Becoming the GEO journalist

GEO could give editorial teams a new reporting brief: find the questions where the organisation lacks a strong answer, then go out and acquire the evidence needed to answer them.

One version of GEO turns the content team into optimisers.

Find the questions appearing in AI search. Audit the pages. Rewrite the answer. Improve the structure. Add citations. Make the existing material easier for machines to retrieve.

All useful, and it’s the version I’ve argued is optimising the wrong layer when it’s the only version running: it can only ever improve the answer an organisation already has, and says nothing about whether that answer was any good to begin with.

But I think there is another role emerging alongside it: the GEO journalist.

The interesting question isn’t always how do we optimise the answer we already have?

Sometimes it is why don’t we have a good answer yet?

And if the evidence doesn’t exist inside the organisation, somebody has to go and get it.

GEO as a reporting brief

Imagine an AI visibility review identifies a cluster of questions that matter to your audience.

Some are already answered well. Connect and improve those assets.

Some expose a genuine knowledge gap.

That’s where the workflow starts to look less like SEO optimisation and more like journalism.

You can ask:

• What do we actually know about this?
• What evidence supports the answer?
• Where is our answer generic because everybody is drawing from the same public information?
• Who has first-hand experience we haven’t captured?
• What would we need to investigate before we could say something genuinely useful?

Then go and report it.

Interview customers. Talk to practitioners. Question subject-matter experts. Analyse existing research. Commission new research where the gap warrants it. Capture disagreements rather than smoothing them away.

The GEO analysis tells you where stronger knowledge may be valuable. The Evidence Engine is what actually acquires and develops that knowledge — this is what feeds it, not a replacement for it.

Not every gap deserves a reporter

I should be honest about the failure mode this invites before describing the opportunity, because the opportunity is the more seductive half.

An AI visibility review can surface dozens of weak or missing answers in an afternoon. Reporting on even one of them properly — real interviews, real verification, an editor deciding what the evidence actually supports — takes days, sometimes weeks. Treating every gap the tool surfaces as a reporting assignment would recreate exactly the problem I’ve argued content teams already have: commissioning by default because a system generated a plausible reason to, rather than because the question is actually worth the resourcing.

The gate is the same one I’d apply to any other commissioning decision, just fed by a different signal: why this gap, why us, why now, and what would count as evidence that it was worth the reporting effort. A visibility tool can tell you a question is unanswered. It can’t tell you whether your organisation is the right one to answer it, or whether the answer would actually change a buying decision. That’s still an editorial call, not an automatable one.

None of this works without the review that surfaces the gaps in the first place, which is a distinct piece of work from the reporting itself — an AI Visibility Audit is what that review looks like as a standalone engagement, before any reporting brief gets written.

The opportunity is bigger than events

Events make the model easy to see because lots of useful people happen to be in one place.

Instead of arriving with a brief that says make some event content, an editorial team could arrive knowing the questions it wants to investigate. Interviews and video become ways of gathering evidence against those questions. Unexpected answers become new signals rather than inconvenient deviations from the content plan.

But the same idea applies almost anywhere an organisation is already talking to people.

A webinar can be designed partly around questions where the evidence is weak.

A podcast interview can test an emerging argument.

A customer interview can capture language and experience that strengthens a wider knowledge territory.

A roundtable can deliberately explore disagreement.

Sales and support conversations can expose questions customers repeatedly ask but the published knowledge base barely addresses.

An executive interview can turn tacit expertise into attributable evidence.

Even a video shoot changes slightly. The goal isn’t only to obtain a polished film. It is to capture useful, self-contained expert answers that can also become transcripts, quotations, written answers, sales material and source evidence for future work.

The activity becomes a signal-gathering opportunity before it becomes a format — designed signal gathering is the fuller workflow version of the same move, for events specifically.

A different content loop

The conventional event-content loop often looks something like:

event → interview → video/article/social posts → distribution

A GEO-informed editorial loop could look more like:

audience questions → visibility/evidence gaps → reporting questions → signal gathering → verified evidence → connected answers → multiple formats → measurement → new questions

That distinction matters.

The first workflow begins with an occasion and asks what content can be extracted from it.

The second begins with something the organisation needs to understand or answer better and uses the occasion as an opportunity to acquire the missing evidence.

It also means GEO doesn’t have to push organisations towards producing even more generic content.

It could do the opposite.

Original reporting becomes more valuable when production gets cheaper

Generative AI is extremely good at reformulating information that already exists.

That makes another competent summary progressively less defensible as an editorial asset.

A customer explaining what actually happened inside their business is different.

So is proprietary research. Practitioner experience. A useful disagreement between experts. A surprising observation from a room full of customers. A claim that can be traced to somebody who has actually done the work.

These things existed long before GEO. Journalism, qualitative research, voice-of-customer work and good content marketing have always depended on them.

What’s new is the possibility of using AI visibility itself as one input into deciding where to point the reporting effort.

That creates a useful division of labour.

AI can help identify patterns, connect transcripts, compare existing material, expose gaps and turn evidence into multiple useful forms.

Humans can decide which questions matter, recognise when an answer is suspiciously generic, ask the follow-up question, notice the unexpected comment and judge whether the evidence actually supports the story.

Where I’ve seen a version of this work

I don’t have a case study that started with an AI visibility gap and ended in a reporting brief — the framing here is new, so I should say that plainly rather than let a real example imply otherwise.

What I do have is Accountex 2026, which is the closest precedent: Hidden Hours research launched into a room full of the practitioners it described, and what mattered wasn’t the data alone, it was going in with questions still open and treating the room’s reaction as evidence rather than as an occasion to cover. That’s the reporting instinct this piece is arguing for, running a year before the GEO framing existed to name it. The gap analysis is what’s untested — using AI visibility specifically to decide where to point that instinct next.

Don’t let GEO dictate the reporting

There is an obvious failure mode here.

If every interview question is derived from what an AI visibility tool says people are asking, you’ve just created another optimisation machine.

Reporting needs room for surprise.

A useful brief might deliberately combine directed investigation with open listening. Go in with questions worth answering, but leave enough space for somebody to tell you that you’ve been asking the wrong question.

That is part of the value of getting out of the content system and talking to actual people.

The strongest case against this

The honest objection isn’t that the idea is wrong. It’s that most content teams don’t have anyone who can actually do it.

Original reporting is a specific, trained skill: knowing which follow-up question gets past the rehearsed answer, when a source is worth pressing and when to let a contradiction stand instead of tidying it away, what’s usable on the record versus what needs corroborating before it can carry an argument. Journalism training exists because those judgements are learned slowly, usually by getting them wrong first. A content team that’s spent the last several years optimising and repurposing doesn’t acquire that capability by being handed a list of AI-visibility gaps and told to go and report on them.

That’s a real constraint on how fast this can be adopted, not a reason the model is wrong. It’s also, not coincidentally, the argument for why editorial jobs are moving upstream rather than disappearing — this is exactly the kind of judgement AI doesn’t do, and exactly the kind that takes longer to build than a workflow diagram implies.

Maybe this is one of the new editorial jobs

I don’t know whether “GEO journalist” will ever be a real job title. It probably doesn’t need to be.

But the function makes sense to me.

Someone sits between AI visibility, organisational knowledge and original reporting.

They don’t just ask how to make existing content more retrievable.

They ask what the organisation should know that it doesn’t know yet.

Then they find the people, conversations, research and moments that can help answer it.

In that version of GEO, the editor doesn’t become less important because machines can produce more content.

The editor gets sent further upstream — towards the questions and the evidence.