---
id: "claim-infrastructure-scales-adoption"
type: "claim"
source_timestamps: ["\\\"§ Breakdown 1: Learning is informal", "while delivery is relentless.\\\""]
tags: ["scaling", "infrastructure", "knowledge-management"]
related: ["concept-centralized-internal-hub", "action-build-centralized-hub"]
speakers: ["Julia Shin", "Sandra J. Sucher"]
confidence: "high"
testable: true
sources: ["reskilling"]
sourceVaultSlug: "hbr-seg-reskilling"
originDay: 10
articleStem: "hbr-sig-50-adoption-overloading-managers"
sourceUrl: "https://hbr.org/2026/06/ai-adoption-is-overloading-your-middle-managers"
sourceTitle: "AI Adoption Is Overloading Your Middle Managers"
---
# Infrastructure, not tool access, scales AI adoption

**Claim (confidence: high, testable):** The differentiator between successful and unsuccessful teams in the study was **not** which specific AI tools they had access to. Success depended on whether the firm had built **infrastructure** — a [[concept-centralized-internal-hub]] — to capture and redistribute what frontline teams had learned. Without it, effective prompts and workflows remain scattered and teams waste time in redundant experimentation. The operational move is [[action-build-centralized-hub]].

**Enrichment / verification.** Well supported: McKinsey and others emphasize workflow redesign, knowledge sharing, and management practices — not particular tools — as the drivers of AI value; Salesforce finds managers want hands-on training, clear strategy, and IT support rather than more tools; the AI-resistance literature stresses revising metrics and accountability structures over tooling. **Nuance a domain expert adds:** in smaller or less-digitized organizations, basic tool access, data availability, and integration can still be genuine blockers — treat infrastructure and tool access as interacting constraints, not mutually exclusive. Testable via cross-firm comparisons of adoption outcomes controlling for tool stack.