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For two decades, the internet taught shoppers to think like machines: typing “best waterproof backpack” or “12-cup coffee maker” instead of simply saying what they want.
Now, agentic AI has taught shoppers to treat the internet like their personal assistant. They tell an agentic AI platform (such as Perplexity, ChatGPT, Copilot, and Gemini) exactly what they want: “Find me a backpack that fits under an airplane seat, holds two days of clothes, and has a side pocket for a water bottle, for under $20.”
And instead of a wall of blue links, they get instant, curated, context-aware recommendations. Discovery, evaluation, and (increasingly) checkout are collapsing into a single conversation.
This is agentic shopping in 2025. It’s here, and it’s moving fast: more than 60% of U.S. consumers already use AI tools in their purchase journeys. At Rithum, we’re seeing this transformation up close across thousands of marketplaces, brands, and retailers. The ones getting ahead are training the models now (or months ago), capturing answer share, and setting the new standard for AI-driven commerce.
How agentic AI is different from any other technology shift
The internet took 30 years to reach mass adoption. Smartphones took 15. AI has crushed that adoption curve, with ChatGPT becoming the fastest-growing consumer application in history, surpassing 100 million active users within two months of launch. Three years later, more than 53% of people now use AI tools in some form, whether for search, writing, or shopping.
Where you once had years to adapt your digital strategies, AI has forced real-time recalibration. Up to 18% of Google searches are now served by their AI Overviews, reducing organic click-through rates. And the agentic AI path of turning search to transaction without ever leaving the interface is the next step in what will certainly be a continuing evolution.
What agentic AI changes for consumers in 2025
Agentic AI adoption is moving so fast in part because it fits how people already want to shop:
- It feels more natural. Shoppers describe what they want in plain language instead of guessing keywords, and prompts like “fits under a plane seat” or “vegan leather under $100” get smarter, more relevant results. Results are personalized answers, not a wall of links.
- It moves faster. Agentic AI handles discovery, filtering, and checkout in one conversation and the last step of the funnel—the one most likely to lose a sale—gets streamlined. Shoppers go from idea to purchase without switching tabs or apps.
In short, agentic shopping removes digital friction. It’s relevant, conversational, and fast. And as the experience shifts so does visibility—if your product isn’t in the answer, it’s not in the conversation.
What agentic AI changes for sellers in 2025
The digital shelf is now inside AI. Visibility depends on whether an assistant recommends your product, not whether you ranked high in traditional SEO. That shift changes the rules for selling:
- Product data becomes narrative: Agentic AI doesn’t care about brand familiarity or ad spend. It selects products based on structured data—attributes, descriptions, and language that matches how people actually ask for things. A shopper won’t search for “SKU 4587.” They’ll say, “I need a waterproof backpack that fits under an airplane seat.” If your catalog doesn’t speak that language, you won’t be in the answer.
- Speed becomes survival: These systems require precision and freshness. Inventory, pricing, images, and product variants must be accurate and up to date in machine-readable formats. If your data lags, you disappear.
- Performance metrics shift: Traditional KPIs like sessions and impressions won’t tell you how often you’re being recommended. What matters now is answer share—how often your product is selected by assistants relative to competitors.
The new discipline: Generative Engine Optimization (GEO)
Just as SEO defined web commerce, GEO will define AI commerce. GEO is about making your products intelligible—and favorable—to AI assistants. At a high level, GEO looks for:
- Expressive product data. Product descriptions written for humans. Titles, bullets, and descriptions should mirror how people actually ask for things.
- Structured context. This includes clear, machine-readable attributes and use-case language that models can parse and match to intent.
- Credibility signals. Citations and social proof that help models prefer your product over any other options.
- Freshness at scale. Real-time feeds so recommendations match reality (price, stock, variants).
That’s why GEO is built for clarity, helping platforms read your catalog the way your best rep sells it.
What retailers and brands should do now
Brands and retailers that want to stay visible need to treat it agentic AI with the same rigor they once applied to SEO or mobile. Start with these first steps:
1. Speak the way your customers do and make your catalog machine-readable
Gather 50 to 100 natural-language prompts your buyers would actually use. Then audit your product content. If it doesn’t answer those prompts, AI won’t either.
It’s not enough to list specs. A good agentic AI-ready product feed explains real-world fit: “fits under airplane seat,” “wide toe box for marathon training,” “vegan leather, under $100.” That’s what models—and shoppers—respond to.
Build an LLM-ready feed with structured titles, bullets, attributes, and variant data. Refresh it often. If your feed isn’t clean, current, and easy to parse, you’ll be skipped.
2. Connect your credibility.
Models weigh signals. Surface your reviews, ratings, creator content, and authoritative mentions in places models can find and index them.
3. Track answer share, not just impressions.
Start measuring how often your products appear in AI-generated answers for priority prompts. If you’re not showing up, you’re not being considered.
At Rithum, the brands we see gaining early ground are treating this shift as foundational, not as a fad.
There is real urgency here. That doesn’t mean you need to overhaul everything overnight, but it does mean that waiting is the riskiest move you can make. Visibility inside agentic AI systems compounds over time—and early movers are already training the models on their language, attributes, and credibility signals.
In addition to the above steps, make sure you’re not held back by these common myths:
“We’ll wait until it’s bigger.”
It’s already big. More than 60% of U.S. consumers use AI tools in some form. Platforms like ChatGPT, Perplexity, Copilot, and Gemini are rapidly becoming product discovery surfaces. By the time it feels mainstream, the top spots will be locked in by those who started training the system early.
“Connecting a feed is enough.”
Feeds give you access. But access doesn’t guarantee placement. Selection depends on relevance, context, and clarity. Product data must be structured and strategic—written for how consumers ask, not just how systems store.
“This replaces our channels.”
Agentic AI doesn’t replace search, retail media, or marketplaces. It sits across them. Think of it as a new, high-intent layer of demand—one that amplifies the channels you’ve already invested in, but rewards brands that are ready to be found in conversation.
How Rithum can help you get agentic AI optimized in 2025
Clean, structured data is the best way to be found by agentic AI. At Rithum, we’re focused on helping retailers and brands deliver that clarity at scale. The agentic era is here, and as this shift accelerates, answer share will matter more than page rank. Make sure your products are ready to compete. Talk to us—we can help.
