Agentic commerce, or AI-driven shopping, isn’t exactly a user experience trend, but a “transformation,” according to David Ingram, chief product officer of Rezolve Ai, a platform that develops agentic commerce solutions for companies.
He explained that the rise of AI-influenced shopping is shifting how products are discovered. It is changing the focus from traditional search engine optimization (SEO) to a generative engine optimization , which is the foundation for how AI systems understand and select products.
How is GEO changing how brands show up online?
AI platforms are gradually becoming consumers’ first point of contact when searching for a product, which changes brand visibility online.
“We’re increasingly expressing our intent conversationally rather than navigating websites manually,” Ingram explained. “Very many of us go first to AI, then to a brand e-commerce site.”
Rather than discovering a product via SEO keywords or a structured search, consumers are communicating their preferences, needs and goals in their own language to bypass endless scrolling and find that one solution. For brands, that means their sites need to be optimized for interpretation by AI agents digging for the best answer, or answer engine optimization (AEO).
Between, SEO, AEO and GEO, the flurry of acronyms can be overwhelming to marketers – but companies like RevolveAI and Noise Media Group provide AEO and GEO audits to help brands identify where, how and why they’re showing up in comparison to their competitors. Ultimately, the strength of a brand’s web presence – website, blogs, quest posts, social media comments – will determine how often they show up on the answer “shelf.”
The ‘answer layer’ replaces the digital shelf
With agentic commerce compressing the digital shelf into a narrower interface, competition is fundamentally changing, according to Ingram.
He points out that the models are now “good enough to really properly understand context” and “very good at guiding decisions.”
Rather than consumers seeing dozens of SKUs on a page, brands are now competing to be in a smaller set of AI-curated recommendations or potentially excluded altogether. Yet rather than brand awareness or paid placement determining inclusion, it’s finely tuned data.
“Make sure that your catalog data is complete, accurate – not just parametric – [and] has some use case information,” Ingram said.
Ingram emphasizes that it’s not enough for brands to rely on traditional product attributes. Brands need to explain “what is the product useful [for] … how does the product relate to use cases that a consumer might be searching for,” he said.
Why product data must evolve beyond SEO
For most brands, product data has historically been optimized for e-commerce listings and search engines. But Ingram argues that’s no longer sufficient.
“It’s a different game now,” he said. “When we’re searching as consumers with an AI assistant, we’re … using a much longer-form query, a more use-based query.”
He gives a clear comparison:
- Traditional search: A white shirt in a specific size with a cutaway collar
- AI-driven search: “I am looking for [a] shirt that I can wear to work, that packs well for travel and comes out my suitcase uncreased.”
That search query requires a different kind of data structure from brands that connects product attributes to real-world use cases and outcomes, Ingram explained.
For CPG brands, the data gap between SEO and GEO is now a visibility risk, he added.
Visibility starts with the foundation first
Brands should refrain from optimizing for specific platforms too early and instead begin with strengthening their foundation around structured product data, clean content and real-time feedback.
“I wouldn’t yet try to optimize for one or other,” Ingram said, referring to OpenAI, Google, Amazon and other AI shopping surfaces.
“Ensure first of all that you’re showing up. That’s the foundation of it all,” he emphasized.
Better decisions, fewer returns and higher expectations
As AI improves the discovery process, it creates more informed purchases, Ingram explained. He frames product discovery as “quite a creative process,” one that becomes “more beautiful, natural … [and] satisfying” with conversational AI.
With AI agents consumers can now ask nuanced, detailed questions about fit, performance or attributes that would have been difficult to answer through traditional browsing. The result is a more refined match between product and need.
But these hyper-attuned recommendations come with a tradeoff.
Ingram suggests improved understanding about a product before purchase is likely to “drive down returns” – a plus for brands.
At the same time, consumers’ expectations rise because they have more clarity before they buy. These shifts reinforce why GEO is about accuracy as well as visibility.
Start with visibility, not perfection
Despite the scale of change, Ingram’s advice is practical: Don’t try to solve everything at once.
“Start small, move fast,” he said, pointing that these changes can be small, fast and light.
Yet, even those early, smaller steps need to be rooted in a clear understanding of how brand visibility is evolving online. Within agentic commerce, the question is no longer about a brand showing up on the first page of a search, it’s whether the show up in the answer.
