---
id: "contrarian-patience-over-speed"
type: "contrarian-insight"
source_timestamps: ["§ 1. Create and maintain high-quality data.", "§ Conclusion"]
tags: ["project-management", "executive-pressure"]
related: ["concept-digital-transformation-1-0", "action-fix-data-infrastructure"]
challenges: "The conventional corporate mandate to rapidly deploy AI pilots and show immediate ROI within quarters rather than years."
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"
---
# Patience in data prep beats speed to AI deployment

**Contrarian insight:** Patience in data prep beats speed to AI deployment.

In an environment where executives feel immense pressure to show quick 'AI wins' (often rushing data prep in a single quarter), Lenovo's approach is highly contrarian: they spent five years strictly on data infrastructure ([[concept-digital-transformation-1-0]], via [[action-fix-data-infrastructure]]) before deploying serious AI. The author argues this uncomfortable patience is *exactly why* Lenovo succeeded where others fail.

**Challenges:** The conventional corporate mandate to rapidly deploy AI pilots and show immediate ROI within quarters rather than years.

> **Enrichment counterpoint:** Agile and "test-and-learn" transformation models advocate *parallel* work — building data capabilities while running small, carefully chosen pilots to generate learning and maintain sponsorship. "Fail fast" narratives (including Lenovo's own AI Library story) show experimentation can coexist with long-term architecture building. The risk of *pure* patience: multi-year foundational programs without visible wins can lose executive support (see [[question-cost-of-transformation]]). The defensible synthesis is *sequenced primacy of data* with staged, tangible early value.