AI doesn’t care about your beautiful website.

Visit any fashion brand’s homepage and you’ll see all sorts of dynamic or interactive elements from image carousels to dropdown menus that are designed to catch shoppers’ eyes and ease navigation.

To the large language models that underlie ChatGPT and other generative AI, many of these features might as well not exist. They’re often written in the programming language JavaScript, which for the moment at least most AI struggles to read.

This giant blindspot didn’t matter when generative AI was mostly used to write emails and cheat on homework. But a growing number of startups and tech giants are deploying this technology to help users shop — or even make the purchase themselves.

“A lot of your site might actually be invisible to an LLM from the jump,” said A.J. Ghergich, global vice president of Botify, an AI optimisation company that helps brands from Christian Louboutin to Levi’s make sure their products are visible to and shoppable by AI.

The vast majority of visitors to brands’ websites are still human, but that’s changing fast. US retailers saw a 1,200 percent jump in visits from generative AI sources between July 2024 and February 2025, according to Adobe Analytics. Salesforce predicts AI platforms and AI agents will drive $260 billion in global online sales this holiday season.

Those agents, launched by AI players such as OpenAI and Perplexity, are capable of performing tasks on their own, including navigating to a retailer’s site, adding an item to cart and completing the checkout process on behalf of a shopper. Google’s recently introduced agent will automatically buy a product when it drops to a price the user sets.

This form of shopping is very much in its infancy; the AI shopping agents available still tend to be clumsy. Long term, however, many technologists envision a future where much of the activity online is driven by AI, whether that’s consumers discovering products or agents completing transactions.

To prepare, businesses from retail behemoth Walmart to luxury fashion labels are reconsidering everything from how they design their websites to how they handle payments and advertise online as they try to catch the eye of AI and not just humans.

“It’s in every single conversation I’m having right now,” said Caila Schwartz, director of consumer insights and strategy at Salesforce, which powers the e-commerce of a number of retailers, during a roundtable for press in June. “It is what everyone wants to talk about, and everyone’s trying to figure out and ask [about] and understand and build for.”

From SEO to GEO and AEO

As AI joins humans in shopping online, businesses are pivoting from SEO — search engine optimisation, or ensuring products show up at the top of a Google query — to generative engine optimisation (GEO) or answer engine optimisation (AEO), where catching the attention of an AI responding to a user’s request is the goal.

That’s easier said than done, particularly since it’s not always clear even to the AI companies themselves how their tools rank products, as Perplexity’s chief executive, Aravind Srinivas, admitted to Fortune last year. AI platforms ingest vast amounts of data from across the internet to produce their results.

Though there are indications of what attracts their notice. Products with rich, well-structured content attached tend to have an advantage, as do those that are the frequent subject of conversation and reviews online.

“Brands might want to invest more in developing robust customer-review programmes and using influencer marketing — even at the micro-influencer level — to generate more content and discussion that will then be picked up by the LLMs,” said Sky Canaves, a principal analyst at Emarketer focusing on fashion, beauty and luxury.

Ghergich pointed out that brands should be diligent with their product feeds into programmes such as Google’s Merchant Center, where retailers upload product data to ensure their items appear in Google’s search and shopping results. These types of feeds are full of structured data including product names and descriptions meant to be picked up by machines so they can direct shoppers to the right items. One example from Google reads: Stride & Conquer: Original Google Men’s Blue & Orange Power Shoes (Size 8).

Ghergich said AI will often read this data before other sources such as the HTML on a brand’s website. These feeds can also be vital for making sure the AI is pulling pricing data that’s up to date, or as close as possible.

As more consumers turn to AI and agents, however, it could change the very nature of online marketing, a scenario that would shake even Google’s advertising empire. Tactics that work on humans, like promoted posts with flashy visuals, could be ineffective for catching AI’s notice. It would force a redistribution of how retailers spend their ad budgets.

Emarketer forecasts that spending on traditional search ads in the US will see slower growth in the years ahead, while a larger share of ad budgets will go towards AI search. OpenAI, whose CEO, Sam Altman, has voiced his distaste for ads in the past, has also acknowledged exploring ads on its platform as it looks for new revenue streams.

“The big challenge for brands with advertising is then how to show up in front of consumers when traditional ad formats are being circumvented by AI agents, when consumers are not looking at advertisements because agents are playing a bigger role,” said Canaves.

Bots Are Good Now

Retailers face another set of issues if consumers start turning to agents to handle purchases. On the one hand, agents could be great for reducing the friction that often causes consumers to abandon their carts. Rather than going through the checkout process themselves and stumbling over any annoyances, they just tell the agent to do it and off it goes.

But most websites aren’t designed for bots to make purchases — exactly the opposite, in fact. Bad actors have historically used bots to snatch up products from sneakers to concert tickets before other shoppers can buy them, frequently to flip them for a profit. For many retailers, they’re a nuisance.

“A lot of time and effort has been spent to keep machines out,” said Rubail Birwadker, senior vice president and global head of growth at Visa.

If a site has reason to believe a bot is behind a transaction — say it completes forms too fast — it could block it. The retailer doesn’t make the sale, and the customer is left with a frustrating experience.

Payment players are working to create methods that will allow verified agents to check out on behalf of a consumer without compromising security. In April, Visa launched a programme focused on enabling AI-driven shopping called Intelligent Commerce. It uses a mix of credential verification (similar to setting up Apple Pay) and biometrics to ensure shoppers are able to checkout while preventing opportunities for fraud.

“We are going out and working with these providers to say, ‘Hey, we would like to … make it easy for you to know what’s a good, white-list bot versus a non-whitelist bot,’” Birwadker said.

Of course the bot has to make it to checkout. AI agents can stumble over other common elements in webpages, like login fields. It may be some time before all those issues are resolved and they can seamlessly complete any purchase.

Consumers have to get on board as well. So far, few appear to be rushing to use agents for their shopping, though that could change. In March, Salesforce published the results of a global survey that polled different age groups on their interest in various use cases for AI agents. Interest in using agents to buy products rose with each subsequent generation, with 63 percent of Gen-Z respondents saying they were interested.

Canaves of Emarketer pointed out that younger generations are already using AI regularly for school and work. Shopping with AI may not be their first impulse, but because the behaviour is already ingrained in their daily lives in other ways, it’s spilling over into how they find and buy products.

More consumers are starting their shopping journeys on AI platforms, too, and Schwartz of Salesforce noted that over time this could shape their expectations of the internet more broadly, the way Google and Amazon did.

“It just feels inevitable that we are going to see a much more consistent amount of commerce transactions originate and, ultimately, natively happen on these AI agentic platforms,” said Birwadker.

Mohammad spoke with Modern Retail at the Virginia-based company’s technology office in Plano, Texas, which it opened three to four years ago to recruit for tech workers like software engineers and analysts in the region home to tech companies such as AT&T and Texas Instruments. At that office, CarMax has since hired almost 150 employees — more than initially expected — including some of Mohammad’s former colleagues from Blockbuster, which he had worked for in Texas in the early 2000s.

He explained how other legacy retailers can learn from how CarMax leveraged new technology like artificial intelligence and a startup mindset as it embraced change, becoming an omnichannel retailer where customers can buy cars in person, entirely online or through a combination of both. Many customers find a car online and test-drive and complete their purchase at the store.

“Every company, every industry is going through a lot of disruption because of technology,” Mohammad said. “It’s much better to do self-disruption: changing our own business model, challenging ourselves and going through the pain of change before we are disrupted by somebody else.”

Digitizing the dealership

Mohammad has been with CarMax for more than 12 years and had also been vp of information technology for BJ’s Wholesale Club. Since joining the auto retailer, he and his team have worked to use artificial intelligence to fully digitize the process of car buying, which is especially complex given the mountain of vehicle information and regulations dealers have to consider.

He said the company has been using AI and machine learning for at least 12-13 years to price cars, make sure the right information is online for the cars, and understand where cars need to be in the supply chain and when. That, he said, has powered the company’s website in becoming a virtual showroom that helps customers understand the vehicles, their functions and how they fit their needs. Artificial intelligence has also powered its online instant offer tool for selling cars, giving customers a fair price that doesn’t lose the company money, Mohammad said.

“Technology is enabling different types of experiences, and it’s setting new expectations, and new types of ways to shop and buy. Our industry is no different. We wanted to be that disruptor,” Mohammad said. “We want to make sure we change our business model and we bring those experiences so that we continue to remain the market leader in our industry.”

About three or four years ago, CarMax was an early adopter of ChatGPT, using it to organize data on the different features of car models and make it presentable through its digital channels. Around the same time, the company also used generative AI to comb through and summarize thousands of customer product reviews — it did what would have taken hundreds of content writers more than 10 years to do in a matter of days, he said — and keep them up to date.

As the technology has improved over the last few years, the company has adopted several new AI-powered features. One is Rhodes, a tool associates use to get support and information they need to help customers, which launched about a year ago, Mohammad said. It uses a large language model combining CarMax data with outside information like state or federal rules and regulations to help employees quickly access that data.

Anything that requires a lot of human workload and mental capacity can be automated, he said, from looking at invoices and documents to generating code for developers and engineers, saving them time to do more valuable work. Retailers like Target and Walmart have done the same by using AI chatbots as tools for employees.

“We used to spend a fortune on employee training, and employees only retained and reliably repeated a small percentage of what we trained,” said Jason Goldberg, chief commerce strategy officer for Publicis Groupe. “Increasingly, AI is letting us give way better tools to the salespeople, to train them and to support them when they’re talking to customers.”

In just the last few months, Mohammad said, CarMax has been rolling out an agentic version of a previous buying and selling assistant on its website called Skye that better understands the intent of the user — not only answering the question the customer asks directly, but also walking the customer through the entire car buying process.

“It’ll obviously answer [the customer’s question], but it will also try to understand what you’re trying to do and help you proactively through the entire process. It could be financing; it could be buying; it could be selling; it could be making an appointment; it could be just information about the car and safety,” he said.

The new Skye is more like talking to an actual human being, Mohammad said, where, in addition to answering the question, the agent can make other recommendations in a more natural conversation. For example, if someone is trying to buy a car and asks for a family car that’s safe, it will pull one from its inventory, but it may also ask if they’d like to talk to someone or even how their day is going.

“It’s guiding you through the process beyond what you initially asked. It’s building a rapport with you,” Mohammad said. “It knows you very well, it knows our business really well, and then it’s really helping you get to the right car and the right process.”

Goldberg said that while many functions of retail, from writing copy to scheduling shifts, have also been improved with AI, pushing things done by humans to AI chatbots could lead to distrust or create results that are inappropriate or offensive. “At the moment, most of the AI things are about efficiency and reducing friction,” Goldberg said. “They’re taking something you’re already doing and making it easier, which is generally appealing, but there is also the potential to dehumanize the experience.”

In testing CarMax’s new assistant, other AI agents are actually monitoring it to make sure it’s up to the company’s standards and not saying bad words, Mohammad said, adding it would be impossible for humans to look at everything the new assistant is doing.

The company doesn’t implement AI just to implement AI, Mohammad said, adding that his teams are using generative AI as a tool when needing to solve particular problems instead of being forced to use it.

“Companies don’t need an AI strategy. … They need a strategy that uses AI,” Mohammad said. “Use AI to solve customer problems.”

Working like a tech startup

In embracing change, CarMax has had to change the way it works, Mohammad said. It has created a more startup-like culture, going from cubicles to more open, collaborative office spaces where employees know what everyone else is working on.

About a decade ago, he said, the company started working with a project-based mindset, where it would deliver a new project every six to nine months — each taking about a year in total, with phases for designing and testing.

Now, the company has small, cross-functional product teams of seven to nine people, each with a mission around improving a particular area like finance, digital merchandising, SEO, logistics or supply chain — some even have fun names like “Ace” or “Top Gun.”

Teams have just two weeks to create a prototype of a feature and get it in front of customers. He said that, stacked up over time, those small new changes those teams created completely transformed the business.

“The teams are empowered, and they’re given a mission. I’m not telling them what to do. I’m giving them a goal. They figure out how,” Mohammad said. “Create a culture of experimentation, and don’t wait for things to be perfect. Create a culture where your teams are empowered. It’s OK for them to make mistakes; it’s OK for them to learn from their mistakes.”

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