You can't turn AI search off

What happened this week in AI (eCommerce, consumer, retail edition)?

Shopify announced integrations with v0 by Vercel, Replit, and Manus AI. Describe your business, the tools build the store, and when you are ready to sell, Shopify connects automatically. Voila. The barrier to founding a brand keeps falling.

Reddit’s r/biohackers moderators are flagging something more instructive. Peptide and hormone therapy companies have been flooding the subreddit with promotional posts designed to be scraped by ChatGPT and Google AI Search. The tactic has a name: AEO, AI-engine optimisation. Or Growth Hacking 2026. Brands are already treating AI search as a channel worth gaming. However, one needs to be a little careful about the way in which they infiltrate traditionally sacred spaces like Reddit threads. Do so at your own peril.

I did a webinar with Chris from Glara last week. In our prep discussion, he said something super interesting - you can’t turn AI search off. Your consumers are going to be using AI spaces to find you, whether you like it or not. So if you can’t turn AI search off. What do you do?

Where the last eighteen months went

About eighteen months ago, AI search visibility was generating real interest. The framing was AEO, answer engine optimisation, and early work showed enough promise that agencies, including ROIROI, were building programmes around it.

Then it faded. The narrative that took hold in Q1 this year was that AEO was SEO with a coat of paint. The models were drawing from the same sources anyway. Brands who had spent years on search were already covered. Roadmaps deprioritised it.

Meanwhile, 55% of consumers now turn to AI for product discovery, up from 40% in Q4 last year. One in seven ChatGPT queries is already commerce-related.

We moved our focus toward operational AI, toward agents and automations inside businesses. The demand for customer experience AI softened, but the consumer behaviour kept shifting regardless.

Customer experience AI is how your brand shows up, gets recommended, and converts in an AI-native world. Operational AI is how your business runs more efficiently with AI embedded in its operations.

Both matter independently. Together they compound. A brand that is visible in AI surfaces and running leaner internal operations because of AI has a structural advantage over one doing either in isolation.

The demand coming back now is for the first. The brands that used the last eighteen months to build the second are in a better position to act on it.

Why the SEO comparison produces the wrong answers

The instinct to file AI search under SEO is understandable. Both involve being found. Both reward content and off-site authority. The mechanics underneath are different enough that treating them as the same problem produces the wrong strategy.

With SEO, you competed for a position in a ranked list. Put in the right inputs, improve your position. The system was deterministic and measurable against familiar metrics. With AI search, you compete for trust. The model decides whether your brand and products are a credible answer to a given query, and that decision is not deterministic. Ask the same question three times and you may get three different answers in a different order. There is no position to hold. There is a share of voice across a prompt set, and if you are not tracking it you have no idea where you stand.

Glara’s data puts the split at 40% product data, 60% off-site authority. Get your product catalogue machine-readable first: structured attributes, complete specifications, accurate use-case tagging. Then build authority on substance. Note - the order matters. Authority on top of incomplete product data does not close the gap.

The second difference is who is reading your content. For fifteen years, product descriptions were written for humans. A human reads for feel, for narrative, for the emotional fit between a product and a life they are imagining. An agent reads for structured attributes: weight, dimensions, use case, compatibility. If those attributes are absent or buried in prose, the agent cannot match the product to a relevant query regardless of how good the writing is.

Chris gave a very good example during our webinar. Polaroid’s instant cameras had strong product descriptions. Clear copy, genuine brand voice. An agent asked which camera to take backpacking through Asia could not surface them, because nothing in the data indicated the camera was lightweight or water resistant. Once Glara updated the catalogue with structured, machine-readable attributes, it could. The product did not change. The description did not change. The data did.

This gap sits at product level, not brand level. A brand mention score tells you how often your name surfaces. It says nothing about whether your individual products are being recommended when a consumer asks something specific. In ecommerce, this is important as revenue is at the product level

The buyers arriving through AI models

The shoppers coming through AI models are not casual browsers. They arrive having used AI as a research layer, and by the time they reach a brand they have already done the work a typical search visitor would not do until they were on site. There is data that shows they spend on average 30% more. They are also 2.7 times more likely to land directly on a product page than on a homepage compared with last year. They view more pages and bounce less.

This means there is an interesting change in the dynamic. As Chris put it, “a compression of the user journey”.

A consumer who would previously have arrived on a homepage, browsed to a category, filtered to a product, is now arriving directly at the product already decided. The homepage and the category page are being bypassed. What matters is whether the product data was accurate and complete enough that the AI model surfaced the right product in the first place. If it was not, that your buyer has gone to a competitor.

Now indulge me for a second and consider what happens when that compression goes further, when the transaction happens inside the AI surface itself rather than on the dot com. Most of what makes a PDP convert does not survive that journey. Urgency signals, loyalty tiers, complex configuration, bundle logic: a text response from ChatGPT cannot replicate the experience of seeing your points balance, your member price, the last two units in your size. Yes, agentic commerce will close some of that gap eventually. But right now, the AI surface is a research and recommendation layer, not a transaction layer. The dot com is not dead. It is receiving a different, higher-intent visitor than it used to.

But what happens to brand experience when discovery, research, and decision all happen inside an AI interface? The brand never got to speak. No art direction, no editorial voice, no carefully considered layout. The model surfaced the product based on attributes and authority signals, and the consumer arrived already sold. For commodity and functional products, that is probably fine. For brands whose differentiation lives in how they present themselves, it is a problem nobody has solved yet. The answer is likely that brand experience shifts upstream into the data and content layer that feeds the models. That is a different kind of brand investment than most marketing teams are currently set up to make.

The platform is not standing still

The FT reported this week that OpenAI is preparing its biggest product overhaul since launch. Resources are moving toward Codex, toward business customers, toward agent infrastructure. In other words, a full frontal attack on Anthropic. One senior employee told the paper: “Chat is dead.” Alongside the reorganisation, OpenAI quietly shelved the in-ChatGPT checkout feature it had been piloting.

Brands that treated AI visibility as a ChatGPT optimisation exercise will find themselves starting again. Brands that treated it as a data and catalogue foundation will not. Machine-readable product data, clean attribute structure, genuine off-site authority: none of that becomes worthless when the interface changes. It carries across whatever surfaces come after.

The paid layer is arriving at the same time. OpenAI has opened its ads waitlist beyond the initial 300-brand pilot. Google AI Max has been running since Q3 last year, generating ad copy from whatever is publicly available about your brand and using landing page quality as a bidding signal. Most UK and EU brands cannot act on ChatGPT ads yet. The gap between now and when they can is the window for getting organic foundations in order. The brands that arrive at paid AI with clean data and established visibility will have lower costs and better match quality. First CPMs are high but intent-matched, and the economics will tighten as more brands enter.

The execution gap

Most brand leaders have reached the conclusion that they should be doing this. The question is why so little of it is getting done.

It is an execution problem. There is usually one person, sometimes two, who are across the AI agenda. The rest of the organisation is not. The work that needs doing is cross-functional and unglamorous. It does not fit neatly into any single team’s remit. It needs someone with budget, cross-functional visibility, and enough understanding of the technology to make decisions. Most brands do not have that person, or they have someone who could be, but they already have a full job.

At a panel at Pulse recently, Ian, Chief Commercial Officer at Axel Arigato, put it well: you need an internal shepherd who can move AI projects across functions, with budget and the visibility to see where things are stuck. Without it, you end up with point solutions that do not connect. You might also end up tokenmaxxing your way through your AI budget in three weeks.

What the channel says about you right now

AI search is not a channel you chose to be on. It launched without you and it is running without you. What it says about your products in response to real consumer queries is outside your control unless you have done the work to shape it. For most brands, what it is saying is incomplete, sometimes inaccurate, and losing ground to competitors who moved earlier. Don’t be that brand. Get ontop of it now.