Marketing to Machines: How to Optimize Your Brand for AI Agents That Buy on Behalf of Humans
Your next customer might never visit your website, read your ad, or see your logo. Here's how to win their business anyway.
Something happened on September 29, 2025, that most business owners completely missed — and it may end up being as consequential to marketing as the launch of Google Ads was in 2000.
OpenAI turned ChatGPT into a shopping platform.
Not a product recommendation chatbot. Not a fancy comparison tool. An actual commerce platform where more than 700 million weekly users can now discover a product, review it, and buy it — without ever leaving the chat window. One conversation. One tap. Checkout complete.
They called the underlying technology the Agentic Commerce Protocol, built in partnership with Stripe. It launched with Etsy sellers, with over a million Shopify merchants — Glossier, SKIMS, Spanx, Vuori — coming online shortly after. PayPal joined the party in October, bringing tens of millions of additional merchants into the fold.
Here's the part that should keep every business owner up at night: when ChatGPT recommends products, the results are ranked by relevance to the user's query. Not by who paid the most for an ad. Not by who has the biggest brand. By relevance — as determined by an AI system evaluating structured product data, availability, price, quality, and whether the merchant is a primary seller.
Your marketing department wasn't built for this. Your website wasn't built for this. Your entire go-to-market strategy wasn't built for this.
But it's here. And it's just the beginning.
The Rise of the Machine Customer
Let's be precise about what's actually happening, because the terminology can obscure the magnitude of the shift.
An AI shopping agent is not a chatbot that answers FAQ questions on your website. It's an autonomous or semi-autonomous system that acts on behalf of a human consumer to research products, compare options, evaluate trade-offs, and — increasingly — complete purchases. The human describes what they want. The agent does everything else.
This isn't theoretical. It's already in production across multiple platforms simultaneously.
Amazon's "Buy for Me" lets consumers shop other brands' and retailers' websites without leaving the Amazon app. The AI agent navigates external sites, adds items to cart, and completes checkout — using the customer's Amazon account. Google's Gemini can add items to a user's cart and complete checkout with Google Pay when shoppers hit a "buy for me" button. It can also set up price tracking and make purchase decisions autonomously. Perplexity rolled out AI-powered shopping with native purchasing capabilities, prompting Amazon to push back over how the system accessed its product data. And a wave of specialized startups — Daydream for fashion, Phia for price comparison, OneOff for creator-inspired shopping — are targeting specific niches where AI agents can outperform the traditional browse-and-buy experience.
The consumer adoption numbers are already significant. An IBM and National Retail Federation study published in January 2026, surveying over 18,000 consumers across 23 countries, found that 45% of consumers now turn to AI for help during their buying journeys. They're using AI to research products (41%), interpret reviews (33%), and hunt for deals (31%). A quarter of Americans between 18 and 39 already use AI-assisted tools for shopping. About a third of U.S. consumers say they would let an AI agent make purchases on their behalf.
McKinsey projects that AI agents could mediate $3 trillion to $5 trillion of global consumer commerce by 2030. Not influence. Mediate — meaning the agent is an active participant in the transaction, not just a passive recommendation engine.
This isn't a five-year-away prediction. The infrastructure is being built right now, in the open, by the largest technology companies on earth.
Why This Changes Everything About Marketing
To understand why agentic commerce is so disruptive, you need to understand what it eliminates from the traditional marketing funnel.
In the old model, marketing existed to do three things: create awareness (so a potential customer knows you exist), build consideration (so they evaluate you against alternatives), and drive conversion (so they choose you and complete a purchase). Every marketing dollar you've ever spent — on advertising, content, SEO, social media, email — was designed to influence a human moving through these stages.
AI agents collapse this entire funnel into a single interaction.
When a consumer tells ChatGPT "find me the best wireless headphones under $200 with great noise cancellation for commuting," the AI doesn't see your billboard. It doesn't remember your Instagram ad. It doesn't care about your brand storytelling video. It evaluates structured product data — specifications, pricing, availability, reviews, and return policies — and makes a recommendation based on relevance to the query.
Your beautiful website? The agent might never visit it. Your carefully crafted brand voice? The agent can't hear it. Your emotional advertising campaign? The agent has no emotions.
This doesn't mean brand becomes irrelevant — we'll get to that. But it means the mechanisms through which brand translates into purchase are fundamentally different when the buyer is a machine.
McKinsey's research on what they call the "agentic commerce automation curve" describes four levels of AI agent involvement in purchasing:
Level 1: Agents help shoppers think and make decisions, but don't execute. ("Compare three noise-canceling headphones and explain trade-offs.")
Level 2: Agents execute specific purchases with human approval. ("Buy the Sony WH-1000XM5 from the best-priced authorized retailer.")
Level 3: Agents handle entire categories autonomously within parameters. ("Keep my coffee supply stocked, spending no more than $40/month, preferring fair trade.")
Level 4: Agents operate against standing goals continuously. ("Keep household essentials under $300/month" or "Maintain my airline loyalty status at lowest total cost over 2026.")
Most consumer interaction today sits at Levels 1 and 2. But the infrastructure for Levels 3 and 4 — the levels that truly disintermediate traditional marketing — is being built right now through protocols like OpenAI's ACP and open-source frameworks from the newly established Linux Foundation Agentic AI Foundation, which counts Anthropic, Google, Microsoft, OpenAI, and others among its founding members.
The question for every business owner isn't whether this shift will affect you. It's whether you'll be ready when it does.
The New Optimization Discipline: Making Your Business Machine-Readable
If the past 25 years of digital marketing were about being found by humans searching on Google, the next decade will be about being selected by AI agents acting on behalf of humans. This requires a fundamentally different approach to how you present your business to the digital world.
The emerging discipline has several names — Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), and what some are starting to call Agent Experience Optimization (AXO). The terminology will settle eventually. The underlying principle won't change: you need to make your business, your products, and your value proposition machine-readable in ways that go far beyond what traditional SEO ever required.
Here's what that actually means in practice.
1. Structured Data Becomes Your Most Important Marketing Asset
When a human visits your website, they can look at a product photo, read a paragraph of marketing copy, and intuit what you're selling, who it's for, and whether it's worth the price. An AI agent can't intuit anything. It can only process structured, machine-readable data.
This means Schema.org markup — the structured data vocabulary that helps search engines and AI systems understand your content — moves from a nice-to-have SEO tactic to a foundational business requirement. Product schema (specifications, pricing, availability), Organization schema (who you are, what you do, where you operate), FAQ schema (questions your customers ask and precise answers), Review schema (aggregated social proof) — all of this becomes the language through which AI agents evaluate your business.
OpenAI's Agentic Commerce Protocol makes this explicit. To participate in ChatGPT's Instant Checkout, merchants must provide structured product feeds containing identifiers, descriptions, pricing, inventory, media, and fulfillment options. The products that show up in ChatGPT shopping recommendations aren't the ones with the best marketing copy. They're the ones with the most complete, accurate, and well-structured data.
For service businesses — consultancies, agencies, contractors, professional services — the principle is the same even though the mechanism differs. AI systems evaluating service providers parse structured information about capabilities, credentials, service areas, pricing models, and client outcomes. If this information isn't structured and machine-readable on your website, you're invisible to the systems that are increasingly shaping how prospects discover and evaluate providers.
2. Answer-First Content Replaces Engagement-First Content
For twenty years, content marketing strategy was built around a simple idea: create content that attracts visitors, keeps them on your site, and moves them toward a conversion. The metrics were pageviews, time on page, bounce rate, and eventually, leads and sales.
In the AI agent era, the most valuable content is content that AI systems can extract, synthesize, and cite — often without the user ever visiting your website.
The numbers are stark. Over 65% of Google searches now end without a click. Google's AI Overviews — AI-generated answer summaries that appear above traditional search results — grew from appearing in roughly 6% of queries in January 2025 to over 30% by late 2025. Gartner projects 25% of organic search traffic will shift to AI chatbots by 2026. The average click-through rate for a site ranking #1 for a keyword dropped 64% in a single year after AI Overviews launched.
This doesn't mean content is dead. It means the purpose of content has shifted. The goal is no longer to get someone to your website. The goal is to be the source that AI systems cite when answering questions relevant to your business.
What does this look like practically? It means structuring your content around specific questions your customers ask, and answering those questions clearly and authoritatively in the first paragraph — not burying the answer below three paragraphs of preamble. It means organizing content with clear headings, logical hierarchy, and self-contained sections that AI can extract independently. It means publishing with author credentials, publication dates, and source citations that signal authority to AI systems evaluating trustworthiness.
This is a technical content architecture challenge, not a creative writing challenge. The businesses that get this right will be the sources AI agents trust and cite. The ones that don't will produce content that no machine ever reads and no AI ever references.
3. Your Reputation Data Layer Matters More Than Your Ad Budget
When an AI agent evaluates your business on behalf of a consumer, it doesn't just look at your website. It synthesizes information from across the web — reviews, ratings, mentions in authoritative publications, social proof signals, business directory listings, and third-party data sources.
This means your reputation data layer — the totality of structured and unstructured information about your business that exists across the internet — becomes a critical competitive asset. And for most businesses, this layer is fragmented, inconsistent, and riddled with outdated information.
Ensuring your Google Business Profile is complete and current. Making sure your business information is consistent across directories and platforms. Actively managing your review presence on sites that AI systems reference. Getting mentioned and cited in authoritative industry publications. Building a body of third-party evidence that AI systems can use to validate your claims about your own business.
None of this is glamorous. None of it makes for exciting marketing campaigns. But when an AI agent is deciding which three businesses to recommend to a consumer asking "best web development agency in Houston for enterprise applications," the agent isn't evaluating your ad creative. It's evaluating your reputation data layer.
4. Technical Infrastructure Becomes a Marketing Function
Here's where this comes full circle to the thesis of our previous piece: marketing has become a technical discipline. Nowhere is this more true than in preparing for agentic commerce.
OpenAI's Agentic Commerce Protocol requires merchants to implement REST API endpoints, webhook integrations, and structured product feeds that meet specific technical specifications. Amazon's Buy for Me feature requires your website to be navigable by AI agents — which means clean HTML, accessible product information, and functional checkout flows that an autonomous system can complete.
For service businesses, the technical requirements are different but equally demanding. Your website needs to be crawlable by AI systems with structured data they can parse. Your content needs to be organized in formats that AI can extract and synthesize. Your data systems need to produce clean, consistent, machine-readable information about your business across every digital touchpoint.
This isn't work your marketing intern can do. It isn't work your social media manager can do. It's systems integration and data architecture work — the kind of work that requires the intersection of marketing strategy and technical execution.
The Brand Paradox: Why Human Connection Matters More, Not Less
At this point, you might be thinking: "If AI agents don't care about brand, does brand even matter anymore?"
Not only does brand still matter — in some ways, it matters more than ever. But the role of brand shifts in an agent-mediated world.
Here's the paradox. For routine, commoditized purchases — household supplies, basic electronics, commodity services — AI agents will increasingly make decisions based purely on structured data: price, specifications, availability, reviews. Brand loyalty erodes when the buyer is a machine optimizing for objective criteria. As one retail analysis noted, AI agents will make "brand-independent purchase decisions based on materials, durability, and sizing rather than traditional brand loyalty."
But for complex, high-consideration purchases — where the human still wants to be involved in the decision — brand becomes the signal that cuts through the AI-generated noise. When the agent presents three options and the human makes the final call, brand recognition, trust, and emotional resonance become the deciding factors.
This means brand strategy bifurcates. For your commoditized offerings, you compete on data quality, pricing, and operational excellence — because that's what the machines optimize for. For your differentiated offerings, you invest more in brand, not less — because brand is the tiebreaker when AI presents options to the human decision-maker.
And there's a second dimension. Bain & Company's research found that 50% of consumers remain cautious about fully autonomous purchasing. Trust is the barrier. The brands that consumers trust enough to let AI buy on their behalf will have an enormous structural advantage. Building that trust requires the deeply human work of authentic communication, consistent quality, transparent practices, and genuine customer relationships — none of which AI can automate.
What Business Owners Should Do Now: A Practical Roadmap
The agentic commerce wave is building, but it hasn't crested yet. That means there's still time to prepare — but the window is narrowing. Here's a practical, prioritized action plan.
Immediate (Next 30 Days)
Audit your structured data. Use Google's Rich Results Test and Schema Markup Validator to check whether your website has proper Schema.org markup. At minimum, you need Organization, Product or Service, FAQ, and Review schema implemented correctly. If you're an e-commerce business, your product data needs to be complete, accurate, and structured to the standard that AI systems can parse.
Test your AI visibility. Ask ChatGPT, Perplexity, and Google's AI Overview about your business, your product category, and the questions your customers typically ask. Are you showing up? Are you being cited? If not, you have a problem — and now you know it.
Clean up your reputation data layer. Ensure your business information is consistent across Google Business Profile, industry directories, and review platforms. Inconsistent or outdated information across these sources confuses AI systems and reduces your likelihood of being recommended.
Near-Term (Next 90 Days)
Restructure your content for AI extraction. Audit your highest-value content and restructure it with answer-first formatting, clear question-based headings, FAQ sections, and author credential signals. Focus on the 20% of content that drives 80% of your business relevance.
Evaluate agentic commerce readiness. If you sell products, investigate the Agentic Commerce Protocol and determine what it would take to make your products purchasable through ChatGPT and similar platforms. If you're on Shopify, you may already be eligible. If you have a custom e-commerce setup, start scoping the technical integration work.
Build your citation strategy. Identify the authoritative publications, directories, and platforms in your industry that AI systems reference. Develop a systematic approach to earning mentions and citations in these sources — through thought leadership, PR, partnerships, or contributed content.
Medium-Term (Next 6-12 Months)
Invest in technical marketing infrastructure. Build or acquire the capability to manage structured data, API integrations, and machine-readable content at scale. This might mean hiring differently, upskilling existing team members, or partnering with a firm that brings both marketing strategy and technical execution.
Develop an AI-first content engine. Shift content production from volume-driven (more blog posts, more social posts) to authority-driven (fewer, deeper pieces that AI systems trust and cite). Invest in content architecture — the technical structure that makes content machine-extractable — not just content creation.
Monitor and adapt. The agentic commerce landscape is evolving monthly. What works in February 2026 may be table stakes by December. Build the organizational capacity to monitor new developments, test new channels, and adapt your approach as the infrastructure matures.
The Bigger Picture
The shift to agentic commerce isn't a marketing trend. It's a structural transformation of how businesses and customers find each other, evaluate each other, and transact with each other.
For the past 25 years, the internet created a world where every business needed a website, and marketing's job was to drive humans to that website. In the emerging world, your website is just one of many data sources that AI systems consult when making decisions on behalf of humans. Marketing's job is expanding from "drive traffic to our site" to "ensure AI systems understand, trust, and recommend our business wherever decisions are being made."
This requires capabilities most businesses don't currently have: structured data management, technical content architecture, API integration, and the strategic vision to understand how human and machine decision-making intersect.
It's a hard problem. But hard problems create the biggest opportunities for businesses that solve them first. The companies that optimize for the machine customer now — while competitors are still debating whether this shift is real — will build advantages that compound over time and become increasingly difficult to overcome.
The AI agents are already shopping. The only question is whether they're shopping for your products — or your competitor's.
Catalyst Studio builds the technical infrastructure that connects businesses to the emerging world of AI-powered commerce — from structured data architecture and system integration to the API-level work that makes your business machine-readable. If your marketing strategy still assumes a human is doing the buying, it's time to update the playbook. Let's talk.