The Brands AI Agents Will Recommend Are Already Pulling Ahead

For two decades, marketing invested in being findable. But as shopping moves inside AI conversations, the question facing senior marketers has changed. The new imperative is whether an algorithm will recommend your brand on a consumer’s behalf.

That sounds subtle, but it rewires how brands need to think about visibility and trust, all the way down to how internal teams are organized.

The Upstream Shift

Most marketing teams still orient measurement around traffic and on-site conversion. That lens is becoming incomplete. A growing share of the purchase journey now happens inside AI tools where brands have no analytics visibility.

Charlie Clark, founder of SEO agency Minty Digital, has seen this across his entire client base. AI search currently drives less than 4% of traffic for most brands he works with, and Google still dominates referrals by a wide margin. But the traffic numbers themselves are a distraction. The real shift is upstream. AI tools are absorbing the research and comparison phases that used to generate those clicks. Consumers still convert, but they arrive at a brand’s site with their decision largely already made, shaped by a conversation the brand never saw.

Clark argues this creates an entirely new measurement challenge. Brands need to track off-site signals like share of search and citation frequency, paying particular attention to whether they’re surfacing in AI-generated answers at all. Keyword manipulation and backlink volume actively hurt in this environment, because AI models prioritize authority and genuine expertise when assembling recommendations.

Trust Becomes the Product

This is where things get commercially interesting. When a consumer delegates a purchase to an AI agent, that agent evaluates structured data, reviews, return policies, community sentiment, and brand authority signals to assemble a recommendation. Paid media spend and sheer volume matter far less in that calculus than they do in traditional search.

REI CEO Mary Beth Laughton has been vocal about this. Her argument is that trust and human expertise will define the competitive divide going forward, and in the context of algorithmic recommendation, she’s likely right. An AI agent doesn’t care about your media budget. It cares about whether the signals it can read suggest your brand will deliver on its promise.

Deloitte’s 2026 Retail Industry Outlook found that 81% of retail executives believe generative AI will weaken brand loyalty by 2027. If that’s the trajectory, the brands that invested in genuine trust signals before the algorithmic layer fully matures will have an advantage that’s very difficult to catch.

What “Recommendability” Actually Looks Like

Ralph Lauren offers a useful reference point. The brand recently developed Ask Ralph, a conversational commerce tool built with Microsoft. What makes it worth studying is the process behind the build.

Before Microsoft wrote any code, the company sent engineers to Ralph Lauren’s Madison Avenue store to observe how human stylists actually work with customers. They watched how associates read a room and guided decisions through personalized, unhurried conversation. The AI was then modeled on those behavioral patterns, grounded in the brand’s actual service philosophy.

The result is a tool that sounds like Ralph Lauren and reduces the purchase uncertainty that typically suppresses conversion in premium categories. But the more significant outcome may be internal. Building Ask Ralph forced teams that rarely shared strategic space to collaborate on a single, unified brand experience. That alignment is exactly what AI models reward when they evaluate brand coherence across the open web.

The Boring Stuff Is the Real Moat

JD Sports has been making a version of this argument that deserves more attention. Agentic commerce and generative engine optimization are accelerating, but the brands gaining real advantage fixed the unglamorous operational foundations first. Clean product data and accurate, real-time inventory. Reliable order management that doesn’t break when volume spikes.

Without those basics, it doesn’t matter how sophisticated the AI layer sitting on top is. The recommendations will misfire, and the algorithms will quickly learn to route customers elsewhere. Bain & Company’s recent research reinforces this point, showing that half of consumers remain cautious about autonomous AI purchasing. That caution won’t be erased by a sleeker chatbot. It erodes only when brands consistently deliver on what AI agents promise on their behalf.

Meanwhile, the platforms are building infrastructure that will govern recommendation visibility for years. Google and Shopify co-developed the Universal Commerce Protocol, an open standard that lets AI agents transact with any merchant inside conversations. Microsoft’s Copilot Checkout embeds the full purchase flow without redirecting to an external site. And Amazon, reading the same landscape, has blocked external AI agents entirely and taken legal action to protect its position as the default shopping destination.

Whoever controls the recommendation layer controls the customer relationship. The platforms clearly understand that. The question is whether brand-side marketing teams are moving with the same urgency.

What This Demands

The shift toward recommendability asks marketing leaders to evaluate brand equity through a new filter. Awareness and creative still matter. But the brands pulling ahead are building consistent, machine-readable trust signals that AI agents weigh when deciding who to put in front of a consumer.

That means investing in brand authority across trusted, independent sources. It means treating product data as a core marketing asset and making sure internal teams are aligned enough that the brand experience an algorithm encounters is coherent across every touchpoint.

Brands now compete for algorithmic trust alongside consumer loyalty. The ones investing today are building an advantage that compounds, and becomes significantly harder to replicate once the rest of the market catches up.