A PR-specific AI copilot that builds and critiques media lists using current journalist activity, outlet/beat changes, adjacent coverage, peer-company signals, and an agency's internal judgment. It should argue for or against targeting a journalist rather than merely matching keywords to past articles.
Described on the record by a senior operator who runs this workflow every day.
Shown because the operator described this sequence step by step.
"I find, at least in all the workflows that I've tried, that finding the so building a media list, right? If we take a step back, building a media list in comms is incredibly important."
"You would think that they would be very powerful. No, there would be super powerful tools and I wouldn't be manually or my team wouldn't be manually creating media lists. We find that's just not true."
"I think there's room for for a really powerful a powerful tool that I can prompt and just ask a simple question, you know, hey, what did you know, Ashley Vance at Core Memory, you know, do you think he'll cover this topic? And it can kind of argue with me as a coworker would."
Maintain a continuously updated profile for each journalist, combining recent articles, beat history, outlet changes, adjacent-company coverage, internal agency notes, and news context. Given a client announcement, recommend targets with a rationale, identify reasons not to pitch, surface freshness/conflict risks, and support an interactive debate mode that pushes back on the user.
A PR-specific AI copilot that builds and critiques media lists using current journalist activity, outlet/beat changes, adjacent coverage, peer-company signals, and an agency's internal judgment.
It should argue for or against targeting a journalist rather than merely matching keywords to past articles.
It was raised by 1 operator in public relations.
Current LLM workflows are poor at identifying which journalists are likely to cover a story next.
Teams still manually build media lists because existing outputs are outdated and do not handle the contextual, tangential judgment involved in beat matching.
The operator described these steps:
Tools named in the interview:
The operator who described this problem is the obvious first customer — they named the pain in their own workflow, so they are the buyer to build alongside.
Product manager · scoped to this opportunity
AR Product Manager
I have 3 sourced passages on "AI Journalist Intelligence and Media-List Copilot" from 1 operator. Ask me for scope, user stories, failure modes, or a full PRD — I answer from the transcripts first, and label anything that isn't sourced.
3 sourced · 1 AR analysis