---
id: "question-legacy-system-integration"
type: "open-question"
source_timestamps: ["§ They Reduce Uncertainty", "¶7"]
tags: ["integration", "legacy-systems"]
related: ["entity-blake-moret", "entity-rockwell-automation", "claim-exec-uncertainty-travels-downstream"]
resolutionPath: "Case studies detailing the technical and operational bridging strategies used by manufacturers to maintain production while legacy and AI systems run in parallel."
sourceUrl: "https://hbr.org/2026/05/the-best-manufacturers-build-ai-with-workers-not-for-them"
sourceTitle: "The Best Manufacturers Build AI with Workers, Not for Them"
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-cl-78-build-ai-with-workers"
---
# How to Manage the Transition Period With Legacy Systems?

**Open question:** How should workers navigate the friction between new AI tools and entrenched legacy systems during a multi-year transition?

The source notes — via [[entity-blake-moret]] of [[entity-rockwell-automation]] — that legacy-system compatibility problems, governance uncertainty, and data-quality issues complicate AI integration and delay outcomes. But it does **not** provide a specific framework for managing that friction during the transition, which is a live gap given the fear dynamics in [[claim-exec-uncertainty-travels-downstream]].

**Resolution path:** case studies detailing the technical and operational bridging strategies manufacturers use to keep production running while legacy and AI systems operate in parallel.

> **Related counter-perspective (enrichment):** in some settings, centrally standardized architectures are necessary for safety, cybersecurity, compliance, or scale — and too much local, worker-led variation can slow rollout or fragment governance, *especially* where legacy systems and interoperability constraints dominate. This tension is unresolved in the source.