AI shopping agents are fundamentally transforming consumer purchasing behavior
Commerce is no longer moving at a human pace. AI shopping agents, autonomous systems designed to act on behalf of consumers, are restructuring how buying decisions happen. These are already embedded across the retail experience, from price comparisons to fully autonomous checkouts.
Amazon has deployed features like “Buy for Me,” while systems like ChatGPT, Gemini, and Perplexity are actively scanning product listings, comparing prices, and synthesizing market data. Right now, most consumers still retain final control at the moment of purchase. That’s changing. Some users already allow these agents to select and even complete transactions automatically. These systems don’t sleep, they don’t get distracted, and they optimize faster than any typical digital shopper.
For anyone leading a business, the message is simple: your customer may no longer be a person clicking through your site. Your customer might be code. And that code is doing all the research, comparing products, prioritizing shipping, and choosing vendors based on what it scrapes from the open internet, not what you shout from your homepage. That creates a huge asymmetry in how relevance and visibility are achieved.
If these agents become the dominant discovery and transaction layer, you risk becoming invisible unless your data is structured, accessible, and accurate. Owning the relationship with the buyer now means preparing to engage primarily with the systems acting on their behalf. This isn’t a problem for later. It’s already here.
Retailers must act decisively and strategically to engage with AI agents in order to stay relevant
Speed matters. In digital, whoever adapts first shapes the system. That’s what happened with food delivery and travel booking. DoorDash and Expedia quietly ate the market by aggregating restaurant menus and travel seats online. They scaled before most businesses realized what was happening. By the time everyone else reacted, they were negotiating from a weaker position. Those who waited were forced to accept unfavorable margins, weaker brand visibility, or both.
The same moment is playing out now with AI shopping systems. These agents are controlling consumer demand upstream, before the customer even sees your brand.
Marriott gives us a clear benchmark on how to do this right. In 2019, it restructured its relationship with Expedia to include last-minute inventory and retain fine control over customer-service touchpoints. Instead of becoming just another listing, Marriott protected its customer experience through integrated loyalty systems like Bonvoy. That move paid off. Between 2022 and 2024, Marriott grew revenues by 10% annually, up from just 1% between 2017 and 2019.
If you’re running a retail business, you need to decide now whether to partner, isolate, or lead on agent-based integrations. Delaying puts you on the back foot. These platforms are gaining influence at the top of the funnel. If they control access to consumers and shape comparisons, your margins will shrink. Your brand will be filtered out. And your future decisions will be made under pressure.
Engagement must be strategic: it isn’t just about visibility. It’s about shaping the rules of interaction early. If your competitors define the standards through early alliances while you wait, relevance becomes expensive, if not impossible, to recover.
Lessons from aggregator partnerships emphasize the benefits of early adaptive integration
Retail doesn’t need to speculate about what happens when platforms begin controlling customer access. We’ve already seen it. A decade ago, businesses that partnered early with aggregators gained reach without losing influence. Those that waited had little room to negotiate.
The Marriott–Expedia partnership is one of the clearest examples. Marriott didn’t hand over the customer relationship. Instead, it expanded the types of inventory it shared and maintained a firm grip on the key experience layers. Loyalty program integration through Bonvoy kept customers within the Marriott ecosystem, even when discovery happened through Expedia. The results were concrete. From 2022 to 2024, Marriott grew revenues at a compound annual rate of 10%, compared to just 1% growth from 2017 to 2019.
Executives need to internalize this: collaboration with emerging platforms doesn’t automatically mean loss of control. But only when you set the terms. Businesses that shape partnerships early define the boundaries. They retain data ownership, they influence traffic direction, and they stay visible under their own terms.
Timing is strategic. The first moves won’t just shape partner economics. They’ll define how the next phase of commerce operates.
Retailers must choose among four distinct AI engagement strategies, each presenting unique risks and opportunities
There’s no one-size-fits-all approach to AI agent engagement. Retailers are already deploying varied strategies across four clear categories. Each has trade-offs. Those choices define visibility, control, and long-term margins.
Some companies are going fully closed. They block AI agents from crawling their sites. Amazon is doing this while simultaneously building its own proprietary AI buying systems that operate on other retailers’ websites. This gives it end-to-end control over both discovery and transaction, internally. But it comes at the cost of visibility on the open web.
Others are passive open. These firms allow crawlers but don’t optimize their listings for agent readability. Pottery Barn is an example. Some of its products appear in ChatGPT and Perplexity searches, complete with descriptions and reviews. But premium services like design consultations, registries, and warranty coverage can only be found on their own channels. This approach balances exposure with gated experiences.
A more integrated model is partial or full partnership. Etsy and Shopify merchants are already on this path. Their product data is structured for easy scraping, and checkout is handled natively within ChatGPT through OpenAI’s Instant Checkout. That’s tight integration with full transactional flow, but it introduces dependency on agent ranking algorithms and checkout UX they don’t control.
The most aggressive strategy is building a dedicated, headless .bot interface. No major company has launched this yet, but several are considering it. This setup would deliver clean, structured data exclusively for AI consumption, maximizing speed and precision for agents. But it’s also resource-intensive and comes with a long-term risk: if AI agents dominate the discovery process, they may start demanding pricing concessions or gatekeeping demand flow unless access is paid for.
Each strategy has a specific ceiling and cost. What matters is aligning the model with business positioning, technical readiness, and competitive dynamics. Delaying that alignment just increases the probability you’ll be choosing from weakened options later.
Agent-Driven commerce disrupts traditional retail economics by increasing reach while compressing profit margins
AI agents can scale reach in ways that traditional digital marketing cannot. They expose products across platforms, compare in real-time, and push competitive options directly to the consumer or the consumer’s AI assistant. This gives retailers access to broader audiences with lower acquisition costs. But there’s a price for that exposure, your margins.
Intermediary control brings transparency, and transparency squeezes pricing. AI agents highlight lowest-cost options first unless there’s a clear value proposition placed in front of them. That leaves little room for pricing power unless your brand is unambiguous about what makes it different.
More visibility doesn’t guarantee more profit. As these agents control upstream decision-making, they become the new gatekeepers. They dictate flow, highlight product features, and aggregate user reviews from disparate platforms. As a result, unless your offer is distinctive or your channel pricing is protected, your products end up competing against identical items sold elsewhere, under the harsh light of aggregated feedback and optimized price points.
Some vendors will see a short-term boost in sell-through rate by playing along early. That traffic, however, may come at the cost of long-term erosion in control. Decision-makers must view AI agent partnerships as both a customer acquisition tool and a negotiating relationship with fast-growing infrastructure owners. Strategic foresight is critical. The entity controlling discovery will eventually demand a share of value.
Vendors must enhance their ecosystem and customer value proposition beyond simple transactional convenience
Transactions are easy to automate. Loyalty is not. If AI agents are deciding what gets presented, and where the buyer completes the journey, then the burden is on vendors to offer more than just a product. You have to give the system, and more importantly the user, a reason to care where the buying happens.
That means controlling the checkout layer whenever possible, owning payment, delivery options, and post-purchase services. If your site becomes just one of many access points managed by an agent, and you add nothing unique, you surrender negotiating power and downstream monetization.
Keep some inventory exclusive to your own ecosystem. Premium bundles, curated experiences, and loyalty accelerators, like points multipliers, only make an impact if they can’t be replicated by agents redirecting traffic to third-party sellers. Consumers still notice scarcity and differentiated services, but only if they’re tied to a specific environment.
Control over insights is equally critical. Data is the compounding asset that underpins future competitive moves. Watermarking content, using access tiers for APIs, and preserving visibility into how your data is used by third parties ensures the feedback loop stays with you, not with the agent.
Finally, it’s not enough to simply allow listings to be scraped. Optimization matters. Listings should be structured so that generative AI agents prefer them, because they’re faster to ingest, easier to parse, and richer in useful content. This requires building systems that serve the AI layer directly, not just human users.
If you want to stay relevant, you can’t just be listed. You have to be first, best, or irreplaceable, on the agent’s terms and the customer’s.
The future of AI agent-to-agent commerce
The structure of agent-based commerce is still fluid. There are two primary outcomes on the horizon: the space consolidates under a small group of dominant, general-purpose AI agents, or the system fragments into specialized agents built for specific industries, categories, or consumer behaviors. Each outcome will drive very different requirements for how retailers organize partnerships, platforms, and customer experiences.
Mega-agents, such as ChatGPT, Gemini, or Perplexity, already have the infrastructure, user base, and cross-domain capacity to dominate discovery and transactions at scale. If this consolidation happens, they will channel enormous volumes of traffic and influence into concentrated layers of control. That changes how retail brands negotiate for visibility, data access, and performance metrics.
On the other hand, niche agents will emerge, particularly in high-consideration verticals like health, finance, or luxury, where category expertise, regulatory compliance, and specialized recommendations offer more value than pure breadth. These agents won’t compete on universality, but on trust, precision, and contextual relevance.
Some companies will attempt to launch their own proprietary or white-label agents to retain direct customer routes. This is a rational hedge against being fully disintermediated. It’s not a guaranteed win, but it offers an alternative engagement model at a time when controlling the terms of interaction matters more than ever.
Executives should not assume a single outcome. Preparation needs to accommodate both fragmentation and centralization. That means building modular systems, ensuring data can be syndicated across agent types, and negotiating early partnership models that preserve adaptability. The objective is to maintain resilience and relevance, no matter how the market architecture finalizes.
Immediate strategic action is necessary
For many businesses, the next wave of disruption won’t come from a competitor, it will come from being excluded entirely from the decision-making path. AI agents are becoming the default entry point for consumers. In some cases, users may never visit a brand’s website, app, or advertisement. They will issue a prompt, and the agent will handle discovery, comparison, selection, and purchase, start to finish.
This reorders the pipeline. Visibility is no longer controlled by ad buying or SEO investment. It’s driven by how well your product data integrates with, and ranks within, the agent’s ecosystem. If your strategy still depends on capturing human attention manually, through retargeting, email, content arbitrage, you’re not preparing for where decisions are really being made.
OpenAI’s Instant Checkout is already live for Etsy vendors and over one million Shopify merchants. It lets consumers buy directly inside ChatGPT without ever interacting with the seller’s native platform. This is not theoretical. This is the beginning of normalized, agent-centric transaction layers.
To respond, vendors need to prioritize agent-aware structuring immediately. Structured product feeds, semantic metadata, fulfillment integrations, and proactive relationships with AI platforms are all essential. This is the foundation for ensuring your brand remains part of the purchasing experience.
Waiting is not a neutral position. Delay decreases your chances of being selected, by both the agent and the user it serves. And once patterns of agent-based behavior are trained into users, reversing that becomes exponentially harder.
You can’t rely on the consumer visiting your ecosystem by default. The system will decide what they see. You need to ensure you’re embedded in that system, on favorable terms, before that window closes.
Final thoughts
You’re not competing for clicks anymore. You’re competing to stay in the loop when the buyer is an AI. That changes everything. The old playbook, optimize a funnel, own a website, build brand loyalty through repeat visits, doesn’t hold in a system where agents collapse the journey into a few API calls.
This shift doesn’t favor the biggest players. It favors the fastest adaptors. The ones who structure their data cleanly. The ones who form the right partnerships early. The ones who find leverage before the platforms set the terms.
There’s no long runway. The technology is moving. Consumers are handing over routines to machines. And those machines aren’t asking you for your homepage, your story, or even your brand. They’re asking for clean inventory, clear pricing, frictionless checkout, and confidence the customer will be satisfied.
If you don’t build for the AI layer now, someone else will. And once the systems are trained and standardized, catching up gets expensive.
Control what you can. Be visible where it matters. And treat these agents like the gatekeepers they’re becoming, because they already are.
