AI agents already generate 10% of revenue for some brands. Is yours invisible to them?

AI agents are already changing how people buy, and this shift is happening right now, not a few years from now.

Picture ordering a pair of running shoes without opening a single website, without comparing prices across multiple tabs, without typing anything into Google. You simply send a message to your AI agent and it handles everything from start to finish on its own. It knows your size, understands your preferences, researches options, compares terms, and completes the purchase. You don’t have to do anything else.

Sound futuristic? It’s already reality for a growing slice of consumers, and the numbers back it up. McKinsey projects up to $1 trillion in U.S. retail revenue by 2030 driven by so-called agentic commerce. And some brands are already seeing those results now, attributing 10% of their revenue to channels where AI agents handle the entire purchase journey, from the first prompt to the final transaction.

To get a sense of how fast this is moving, Target’s traffic from ChatGPT is growing 40% month over month. Walmart already sees up to 35% of its referral traffic coming from AI agents. These aren’t fringe numbers. They’re clear signals that the e-commerce battlefield is shifting to a new address.

The problem is that while these brands grow, many others simply don’t exist to these agents. They don’t show up, don’t get mentioned, don’t get recommended. And that brings up the question every company should be asking right now 👇

Is your brand visible to who, or what, is doing the buying?

What is agentic commerce and why it changes everything

Agentic commerce is essentially the model where an AI agent acts autonomously on behalf of a user to complete entire purchasing tasks, from initial research to final payment, without the person needing to step in at every stage. It’s not an assistant that suggests options and waits for you to click. It’s a system that interprets your need, evaluates products, compares conditions, picks the best path, and closes the transaction. All of it in seconds, based on preferences, history, and context.

This represents a complete shift in how digital retail works, because the intermediary is no longer a search engine or a marketplace but rather an intelligence that makes decisions. With the execution layer advancing rapidly through agent-compatible browsers and protocols like OpenAI’s UCP and Gemini’s ACP, the result is a seamless end-to-end flow. From discovery to payment, everything happens without the consumer needing to open a single browser tab.

This change has a direct impact on consumer behavior. People who already use AI agents daily report that they simply stop using traditional search engines for routine purchases. The convenience is so significant that habits change fast, and once you experience a purchase handled by an agent, going back to opening ten browser tabs feels like an absurd step backward. This means brands that aren’t on these agents’ radar lose sales not because the consumer consciously chose a competitor, but because the agent never considered the brand as a valid option.

The data backing this movement isn’t made up of optimistic projections from startups looking to raise funding. McKinsey, one of the most conservative and respected consultancies in the world, is talking about a trillion dollars by 2030 in U.S. retail alone. That puts agentic commerce in the same category of transformations that e-commerce represented in the 2000s or that smartphones represented for digital behavior in the following decade. Those who got in early captured massive market share. Those who waited spent years trying to make up lost ground.

The death of the traditional front door

For decades, the buying journey had a well-defined front door. Visibility on platforms, paid media investment, search engine rankings. Everything depended on the consumer arriving somewhere before they could buy anything. Whoever controlled that destination controlled commerce.

That era is ending.

When someone asks ChatGPT, Gemini, Claude, or Perplexity for a running shoe recommendation, they’re delegating the entire discovery process to an agent powered by large language models. It’s the agent that decides which products appear and which will never be seen. There’s no sponsored listing in this context. No search position. No mandatory destination.

Brands visible to AI agents can win in AI-driven search without being the top result on Google. It’s a game with completely different rules, and those still playing only by the old rules risk being left out without even realizing it.

Agent experience and the new concept of digital visibility

There’s a concept gaining serious traction in discussions about the future of digital marketing: agent experience, or AX for short. It describes how an AI agent perceives, interprets, and interacts with a brand or product while carrying out a task. Unlike user experience, which considers how a person navigates a site, AX considers how an automated system reads, classifies, and makes decisions based on the information a brand makes available.

A site with great UX can have terrible AX if the information is structured in a way that an agent can’t process clearly and efficiently. CMOs ahead of the curve already recognize this fundamental shift: AI agents are no longer just tools that consumers use. They are the customers.

Just as user experience defined the era of B2C digital commerce, agent experience is defining the emerging era of B2A, or Business to Agent. If you’re a brand, this means your actual audience now includes the automated crawlers you might still be actively blocking on your site. Except these agents don’t browse like humans.

And the numbers reinforce this point hard. One study found that only 12% of URLs cited by AI tools overlap with the top 10 Google results. Another study showed that 90% of the sources ChatGPT cites aren’t even in the top 20 pages on Google. Traditional SEO alone just isn’t enough anymore.

This new quality benchmark changes what it means to have good digital visibility. For years, visibility meant ranking well on Google, having solid organic positioning, maintaining a consistent social media presence with engagement. Those things still matter, but they no longer guarantee that an AI agent will recommend or even consider your brand. Agents look for structured data, objective descriptions, clear information about pricing, availability, return policies, and verifiable reviews. If a brand doesn’t deliver this in a way an agent can quickly interpret, it simply doesn’t enter the equation, regardless of how many Instagram followers it has.

Agentic web optimization emerges in precisely this context as the practical answer to this new reality. Just as SEO emerged as the discipline of making sites readable and relevant to search engines, agentic optimization is the process of making a brand readable, trustworthy, and recommendable to AI agents.

What agentic visibility looks like in practice

Brands can lose positions overnight, not because the product changed, but because the content wasn’t structured in a way that agents could interpret reliably. On the flip side, brands that were invisible in the AI-first world managed to reach the top spot by embracing agent experience and answer engine optimization, known as AEO.

A real-world case illustrates this well: a robotics company achieved a 94% increase in agentic visibility in four months by restructuring its content for AEO. The original content was engaging for human readers, but analysis revealed it lacked elements that LLMs need to extract and cite information reliably:

• A clear FAQ section
• Real, detailed use cases
• Precise answers to the exact questions users were asking AI tools

By deepening content relevance and restructuring it for machine comprehension, while competitors remained vague, promotional, and poorly formatted, this brand became the go-to reference in its category. LLMs started citing it. Agents started recommending it.

The playbook for brands that want to compete in this landscape

Brands already seeing concrete results with AI agents didn’t get there by accident. They made strategic decisions about how they structure their information, how they position themselves on platforms that feed language models, and how they ensure their commercial policies are clear enough for an agent to make a purchase decision with confidence.

The practical playbook these brands follow involves four main fronts:

• Audit how agents see your brand. Tools already exist that simulate how LLMs crawl and interpret a site. Most brands are surprised by the gaps they uncover in this process.
• Structure content to be visible to agents, not just for SEO. This means creating clear FAQs, specific use cases, and precise answers to real user queries, instead of pages stuffed with keywords but lacking depth.
• Manage external citations. AI models give significant weight to sources like Reddit and Wikipedia. Understanding how your brand is referenced in these spaces and actively working to shape that narrative makes a real difference in recommendations.
• Build machine-readable product data. Well-documented APIs, structured schemas, and clean, up-to-date product feeds are the new digital storefront.

Beyond the technical structure, these brands also understand that digital reputation now carries a new meaning. An AI agent, before recommending a product, consults varied sources to validate whether that brand is trustworthy. Reviews on recognized platforms, mentions in tech and retail publications, consistency of information across different digital touchpoints — all of this factors into the calculation an agent makes before telling the user: I found the best option for you. Brands with fragmented reputations or inconsistent information lose in this evaluation, even if they have a product superior to the well-structured competitor that shows up.

What the major players are already doing

This isn’t a theoretical exercise. Major retailers like Target, Walmart, and Etsy are already investing in APIs, schemas, and content strategies tailored to the way AI agents consume and act on information. The practical result is that referral traffic from ChatGPT already accounts for up to 35% for these companies.

Consumer behavior is already shifting to find these brands. An Adobe study revealed that while nearly half of American consumers use TikTok as a search engine, 14% already prefer ChatGPT over Google. The leap from searching and clicking to asking an agent and approving the suggestion isn’t a big one, and it’s happening faster than most brands realize.

Over the next 12 months, the expectation is for significant advances in the B2A model, where companies need to think about marketing, sales, and communication not only for human buyers but for the AI agents acting on their behalf. More consumers delegating purchases to agents, fewer people manually browsing websites, and the emergence of the first agent-to-agent networks, where agents learn from each other’s successful transactions to make increasingly better recommendations.

What changes in practice for those not yet part of this movement

For those sitting on the sidelines, the real risk isn’t losing a sale here or there. It’s losing relevance structurally in a channel that will grow at an accelerated pace over the coming years. When an AI agent learns that certain brands are trustworthy and deliver good experiences in the purchases it handles, it tends to recommend those brands repeatedly, creating a reinforcement cycle that favors those who are well-positioned and makes it increasingly harder for new players to break into that recommendation space.

This doesn’t mean the answer is to panic and throw away everything built through traditional SEO and social media presence. Those strategies still hold value and still drive results. What changes is that they need to be complemented with a new layer of agentic web optimization that ensures the brand’s entire digital infrastructure is readable and relevant not just to humans but also to the autonomous systems increasingly making choices on their behalf.

Thinking about digital visibility today without considering how agents see your brand is like thinking about SEO in the 2010s without considering mobile. It’s not optional. It’s a matter of competitive survival.

Another thing that sets winning brands apart is the speed at which they adapted their strategies once they spotted the movement. Instead of waiting for the market to consolidate before reacting, they treated agentic commerce as a real window of opportunity and allocated resources to understand how agents work, which platforms they use as data sources, and which criteria weigh most heavily in recommendations. This anticipation creates a competitive advantage that compounds over time, because while they’re already optimizing and learning, their competitors are still deciding whether the topic deserves attention.

The landscape taking shape is one of an internet where a significant portion of purchasing decisions passes through layers of artificial intelligence before reaching the final click, or before no longer needing the click at all. The next decade of digital commerce won’t be won by brands with the best websites or the best Google rankings. It will be won by brands that machines understand, trust, and recommend.

The 10% of revenue some brands already attribute to this channel is just the beginning of a curve that, if McKinsey is right, will redefine digital retail over the next five years. 🚀