What am I now? When the job title no longer explains the work

If someone asks what I do at Sage, the straightforward answer is that I’m a Content Marketing Manager. That’s my actual job title, and it already describes a broader remit than writing articles. I work on research-led narratives, customer stories, briefs and finished content; I also have the usual realities of production deadlines, stakeholders and approvals.

The awkward part is explaining the work that increasingly sits across those responsibilities rather than neatly inside any one of them.

If I say I work in content marketing, that is true. But it doesn’t immediately explain why I might spend a morning comparing practitioner evidence with AI-search questions or an afternoon testing how editorial decisions could be made easier to recover. If I say I’m an AI strategist, I’m claiming a formal role I don’t have. Editorial strategist gets closer, but still misses the practical systems work.

I don’t think I’ve suddenly become a different professional or moved from a junior writing role into strategy. Strategy was already part of the job. I think the boundaries between editorial, audience insight and technical workflow design have become harder to describe.

A content brief is not always where the work begins

Take Hidden Hours, the research-led work on the pressures facing accountants and bookkeepers. There is plenty of traditional content work in it. Someone has to interpret the findings, write the articles, make the argument clear and produce material that people can actually read.

But the questions I find myself spending time on are often one step earlier. What are practitioners telling us that the survey alone doesn’t explain? Which example is strong enough to make an argument credible? How does something heard at an event relate to what a customer said in an interview? Does a video conversation give us a more useful explanation than the one we originally planned to write?

None of those questions can be solved just by making the draft more polished. They change which draft deserves to exist, what the reader should understand and which evidence needs to be carried into it.

I still write the piece. I increasingly want to know whether we started with the right piece.

That may sound like ordinary editorial judgement, because in many ways it is. A good journalist or editor has always done some version of this. What is changing for me is how much of the job can be organised around those decisions rather than leaving them implicit inside the writing process.

GEO has made the job stranger, in a useful way

Generative engine optimisation, or GEO, was initially easy for me to place alongside SEO: understand how people are finding answers and make the information more useful and discoverable.

Then I started working with questions around Making Tax Digital for Income Tax. The obvious task was to improve content explaining quarterly updates, deadlines and responsibilities. But once I began connecting those questions to what practitioners were actually saying at Accountex, the job became something else.

I was interested in the gap between the official explanation and the difficulty people were experiencing. Did they understand the difference between a quarterly update and a final tax calculation? Were they struggling with the submission itself, or with collecting the right information from clients before they could submit it? Where did an apparently simple digital requirement create more chasing, clarification or uncertainty?

That is not only an optimisation question. It is a reporting question, a customer-understanding question and a question about which evidence the organisation needs in order to answer responsibly.

I explored the mechanism in Becoming the GEO journalist. In practice, it means that a potential content gap can become a brief to go out and learn something, or a reason to retrieve useful evidence the organisation already holds somewhere else.

I’m part of a wider GEO effort, not the person running the whole programme, and I cannot claim that asking better questions has automatically improved rankings or revenue. What I can point to is a change in how I approach the work: AI-search questions are becoming another way to decide what deserves investigation before content production starts.

I’m also thinking about what survives the article

Customer stories have exposed a different problem. We interview someone, capture useful detail, turn it into an approved story and move on. The finished article might be good, but a surprising amount of the thinking behind it becomes difficult to recover.

Why did a particular quote make the cut? What did we wish we’d asked? Which claim needed extra checking? Where did two reviewers have different ideas about the article’s purpose? And will the person working on the next customer story know any of this happened?

I’ve been experimenting with a structured way of retaining some of that knowledge: proof points, evidence gaps, questions we wish we’d asked and editorial lessons that might be useful next time. It’s a small working experiment, not a fully adopted company-wide system.

That’s an important distinction. I can build a register, create a workflow and make a convincing demonstration. I still need to establish whether it helps somebody else produce a better brief, find a source more reliably or avoid repeating a mistake. Until that happens, I’ve demonstrated a possible method, not an organisational improvement.

Even so, I find the work absorbing. The problem began in writing and editing, but the response looks more like knowledge design. I’m asking not only how to produce a better article but how to make the experience of producing it available to the next person.

This is where AI becomes practical for me. It can help retrieve context, compare material and make a first pass at finding patterns. It cannot decide whether two similar claims are really the same, whether a customer has approved a quotation or whose judgement should settle an editorial disagreement.

Maybe I’m describing an old job with more tools

There is a fair objection to this whole account: aren’t research, synthesis, audience understanding and judgement simply what good editors have always done?

Yes. I’m not claiming that AI invented editorial strategy or that a writer becomes an engineer because they can prototype a workflow. When I look back at journalism and agency work, I can see the same instincts all the way through: interrogate the brief, find out what is missing, make sense of conflicting information, decide what deserves attention.

The difference is partly one of reach. I can now work across more transcripts, earlier decisions and related projects without needing to reconstruct everything manually. I can test a small information structure myself rather than explaining what I want in a deck and hoping it might someday be built.

But there is another, less comfortable possibility. Sometimes an expanding job is just an expanding workload. The old deliverables remain, new responsibilities arrive, and the person doing them is expected to be pleased that AI makes everything possible.

I don’t want to confuse having more things to do with having more influence over what gets done. Those are very different changes.

For this to be meaningful professional development, the work needs time, authority and a test of whether it helped. If an evidence review changes nothing, if a GEO observation never reaches a brief, or if a knowledge workflow works only when I’m the one operating it, then the new language around my role doesn’t make those activities valuable.

That is the standard I want to hold myself to, especially when I’m tempted to describe the work in a more impressive way than the evidence supports.

The title can wait. The contribution has to become clearer.

I’ve written about the content job moving up a level and the writing job unbundling. Those pieces looked at what is happening to the work and the market around it. This question feels more personal because my existing Content Marketing Manager title already includes strategy and delivery, but doesn’t fully capture the way research, AI search and workflow design now intersect within the work. The production workload remains real even as those intersections grow.

Outside Sage, I’m developing Editorial Intelligence as my own way of connecting evidence, narrative and the systems around publishing. That gives me somewhere to test ideas openly. It does not mean Sage has adopted those ideas as an operating model, or that a personal experiment is equivalent to a deployed service.

I could probably find a new label for myself. Content engineer. GEO journalist. Editorial architect. Applied AI strategist. Each catches something true and leaves something out.

I’m less interested in collecting the labels than I am in being able to answer a more useful question: what changed because I was involved?

Was the brief better because we found an unanswered question? Did we avoid making a claim the evidence couldn’t support? Did someone retrieve a useful source that would otherwise have been lost? Did the next project start with more knowledge than the previous one?

Those are modest tests compared with announcing a new profession. They’re also tests I can actually work towards.

So what am I now?

I’m a Content Marketing Manager. I increasingly spend time connecting the evidence, decisions and workflows that make content worth producing — not just developing the content itself. I don’t know whether that requires a different title, and I don’t think I need one before I can show whether the work matters.