Your Next Customer Is an AI Agent. Here’s How to Market to It.

When most people hear “AI marketing,” they think of using AI to do marketing. This may mean writing copy faster, generating ad creative, automating outbound sequences, etc. That’s useful and has a huge impact on how marketers approach their jobs, but it’s not what I’m talking about. 

I’m talking about marketing to AI. Specifically, to the agents that are starting to shop on behalf of people. 

Chapter one is answer engines 

The first wave of AI search optimization (AEO, GEO, AIO, pick your acronym) was built around one shape of AI: the chatbot or answer engine. A shopper asks a question, an LLM writes an answer, and somewhere in that answer your brand shows up, or it doesn’t. 

That framing got a lot right, but it is still largely rooted in how we think about traditional search, the only real difference being the shift from keywords to questions. That said, it has sparked a new discipline within marketing, one where many brands are tracking and investing energy in understanding how AI models read their site and content, which sources AI cites when answering brand-relevant questions, and how they stack up against competitors across LLM responses. 

All of this is important, but just the tip of the iceberg. What marketers need to do next is evolve their GEO strategies to not only ensure their brand appears in the first answer, but also to ensure that as a shopper travels through the full buyer’s journey within the same AI search conversation, their products and services remain relevant and ultimately win the customer decision. The later portion of the journey used to happen after a shopper clicked from their initial Google search to your website. Now it lives entirely between the shopper and their agent. 

This is where we’ve seen brands quietly lose. When my team ran 320 simulated shopper conversations, nearly three in ten brand mentions never turned into a recommendation. The brand showed up in the answer, and then somewhere further down the conversation the agent picked someone else. That lines up with independent research showing AI shopping assistants get worse as conversations get longer. Getting mentioned and getting picked are two different things, and right now most brands are only measuring the first one.

Chapter two will encompass more than one shape of agent. 

It is important to note that not all agents look like chatbots, and as AI Search evolves into new formats, so must our marketing strategies. 

Look at Meta’s Muse. It launched on Sept. 8 and quickly climbed to the top of the App Store. It’s not really a chatbot; it’s a personal agent that runs on its own cloud machine, opens its own browser, scans retail sites, compares prices, and checks out for you. It keeps a shopping profile on each person, tracking their actions and tastes. 

And Muse is one example, but there are agents that live in text messages, inside browsers, in glasses, and in voice. Many brands and retailers are building their own agentic shopping experience too. Each iteration and version finds products differently, reads them differently, and ultimately guides their users down buyer journeys in different ways. These are all new potential blind spots that we, as marketers, must learn to address and influence to reach our shoppers via their agent. 

A few things already stand out: 

They pull from everywhere. In one early test, most of Muse’s product picks came from live websites rather than Meta’s own catalog, even though the catalog offered far more options. Your site, your feeds and every catalog you’re listed in all count. 

They act, not just answer. An agent that can check out has to get through your site first. Amazon has already blocked Muse. Plenty of brands are blocking agents without knowing it, through bot protection built for a world where bots were the threat. 

They remember. A profile that says “Wide feet, and need extra support for bad knees” shapes every future recommendation. That’s potential brand loyalty, written down by a machine. 

They report to a human. Muse asks for approval before it makes a purchase. The agent has to justify its pick and give its reasons. 

That last point matters more than most brands realize, and it’s a big part of where that three-in-ten number comes from. Agents don’t just find you, they do a lot of the “choosing” on behalf of their shopper. In our simulations, in which shoppers were looking for headphones under $150, the shift from mentioning a brand to recommending it occurred once the shopper provided a budget. One premium brand was mentioned in 82 answers and recommended in none. Another got named as a premium option and then dropped as soon as the price cap came up, losing the chance to sell premium value to justify a higher cost. 

Meanwhile, a value brand went from never being recommended when budget came up to almost always being recommended afterward, which is a price-fit story, not a trust story. The brands that dropped off weren’t unknown to the agent, they just didn’t fit the shopper’s constraints, which meant the agent had nothing to justify the pick with when it came time to choose. 

Chapter 3 is about naming the buyer, not the interface 

Here’s the issue with AEO and GEO as labels: they name the interface. 

A lot of people, Forrester included, are now saying the agent is your new customer. I don’t think that’s quite right. The customer hasn’t changed; the person is still the one you’re trying to win, but more and more they’re represented by an agent that does the buying for them, on a journey we aren’t used to. That agent is both a new gatekeeper and a new audience, and marketers need strategies for both. 

So I think the more useful frame is marketing to agents. Not instead of GEO, but as an extension of. If GEO is how you show up when an agent answers, then marketing to agents more broadly is how you win. Being able to influence the entire AI assistant shopping journey is the target we need to keep in view. I think about this as “AI Search marketing” broadly. 

We’ve seen this before. “Internet marketing” eventually split into search, social, email, ecommerce, affiliate and a dozen other disciplines. Marketing to agents will split the same way. The brands that do well will be the ones that start treating the agent as a buyer worth winning now, not a bot to block, while the shapes are still settling. 

How to start 

You don’t need to rebuild anything this quarter. A few moves go a long way: 

1. Audit access. Find out which agents can reach your site and which you’re blocking by accident. Then decide on purpose. 
2. Pick your rails. Agents check out through specific payment and catalog partners. Know which ones you support, and which agents that lets in. 
3. 3. Treat product data like ad copy. It’s the first thing your customer’s agent reads. Specs, price, stock, shipping and returns, ratings and reviews, etc. In our simulations, price fit was the biggest reason a brand was mentioned but not picked, so make sure an agent can easily see where you land relative to a shopper’s budget and can substantiate why the shopper should consider a premium upgrade. 
4. 4. Test beyond chatbots. Check how your brand shows up when an agent shops, not just when a chatbot answers. Run the same request through a few different agents and compare. And don’t stop at the first answer; follow the conversation all the way through to the pick, because that’s where we saw brands drop off. 
5. 5. Stay nimble. Agents, platforms and access rules are changing month to month. We all need to track it and adjust as we learn. 

The shopper hasn’t changed. Their representative has. 

People still want the right product, at a fair price, from a brand they trust. What’s new is that more of the legwork goes to software that reads everything, never sees your homepage hero, and remembers how it went. 

Market to the buyer, win the customer. Build for the whole job, not the initial chat response.

When most people hear “AI marketing,” they think of using AI to do marketing. This may mean writing copy faster, generating ad creative, automating outbound sequences, etc. That’s useful and has a huge impact on how marketers approach their jobs, but it’s not what I’m talking about. 

I’m talking about marketing to AI. Specifically, to the agents that are starting to shop on behalf of people. 

Chapter one is answer engines 

The first wave of AI search optimization (AEO, GEO, AIO, pick your acronym) was built around one shape of AI: the chatbot or answer engine. A shopper asks a question, an LLM writes an answer, and somewhere in that answer your brand shows up, or it doesn’t.