Every era of retail has had a moment where the rules changed and winning retailers reinvented to build the next generation of great businesses. The corner store gave way to the mall; the mall gave way to e-commerce; and e-commerce gave way to the algorithm. That next moment is here again, only this time the customer isn’t the one driving the change, the agent is.

Agentic commerce isn’t just changing how shoppers find you. It’s changing whether they find you at all.

This year generative artificial intelligence surpassed the physical store as the top discovery channel for active AI users. Seventy-one percent of consumers expect AI to influence at least half their spending decisions in the next 12 months.

Most of the industry is treating this shift as a data infrastructure problem, and they’re partially right. Focusing on better product feeds, richer metadata and cleaner APIs are foundational. The problem is that they’re not sufficient for growth. They’re the cost of doing business in an agentic world. AI recommendations aren’t built on specs, they’re built on evidence. And evidence comes from people.

That creates a new dynamic in the customer journey: machines may make the shortlist, but people still look for human proof before they act. Almost half (48 percent) of shoppers verify AI recommendations on Reddit, underscoring that AI-driven discovery doesn’t eliminate human validation, it increases its importance. Being machine-readable only gets you into consideration. Having credible human evidence around your brand is what turns that consideration into confidence.

To thrive in this future, retailers need something harder to replicate: a rich ecosystem of creators, customers and communities generating proof that they matter at a scale and depth that agents can read, trust and cite.

Here’s where to start:

1. Make yourself readable to machines, not just humans.

If an agent cannot crawl your site, parse your product data, verify your claims, and check your inventory in real time, none of the other moves matter. Mondelez discovered that even Oreo, one of the most recognized brands in the world, appeared in only 10 percent of AI chatbot recommendations for cookies. Mondelez’s response was to overhaul its entire digital presence to make product content machine-readable across its brand portfolio. For retailers, the same logic applies: structured data, verified attributes, real-time availability, and clean APIs are not a competitive advantage, they’re table stakes.

2. Turn creators and communities into an evidence network.

Most creator programs are designed to generate reach. However, the better model is to use them to generate evidence. Every detailed review, styling video, honest comparison, and tutorial becomes a signal that helps both humans and machines understand why a product deserves consideration. A creator who explains exactly why a product works for a particular person, in a particular situation, is doing more for your agentic findability than any campaign built for impressions.

Customer-to-customer communities are another part of the fabric of evidence that AI learns from and customers use to verify. Engagement strategies that incentivize the audience to transition from passive to active, whether it’s leaving a five-star review with a transformation photo or sharing brand love in r/BuyItForLife, have compounding results in an agentic world.

3. Get specific about the moments you own, not just the products you sell.

Agents don’t need more assets, they need context. Retailers gaining ground are the ones associated with clear, specific narratives: the most trusted destination for dressing a big occasion, the place people turn to when they entertain, the retailer that knows how to outfit a family through every chapter of life. Categories matter less than memorable and highly specific associations. If a shopper can’t finish the sentence, “I go to [retailer] when …” you don’t have a narrative an agent can use. Generic positioning is as invisible to machines as it is to humans, except machines decide in milliseconds and move on.

4. Build around customer missions, not product categories.

The strongest signals increasingly come from people helping other people solve real problems. “How do I furnish a first apartment on a budget?” generates richer, more specific intelligence than “living room furniture.” Retailers that show up as the trusted guide for a shopper’s actual situation will outperform retailers that merely organize SKUs. And the data backs it up: Walmart shoppers who engage with the Sparky AI assistant spend 35 percent more per order because the experience is built around intent rather than inventory. How often your brand gets referenced in those conversations, by customers, creators, journalists, and communities, is exactly what AI systems learn from.

5. Make your expertise visible and findable.

Most retailers sit on extraordinary institutional knowledge: buying rationale, fit guides, category expertise, styling logic. Yet most of it’s buried in PDFs, training materials, or the heads of their associates. In an agentic economy, expertise needs to be discoverable. A retailer that teaches customers wins more often than the retailer that only sells to customers. Agents recommend sources that can answer a question, not just fulfill an order. Publishing that knowledge in formats humans and machines can both read is one of the highest-return investments a retailer can make right now.

Every era of retail has had its scarce resource: first shelf space, then search position, now stature.

While the prevailing worry might be that AI will commoditize retail and make brands matter less, evidence suggests that the opposite is more likely. When an agent narrows thousands of options to a handful of recommendations, reputation becomes more valuable than ever. And stature belongs to the retailers that have earned a place in the conversation long before the transaction begins.

Sources: Accenture Consumer Pulse Research 2026, “Talk to My AI”