Part 2:  What scaling in a zero-click environment requires and why more content isn’t the answer to all of your AEO problems.

Read Part 1: What the Zero Click Era Means for Your Brand: A Conversation with Dan Dawson

The data Dan Dawson brought back from Profound's Zero Click New York event is the kind that tends to produce the wrong reaction. Eight out of ten B2B buyers select from options surfaced in their first AI chat session. Fifty percent start their research there. And a lot of marketing teams hear that and immediately reach for tools: more content, a content calendar built around AEO keywords, and a marketing engineer to operationalize the whole thing.

The instinct to scale is understandable, but the timing is wrong. And getting the order of operations backward is exactly how brands end up producing more content that AI systems still can't cite accurately.

This is Part 2 of my conversation with Dan. Part 1 covered the data from the event, the Ramp agent experiment, and what unsolicited editorial means for your brand. Here, we're getting into what the data asks of you, and why the answer isn't what most teams are reaching for.

The Mercury story is about positioning, not production

Dan's neobank search is the clearest proof point in the whole conversation. He wasn't looking for the brand that had published the most content about neobanking. He ran queries, got a list, Mercury was on it, and Mercury's presence in that first session was enough to lodge it in his head. Three or four days of further research later, he went with them.

"If you're not showing up in that first primary search session and you're not on that shortlist," Dan said, "you could be the right choice, the best far and away, but you're not showing up."

Mercury earned that shortlist spot because their positioning was specific and consistent enough to survive a single AI response. That's a different problem than most teams are trying to solve, and it requires a different starting point. When we work through AEO audits with clients, this is the pattern we see consistently: the brands showing up aren't outproducing anyone. They're outclarifying them.

AEO content work is a brand clarity project with a content execution layer

Here’s how we see this at Edgar Allan: Most content agencies still want to start with the keyword list or the editorial calendar and work their way up toward brand clarity. The direction that produces AI-citable content runs the other way.

You start with what the brand stands for, who it's for, and what it uniquely knows. Then you build the content outward from that foundation. Not because this is a philosophical preference, but because it's the only sequence that produces content with a defensible point of difference.

An AEO content project that starts without brand clarity isn't a content project. It's a brand clarity project in disguise, and it will keep revealing itself as one until you do the upstream work.

The listening phase is where that upstream work happens, and it's worth being specific about what listening means here. Before any writing starts, the real work is paying attention: to the questions buyers are asking in your category, to the knowledge that only exists inside your company and the specific way you solve problems, and to the conversations already happening between your customers and your sales team, on stage at your events, in your podcasts, in the content that comes from the core of the business rather than from a brief. That's the source material. Everything else is execution.

This is why content produced from a brief tends to read like content produced from a brief. And why the content that AI systems cite, and that humans share, tends to come from somewhere more specific: the intersection of the questions buyers are genuinely asking and the knowledge only that brand can answer. Where those two things overlap is where content becomes defensibly unique. When that intersection is clearly established, AI systems are drawn to it. When it isn't, no amount of production closes the gap.

Scaling authenticity requires something to be authentic about

Dan and I both landed on the phrase "scaling authenticity" during the conversation and moved past it. It's worth stopping on.

Dan's framing in the conversation was direct: "It really is driven by authenticity, but then just being there. So it's kind of like, how do you hyperscale authenticity?"

The tools available right now, like Profound and AirOps, which are agent workflows built around content production, are genuinely powerful at scaling. What they can't do is manufacture something worth scaling. If what's already there is a vague value proposition and a content library built around keyword clusters rather than genuine company knowledge, the agents will scale that, and do it faster, more consistently, and across more surfaces than any human content team could manage, but do you really want them to?

The listening work described above is what makes scaling authentic rather than just efficient. When you've identified the questions your buyers are genuinely asking, the knowledge only your company holds, and the specific way you solve problems that no competitor solves in quite the same way, you have something worth putting into a production workflow and blowing it up over multiple pieces of content. So, clarity first, then infrastructure, then scale.

At Edgar Allan, this is why our approach to Visibility Engineering and Optimization starts upstream. Brand clarity and site architecture come before content infrastructure, not after. The brands that will look back on this period and understand why their AEO investment compounded are the ones that treated the listening and positioning work as the real project, with content execution as the output. The ones who started with the content calendar and hoped brand clarity would emerge from volume will spend years optimizing a muddled signal.

The marketing engineer role is being misread

Most teams hearing about the marketing engineer role are picturing a content operations hire: someone to run the tools, manage the agent workflows, and keep the production machine moving. Dan's framing at the event was more specific than that.

The role he described sits closer to brand strategy and site architecture than to content production. It requires someone who can interpret AEO signals and build the workflows to act on them. And those signals are only meaningful if the brand being measured is differentiated.

A marketing engineer hired into an undifferentiated brand will optimize for a signal that doesn't mean what the team thinks it means. They'll get good at measuring a muddled position more efficiently. The problem is sequencing, not talent. Getting the most out of the role requires doing the upstream work first.

Dan's read on where the role is headed is worth keeping in mind: "Even using agents, building workflows, it's still something where you’ll want that led by a human." The role scales the work. It doesn't replace the judgment about what the work should be building toward.

Brand clarity is a leading indicator

Most attribution models are built to measure what already happened: a form fill, a click, a conversion. They're not built to measure the buyer who added your brand to their consideration set after a single AI chat session, before ever visiting your site. That buyer is invisible to your reporting. And in a zero-click environment, that buyer is increasingly the rule.

Dan put it plainly at the event: no one at Profound's Zero Click New York left with a clean answer on attribution. "When you have zero click, when you have a big breaking of the traditional funnel, how do we track everything?" That question is still open, and this post isn't going to pretend otherwise.

Here’s how to think about this: brand clarity functions as a leading indicator in this environment. If AI systems are describing your brand accurately and consistently, you're appearing on shortlists that no attribution model you currently use will ever surface. That's a feature of how buying is changing, and it argues for treating brand investment differently than most teams currently do.

Teams waiting for a dashboard that confirms what they should have already decided will keep waiting. The brands getting this right are building the visibility signals that precede conversion, not the ones that follow it. As Dan put it: "If you're not investing in some sort of AI measurement, AI insights, you're flying pretty blind."

The AEO audit Edgar Allan runs gives teams a starting point for understanding where they stand: how AI systems are describing your brand, what's accurate, what's outdated, and what's simply missing. That's a reasonable place to begin building toward a position you can measure from.

Start with what you own, then scale what's worth scaling

Start with the content you own. Your website is the one surface you control completely, and making it citation-ready sets the discipline for every other surface that matters. Write honestly about your products, including where they're not the right fit. Build in date stamps and clear structure so agents can parse and cite your content accurately. Invest in AI measurement before you need it, not after. 

Before you scale, do the listening work: the conversations your sales team is already having, the questions that surface in client calls, the knowledge that only exists inside your company and hasn't made it onto a page yet. That's where the defensibly unique content comes from. Scale production with agents once you've found it.

The brands that come out of this period ahead will be the ones who got clear on what they stand for before they asked AI to say it for them. The tools, the workflows, the marketing engineer role, all of it works better when the brand underneath is specific, differentiated, and honest about what it knows.

If you're not sure where your brand stands, that's exactly what our AEO audit is designed to surface. It shows you how AI systems are describing your brand, your competitors, and your category right now, before you build a content plan around gaps you haven't confirmed yet. Most brands that run it find the results clarifying in ways that change what they work on next. Start here.