---
id: "question-measuring-ai-roi"
type: "open-question"
source_timestamps: ["§ The Workslop Pressure Cooker"]
tags: ["board-governance", "roi"]
related: ["concept-performative-ai-use", "counter-adoption-metrics-early"]
resolutionPath: "Shifting board-level reporting from 'adoption metrics' (e.g., seats deployed, prompts generated) to 'outcome metrics' (e.g., time saved on specific workflows, reduction in error rates)."
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-edu-38-ai-workslop"
sourceUrl: "https://hbr.org/2026/01/why-people-create-ai-workslop-and-how-to-stop-it"
sourceTitle: "Why People Create AI “Workslop”—and How to Stop It"
---
# How can boards measure AI ROI without forcing performative use?

Boards are pushing for leaner teams and demanding AI usage to compensate for slowing productivity. This top-down pressure creates the mandates that cause [[concept-performative-ai-use]] and, downstream, [[concept-workslop-d38]]. The article does not detail how boards should alter their ROI expectations or measurement strategies to relieve this pressure.

**Resolution path:** Shift board-level reporting from 'adoption metrics' (seats deployed, prompts generated) to 'outcome metrics' (time saved on specific workflows, reduction in error rates).

**Counterpoint:** [[counter-adoption-metrics-early]] argues crude adoption metrics may be necessary early to justify investment, since outcome metrics lag.


## Related across articles
- [[claim-traditional-training-metrics-fail]]
- [[question-measuring-ai-team-effectiveness]]