---
id: "question-change-management-trust"
type: "open-question"
source_timestamps: ["§ 3. Align AI investments with business priorities."]
tags: ["change-management", "human-in-the-loop"]
related: ["concept-supply-commit-accuracy-system", "claim-ai-adoption-collapses-18-months"]
resolutionPath: "Interviews with Lenovo supply chain planners regarding the rollout of iChain and the specific UI/UX or training methods used to build trust in automated data recalibration."
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"
---
# How did Lenovo manage the cultural shift to trusting AI recalibrations?

**Open question:** How did Lenovo manage the cultural shift to trusting AI recalibrations?

The [[concept-supply-commit-accuracy-system]] recalibrates planning inputs *before human planners see them*. The article notes that bad AI causes planners to lose trust ([[claim-ai-adoption-collapses-18-months]]), but it does not explain how Lenovo convinced its planners to trust an AI system that was *actively altering* the data (supplier commitments) they were used to seeing.

**Resolution path:** Interviews with Lenovo supply chain planners regarding the rollout of iChain and the specific UI/UX or training methods used to build trust in automated data recalibration.

> **Enrichment note:** Research on "algorithm aversion" suggests trust is fragile after visible errors, making the silent-recalibration design especially interesting — the source leaves the human-in-the-loop mechanics unexplained.