrl: "https://hbr.org/2026/06/ai-adoption-is-overloading-your-middle-managers"
sourceTitle: "AI Adoption Is Overloading Your Middle Managers"
---
# Adjust Performance Incentives for Managers

**Action.** Revise evaluation systems to reward the behaviors that drive successful AI adoption. Move away from metrics that solely reward billable hours and individual output (see [[prereq-consulting-business-model]]). Explicitly tie performance reviews to how well employees document and share AI use cases, and reward managers for coaching, team development, and knowledge transfer.

**Outcome.** Aligns managerial behavior with the actions required to scale AI, rather than defaulting to utilization metrics.

This directly resolves the second of the [[framework-three-breakdowns]] (incentives reward the wrong behaviors) and is what makes the third leg of the [[concept-triple-burden]] — developing people — actually rewarded rather than done at the margins. It links to the unresolved [[question-new-performance-metrics]].

**Enrichment context.** The AI-resistance literature explicitly recommends shifting performance objectives away from legacy metrics (headcount managed, decisions made) toward AI adoption, efficiency, and team-capability development — operationalizing this recommendation.