---
id: "prereq-adoption-telemetry"
type: "prereq"
source_timestamps: ["§ Why Anxiety Can Increase AI Use and Still Stall Results", "¶20", "§ What Leaders Must Do Differently", "¶30"]
tags: ["metrics", "software-deployment"]
related: ["claim-usage-not-buy-in", "action-pair-metrics-with-safety-signals"]
reason: "Necessary to understand the baseline IT practices the authors are critiquing when they say usage is a flawed proxy for buy-in."
sources: ["tail2"]
sourceVaultSlug: "hbr-seg-tail2"
originDay: 2
articleStem: "hbr-tail-127-ai-adoption-stalls"
sourceUrl: "https://hbr.org/2026/02/why-ai-adoption-stalls-according-to-industry-data"
sourceTitle: "Why AI Adoption Stalls, According to Industry Data"
---
# Enterprise Software Adoption Metrics

**Prerequisite.** The article critiques reliance on surface-level metrics like *"licenses activated"* and *"tools used."* A basic understanding of how enterprise IT departments traditionally measure software rollouts — **DAU/MAU, license utilization rates** — is necessary to understand why the authors argue these standard metrics become **dangerously misleading** when applied to AI.

**Why it's required.** [[claim-usage-not-buy-in]] and [[action-pair-metrics-with-safety-signals]] are both defined *against* this baseline: the authors are not saying telemetry is worthless, but that these familiar indicators, read naively, mistake [[concept-performative-ai-usage]] for genuine adoption.


## Related across articles
- [[concept-leading-indicators-of-focus]]