How Agentic Commerce Is Reshaping the Brand-Customer Relationship

A third of consumers now expect at least one in ten of their purchases to be driven by artificial intelligence within the next year, and the merchants who serve them are already admitting they are not ready. Agentic commerce, the model in which AI agents discover, compare, and buy on a consumer's behalf, has moved from concept to kitchen-table reality faster than almost any technology shift in recent memory. For Fortune 500 brands, this raises an uncomfortable question: if the customer interacting with your website, your chatbot, and your checkout flow is increasingly a machine acting on someone else's behalf, who exactly is the customer relationship with anymore?

Matt Britton has spent his career tracking the moments when consumer behavior outruns corporate strategy, and he argues this is the biggest one since e-commerce itself rewired retail. The rise of personal AI agents like Meta's Muse is not a niche gadget trend. It is a structural change in how purchase decisions get made, and it is forcing brand leaders to confront a future where agentic commerce and the brand customer relationship are no longer separate conversations.

A third of consumers expect at least 10% of purchases to be AI-driven within a year, and nearly three quarters of merchants agree that consumers will adopt agent-led shopping faster than most merchants are prepared for . That gap between consumer appetite and merchant infrastructure is the central tension Britton unpacks in his keynotes on the future of consumer behavior. It is also the reason insights and CRM leaders at the world's largest companies need to rethink what "knowing the customer" even means when the customer is frequently a bot.

This piece breaks down what agentic commerce actually is, why Meta Muse's explosive adoption matters, where merchant readiness is falling short, and what Fortune 500 insights and marketing leaders must do right now to avoid losing the relationship entirely.

What Is Agentic Commerce? Defining the New Brand-Customer Relationship

Agentic commerce describes a transaction model where an autonomous AI agent, not a human shopper, handles some or all of the discovery, comparison, and purchase process. Instead of a person browsing a retailer's app, an agent does the browsing, applies the person's preferences and budget, and executes the purchase with minimal direct human input. The defining feature of agentic commerce brand customer relationship dynamics is that the brand's traditional touchpoints, the website visit, the email open, the loyalty app login, may simply stop happening.

Just as brands are making sense of their own AI agents, consumers are getting agents of their own, and those agents are changing who controls the customer relationship by comparing products, evaluating policies, and making purchases without customers ever visiting a brand's website . That last point is the one keeping Chief Marketing Officers awake at night. First-party data collection, the foundation of modern CRM, assumes a human is clicking, scrolling, and leaving a digital trail. When an agent handles the transaction instead, that trail either disappears or gets captured by a platform the brand does not control. Brands now face a choice: figure out how to compete when consumer agents increasingly mediate the shopping experience or risk losing customers to competitors that do . Britton frames this as the defining strategic decision facing consumer brands heading into 2027 planning cycles. Companies that treat agentic commerce as a technical IT problem will lose to companies that treat it as a customer relationship problem.

Meta Muse and the Explosive Rise of Personal AI Shopping Agents in 2026

No single product has made the agentic commerce shift more visible than Meta's Muse. Meta Muse, a personal AI agent app, topped the Apple App Store's free rankings ahead of OpenAI's ChatGPT shortly after launch , a milestone that signals consumers are shifting from conversational AI tools toward agents that take action on their behalf. The rise of Muse reflects a shift toward agentic apps that help users with a variety of tasks, designed to access a person's apps across categories like email, calendar, payments, health, shopping, and the smart home .

The adoption curve has been remarkable even by big tech standards. Muse reached No. 1 on the U.S. App Store shortly after launch and passed roughly 900,000 downloads in its first week, with estimates later putting weekly users above 3 million and daily active users above 1 million . Within its first three weeks, the app pulled in over five million downloads, pushing it to the top of the US app charts . For context, few consumer apps in history have converted that fast from launch to daily habit. Equally telling is what the app can actually do once installed. Muse can autonomously send emails, book travel on the user's behalf, sell a car, and lower a bill . That last capability, negotiating a bill down on a customer's behalf, is a direct preview of what happens to brand pricing power once agents start comparison-shopping and renegotiating at scale.

Brand reactions have already started, and they are not subtle. Retailers' signals are mixed, and on September 20, 2026, Amazon started showing a pop-up to people using Meta's new Muse agent to shop its store . That defensive move, a major retailer actively warning customers off an AI agent inside its own storefront, is the clearest signal yet that the balance of power in the brand customer relationship is shifting. Matt Britton covers these flashpoints in real time on The Speed of Culture podcast, where he breaks down what rapid consumer platform shifts mean for brand strategy weeks before they hit mainstream business press.

Agentic Commerce Merchant Readiness: Why Trust Remains the Biggest Barrier

Consumer appetite for AI shopping agents is real, but trust has not caught up, and that mismatch is exactly where brand opportunity lives. New research has found that one in four consumers say they will never delegate purchases to AI, and 27% trust no organization to operate an AI shopping agent . That is not rejection of the technology outright. It is a demand for proof before handing over the keys to a purchase decision.

The merchant side of the equation tells a similarly uneven story. Checkout.com's 2026 research on agentic commerce found a widening gap between what consumers expect and what merchant infrastructure can currently support, and that gap is precisely where brands either win or lose share. Companies that invest early in verifiable product data, machine-readable policies, and agent-friendly checkout flows will capture disproportionate share once trust catches up. Those that wait will find their products invisible to the very agents making purchase decisions for their best customers.

Britton's work with the Suzy consumer insights platform gives him a front-row seat to exactly this kind of shift, tracking in real time how fast stated consumer intent is translating into actual behavior change around AI-assisted shopping. The data consistently shows early movers in agent readiness building loyalty before their competitors even register the threat. Here is the opportunity window most Fortune 500 insights teams are missing:

• Verify, don't just advertise. Agents weight verifiable claims over marketing copy, so structured, auditable product data becomes a competitive asset.
• Build for machine-readable policy. Return policies, warranty terms, and loyalty benefits need formats agents can parse and compare instantly.
• Treat agent checkout like a new channel. Brands that built dedicated mobile experiences a decade ago need the same urgency for agent-native commerce now.
• Audit where your product disappears. Run test queries through major consumer agents today to see whether your brand even surfaces as an option.

How AI Agents Are Disintermediating Brands From Their Own Customers

Disintermediation is not a new fear for brand leaders, but agentic commerce makes it structural rather than situational. Historically, a retailer or marketplace stood between a brand and its end customer. Now an AI agent, acting purely in the consumer's interest, can sit between the brand and the retailer, the retailer and the payment rail, and the payment rail and the actual purchase decision. Agents can compare products, evaluate policies, and make purchases without customers ever visiting a brand's website, leaving brands with less visibility into customer preferences and purchasing decisions .

This has direct consequences for CRM strategy, loyalty programs, and first-party data collection, the three pillars most Fortune 500 marketing organizations have built their entire customer strategy around over the last decade. If an agent completes the transaction, the brand may never capture the browsing behavior, the comparison set the customer considered, or even confirmation that the customer saw competitor pricing first. Matt Britton frequently tells Fortune 500 audiences on AI keynote stages that the companies still measuring loyalty purely through app logins and email opens are measuring a shrinking slice of the actual decision journey.

The industries most exposed differ by purchase frequency and complexity. High-consideration categories like real estate and financial services face a different version of this challenge than daily household purchases, since agents handling mortgage comparisons or wealth management research carry higher stakes and require deeper trust signals. Britton's work advising audiences through platforms like AI speaker finance and AI speaker real estate sessions focuses specifically on how high-trust, high-value categories need to adapt their customer relationship models before agentic adoption accelerates further in those verticals.

Rebuilding CRM and Insights Strategy for a Machine-Mediated Customer Relationship

The research and insights function inside most Fortune 500 companies was built to understand human psychology, human motivation, and human decision friction. None of those frameworks automatically translate to understanding how an AI agent weighs a brand's offer against a competitor's. Insights leaders need new research questions entirely: what signals does an agent trust, what data formats does it read, and what proxy behaviors indicate the human behind the agent is satisfied.

Britton's book, Generation AI, makes the case that the generation now entering peak earning years has never known a shopping experience without some AI mediation, and their expectations are shaping agent design itself. Millennials are leading the shift, with 72% comfortable with AI spending on their behalf and 70% already using it to choose gifts . That is not a fringe behavior pattern anymore. It is the baseline expectation of a generation that will represent the bulk of consumer spending within the decade, a theme Britton explores in depth through his Gen Z keynote speaker work. CRM teams need to start building agent personas the same way they built customer personas, mapping which agents their customers are likely to use, what those agents prioritize, and how brand data needs to be structured to win agent-mediated consideration. Loyalty programs need redemption paths that work when the redeemer is software. None of this is theoretical anymore, and the brands waiting for a stable playbook will find agents have already made the decision without them.

Key Takeaways for Business Leaders

• Audit your brand's visibility inside major consumer AI agents today, since invisibility to an agent now functions the same as invisibility to the customer.
• Restructure product and policy data into machine-readable formats agents can verify, compare, and trust at transaction speed.
• Redesign CRM and loyalty programs to recognize and reward agent-mediated purchases, not just human logins and clicks.
• Prioritize trust signals in high-consideration categories like finance and real estate, where agentic adoption carries higher stakes and slower consumer confidence.
• Invest in real-time consumer insight tools that track agent adoption behavior rather than relying on annual survey cycles that lag the market.

Frequently Asked Questions

What is agentic commerce and how does it affect brands?

Agentic commerce is a transaction model where AI agents, not human shoppers, handle product discovery, comparison, and purchasing on a consumer's behalf. It affects brands by reducing direct visibility into customer behavior, since agents can complete purchases without the customer ever visiting a brand's website or app, weakening traditional CRM and first-party data collection.

Why did Meta's Muse become so popular so quickly?

Meta's Muse personal AI agent app topped the Apple App Store's free rankings ahead of OpenAI's ChatGPT shortly after launching in early September 2026 . Its popularity reflects consumer demand for agents that take real-world action, like booking travel or negotiating bills, rather than just answering questions conversationally.

Are consumers ready to trust AI agents with purchases?

Trust is uneven and still forming. Research shows one in four consumers say they will never delegate purchases to AI, and 27% trust no organization to operate an AI shopping agent , even as a third expect AI to drive a meaningful share of their purchases within a year. Brands that build verifiable, transparent data win this trust gap fastest.

How should Fortune 500 companies prepare for agentic commerce?

Companies should audit their visibility inside major consumer AI agents, restructure product and policy data into machine-readable formats, and redesign CRM systems to recognize agent-mediated transactions. Insights teams also need new research frameworks focused on understanding how agents, not just humans, evaluate brand offers and trust signals.

Agentic Commerce Is Already Rewriting the Brand Customer Relationship

The brands that win the next decade will be the ones that treat agentic commerce as the biggest customer relationship shift since e-commerce, not as a passing technology trend. Matt Britton has built his reputation helping Fortune 500 leaders see these inflection points before they become headlines, and this one is moving faster than most. The data on Meta Muse's adoption curve and the merchant readiness gap both point to the same conclusion: the customer relationship is being renegotiated right now, with or without brand input.

Matt Britton works directly with Fortune 500 insights, marketing, and CRM leaders to rebuild strategy for a world where the customer may be a machine. To bring this conversation to your next leadership offsite or industry conference, explore Matt Britton's speaker platform and book a keynote built specifically around agentic commerce and the future of consumer trust.