Answer Engine Optimisation (AEO): A discipline that won't sit still

A question landed on my desk this week. Not one I was being asked to answer on the spot, but one to sit with because it keeps coming up in one form or another. Will AI impact my brand's growth?  

I wrote TRA’s first AEO strategy in November 2025. I rewrote it in April 2026. And today, in August, it’s back on my desk. Once you strip away the panic, the question I think we’re actually being asked is: does AI-assisted search change how people choose a brand, or does it just change where they happen to be standing – a shop aisle, the boardroom, a chat window – when that decision gets made? I don't think anyone in this discipline has earned the right to answer that with certainty yet – the measurement is too young, and the opinions are too loud for how little any of us actually know. What follows is a personal perspective for the marketers who don’t know where to start, from someone who's been paying closer attention to AI-assisted search than most.

Does AEO replace brand building?

No. To understand why, we first need to understand what AEO is and how an AI model arrives at an answer.

What is AEO?

Answer Engine Optimisation (AEO) is a new discipline, in a rapidly evolving and fragmented category that can't yet agree on a name – you'll also see it as Generative Engine Optimisation (GEO), LLMO, or AI search optimisation, and no two definitions are the same. I'm using AEO throughout. In simple terms, it is the practice of optimising online content through placement, structure and format, so that AI-assisted search tools – such as ChatGPT, Google's AI Overviews and AI mode, and Claude – can easily read, extract, mention and cite it as a direct answer to a user's question.

How does an AI model surface answers?

Until recently, I understood broadly that AI models use different mechanisms to surface answers. Good. What I hadn’t properly understood was what that means for the work we’re doing on brand building and the work we’re doing on AEO. In short, AI models draw on “parametric” pathways and “retrieval” pathways.  

• Retrieval pathways: Where an AI model triggers the retrieval pathway, it is retrieving a set of relevant and reliable passages from sources such as live web or licensed datasets to return an answer. This pathway is exclusively responsible for brand citations and it’s where technical AEO (and SEO) plays an important role. Think AI access governance, technical discoverability, relevant and extractable content and consistency of message validated by external sources.  

• Parametric pathways: Where an AI model uses parametric pathways, it is effectively answering from memory by drawing from the vast amounts of data it has been fed through training. This is where brand mentions can happen and it’s where the science of brand – the sort that comes from consistent, measured and long-term investment – plays a more meaningful role.  

Some models are parametric-first, some are a balance of the two, and others are exclusively retrieval. I have found the headlines tend to focus on the perils of retrieval with urgent calls to immediately adopt AEO, while underplaying the role brand still plays in shaping what gets surfaced. That nuance is both comforting and notably underrepresented. Distinctive assets aren’t dead. I don’t need to build a second website for AI. AEO and the science of brand can comfortably co-exist in pursuit of growth.  

That realisation isn’t where this story ends though. It's just one example where something reopened for me, landing a piece of foundational understanding that I hadn’t yet grasped. If there is a lesson in this, it’s that AEO and a marketer’s curiosity should go hand in hand.

Is AI visibility measurement actually a decision tool yet?

“If AI Doesn't Mention Your Brand, You Don't Exist”. Oof. For a category that is barely a year old, the conviction is astounding. Clickbait headlines should always generate a degree of scepticism, and marketers should know that better than most. I’m not immune to this hysteria, but the question I keep coming back to is whether AI visibility measurement is even mature enough to inform strategic decisions yet. The agencies and platforms would have you believe it is – I’m not so sure.  

But this is 2026, and if you aren’t moving at the speed of AI then you’re probably falling behind, right? Wrong. We give ourselves such a hard time for falling short of perfect, but everything else in a modern marketing stack earned its place in a decision by proving itself over years. AI visibility tools haven't had that yet. Measurement approaches vary provider to provider. Nobody can agree what “good” looks like. And there doesn’t seem to be any alignment on where AEO sits within an organisation, with one report from Semrush having it sit across more than six disciplines.  

That doesn’t mean it is unimportant. It makes it premature to treat a single visibility score the way you'd treat a mature, load-bearing metric like unprompted awareness. The tool we’ve landed on at TRA is complicated and confusing, and the numbers we’re seeing today are wildly different to what we were seeing earlier this year when it confused us with Trades Recognition Australia and a leading provider of caravan accessories. Not exactly comforting, but it’s a start and that’s exactly the point. It’s given us our baseline metrics, it helped to inform our priorities, and we’ve had some wins to be proud of which feels more important than having it all sorted out right now.  

AI changes where people decide – not how

People still build their picture of a brand exactly the way they always have – through accumulated emotional responses to what they see, hear, experience and remember. Some of those responses are now being triggered inside a generative AI answer instead of a search result or a shelf. That doesn't make AI the source of the feeling. It makes it one more place the feeling gets triggered – an intermediary, not a replacement. Which changes the question worth asking. Not "how do I win in AI?" but "how differently does winning look, depending on what's actually at stake for the person doing the choosing?" I have considered this from two angles: high-involvement and low-involvement purchases.  

High involvement: AI speeds up the research, but people still validate it

I've spent all my career marketing high-involvement purchases – high-stakes, high-cost, relationship-driven, long sales cycles. That's the lens I tend to look through when considering the impact of AI. We already know the buyer journey is much more involved for an enterprise solution than it is for a loaf of bread, but how will AI change the shape of those journeys and what does that mean for brand?  

• A study by Gartner found 45% of B2B buyers used generative AI in a recent purchase but 69% still turn to a sales rep to validate AI-generated insights.

• For buyers researching software specifically, G2 reported 71% rely on AI chatbots in the research process, but when AI leaves out a brand they trust, or gets something wrong about one they know, most buyers seek out a second opinion.  

• In a study by TrustRadius, 94% of technology buyers who'd used AI said they'd fact-checked it at least some of the time before trusting it.  

So, AI-assisted research may be fast, easy and low-friction, but it only takes the buyer so far. Credibility, reassurance and decision support still rely heavily on brands and humans showing up when it matters.

Put simply, people feel first, think second, and then potentially act – that sequence doesn't stop being true because an AI model is now part of how someone researches a decision. What I'm less sure of is whether it shows up the same way across categories. For a multi-year contract, feeling shows up as trust in a person, chemistry in the room, credibility built over years. For a weekly shop, it might show up as nothing more than an old preference that’s become habit, even once I've handed the act of ‘adding to basket’ to something else.

Low involvement: you'd already stopped caring

I recently built an AI agent to handle my weekly meal plan and shopping list. For the products I care about, brand preference is non-negotiable. I know what I like, and the agent has very little room to deviate. Bread, for example, isn’t just bread to me. It’s taste, dietary considerations and value.

But there are plenty of products I barely think about – bread might be that for you. And if I wasn’t actively choosing between brands before, I’m unlikely to start interrogating AI about them now. I’ve outsourced the decision, with a few shortcuts and parameters I’m unlikely to revisit.

Which takes me to my point – albeit based on a sample of one. The categories where I have real brand preference are the ones I've protected, deliberately, by telling the agent what to choose. The categories I never thought about are the ones I've handed over completely, no questions asked. If a brand is at stake in that second group, it isn't losing to AI. It's losing to indifference that AI just made easier to act on – and it was probably losing to that indifference already, AI or no AI. I’d put that challenge squarely in the category of brand, not AEO.  

Don’t panic – build brand and get curious about AEO

I don't think anyone serious about this discipline would claim to have it all figured out right now – and I'd be seeing red flags if they did. What I have is a few more months of paying close attention than most of the people asking me about it, which qualifies me to say this much: don’t panic, keep focusing on brand building and get curious about AEO.  

If you're wondering how much of your brand's future runs through channels you don't control – nobody can answer that yet. What hasn’t changed is the importance of building emotional connection and relevance with the people you're trying to reach – and measuring it. That's a steadier foundation than a visibility score that couldn't tell us apart from a caravan accessories supplier six months ago, and it's still the part I'd confidently stand behind in a board meeting.  

See how TRA approaches brand tracking.

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