---
id: "action-build-centralized-hub"
type: "action-item"
source_timestamps: ["\\\"§ Breakdown 1: Learning is informal", "while delivery is relentless.\\\""]
tags: ["infrastructure", "knowledge-management"]
related: ["concept-centralized-internal-hub", "claim-infrastructure-scales-adoption"]
speakers: ["Julia Shin", "Sandra J. Sucher"]
action: "Deploy a highly searchable centralized hub for AI tools, use cases, and governance guidance."
outcome: "Scales effective frontline AI practices across the organization and prevents siloed problem-solving."
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"
---
# Build a Centralized Internal AI Hub

**Action.** Construct a [[concept-centralized-internal-hub]] that consolidates AI tools, proven use cases, and governance guidance. Ensure it has a **robust search function** so employees know exactly where to go. This infrastructure captures frontline learnings and redistributes them, enabling cross-project reuse.

**Outcome.** Scales effective frontline AI practices across the organization and prevents siloed problem-solving.

This is the operational form of [[claim-infrastructure-scales-adoption]] (infrastructure, not tool access, is the differentiator) and complements [[action-protect-learning-time]] — protected learning produces knowledge; the hub keeps it.

**Enrichment context.** McKinsey and enterprise AI guides consistently recommend centralized repositories of use cases, prompts, and best practices as the scaling mechanism; practitioner guidance adds accountability matrices and role-transition briefs as governance layers that live in such a hub.