---
id: "claim-ai-adoption-collapses-18-months"
type: "claim"
source_timestamps: ["§ 1. Create and maintain high-quality data."]
tags: ["adoption-metrics", "change-management"]
related: ["concept-broken-data-foundation"]
speakers: ["Robert Handfield"]
confidence: "medium"
testable: true
sources: ["tail1"]
sourceVaultSlug: "hbr-seg-tail1"
originDay: 1
articleStem: "hbr-tail-107-lenovo-ai-supply-chain"
sourceUrl: "https://hbr.org/2026/05/how-lenovo-built-an-ai-powered-supply-chain"
sourceTitle: "How Lenovo Built an AI-Powered Supply Chain"
---
# AI adoption collapses within 18 months on bad data

**Claim:** When AI is layered on fragmented data, its recommendations will inevitably contradict the native knowledge of experienced human planners. Once planners stop trusting the AI outputs, user adoption collapses rapidly — usually within an 18-month timeframe.

**Confidence:** medium · **Testable:** yes

This is the failure-timeline component of [[concept-broken-data-foundation]] and connects to the trust/change-management gap in [[question-change-management-trust]].

> **Enrichment validation — mechanism supported, timeframe anecdotal.** The causal chain (bad data → contradictory outputs → loss of trust → collapse in adoption) is well supported: research on "algorithm aversion" shows users abandon algorithms after a few visible errors, especially in high-stakes contexts. **However**, the specific "within 18 months" horizon appears to be practitioner experience, not a generalizable empirical constant — adoption trajectories vary widely by organization, domain, and governance. Cite the mechanism confidently; treat 18 months as illustrative. (Confidence deliberately marked *medium*.)