5 Ways to Make Your PDPs GEO-Ready 

special guest post by Caitlyn Ford and Brittny Cantor, AI Commerce Innovation Leads at Accenture.

For years, your product detail page (PDP) was the critical bridge to turning browsers into buyers by giving them specs, visuals, and the confidence to click “add to cart.”  

Generative AI has changed the path to that click. Shoppers aren’t reading your PDP first; agentic AI is. These agents scan your descriptions to decide whether a product is worth a shopper’s time—and sometimes even handle the recommendation, summary, and transaction themselves. Agentic AI assistants, such as ChatGPT, Gemini, and Perplexity, are now common starting points for product research, and Google’s AI summaries increasingly sit above the traditional blue links, often answering the query without a click. Analyses show that when an AI overview appears, the #1 organic result can lose roughly a third of its usual clicks, and more than half of Google searches already end without any website visit at all. 

That means your PDP now speaks to three audiences at once: the shopper, search algorithms, and the AI agents deciding what gets recommended. You’ve built for shoppers and search, but when agents answer a query, it gets more complicated. They synthesize data across multiple PDPs, reviews, listings, and brand content into one recommendation. To even be brought into their considering, your PDP must be accurate, consistent, structured, and easy for AI to parse. Because most AI summaries trigger on informational, top-of-funnel queries, discovery and education are increasingly happening inside AI layers, while the remaining clicks are skewing toward bottom-funnel, brand- and product-specific searches. That makes the quality of your PDPs even more important: when a shopper finally does click through, they’re usually closer to a decision than they used to be. 

This is Generative Engine Optimization (GEO) for your product page: optimizing the content so AI agents see your product as consistent, credible, relevant, and complete. In GEO terms, you’re optimizing not just for rankings, but for how (and whether) AI systems mention and cite your products inside their answers—whether that’s a link in a Google AI Overview or a product shout-out in a ChatGPT or Perplexity response.

GEO doesn’t replace SEO—it sits beside it. The fundamentals still matter, and you can start with a few practical moves to make your PDPs GEO-ready without losing ground with direct shoppers or SEO. Traffic from LLMs convert 9x higher than traditional search.  

1. Build for questions, not keywords 

Traditional SEO was about ranking for keywords. GEO is about being the best answer. AI agents scan content to understand intent. This includes who the product is for, what problem it solves, and why it’s trustworthy. Under the hood, modern search engines break complex prompts into many related fan-out queries (such as budget, location, use case, constraints) and then recombine the best matches into one synthesized answer. If your PDP doesn’t clearly map to those real-world questions, it’s less likely to be pulled into that synthesis. 

Start by rewriting your PDPs to sound more conversational. Frame details around real-world questions: “Is it waterproof?” “How long does it last?” “Is it safe for sensitive skin?” Product FAQs and comparison callouts matter more than ever because they train AI models on context, which is the very thing that makes a product relevant in generative results. 

Pro tip: The more structured and direct your answers, the more easily AI can cite and summarize them. Think in use cases: 

• Instead of: “Premium leather tote with zip closure and inner pocket.” 

• Try: “Looking for a work tote that fits a laptop and zips closed for travel? This one holds up to 15 inches and has a reinforced base for durability.” 

The goal is to pre-answer what a shopper might ask, because AI is looking for the best source to do just that. When generative systems are doing the research legwork on behalf of the user, you want them to find crisp, unambiguous language they can safely reuse or summarize on your behalf. 

2. Standardize data across every channel 

Whether you sell through Amazon, Walmart, Target, Instacart, or your own DTC site, the same PDP likely shows up in multiple places. For AI, inconsistency kills trust, as agents compare listings across channels. If your dimensions differ on HomeDepot.com and your own brand site, the agent is less likely to choose either. 

This isn’t just a data hygiene issue; it directly affects how AI systems model your products. Large models build their understanding of brands from many sources—retail PDPs, comparison sites, reviews, blogs—so if product specs conflict across those surfaces, the safest move for the AI is to ignore you or recommend a competitor with cleaner signals.  

Pro tip: Use shared taxonomies, normalize specs, and keep your product attributes up-to-date everywhere. AI agents can’t make sense of your product if your systems don’t agree on what it is. 

• For brands: Build and maintain a “single source of truth” for product data—ideally housed in your PIM and pushed everywhere via API or feed. 

• For retailers: Set clear PDP templates and data ingestion rules for third-party sellers to reduce conflict and duplication. 

Think beyond your own site, too. If comparison or review sites are important for your category, make sure your specs, naming conventions, and positioning match what appears there. AI answers often lean heavily on third-party sources—reviews, forums, round-ups—rather than a single brand’s website. 

3. Focus on proof, not promotion 

Agentic AI is trained to be skeptical. They value facts, third-party validation, and consumer signals. That means your PDPs need more than adjectives—they need evidence. It’s up to you to provide it.  

For example: 

• Instead of “super comfortable,” include “Rated 4.7/5 by 2,100+ customers for comfort.” 

• If you claim sustainability, link to third-party certifications like USDA Organic or OEKO-TEX®. 

• If your laptop is rugged, cite test conditions: “Drop-tested from 1.2 meters to meet MIL-STD-810G standards.” 

AI is scanning your product page for trust markers. Verified reviews, return policies, sourcing info, and warranty terms matter. This is valuable for customers as a rule of thumb, but is even more important now for the bots deciding what gets recommended. In AI search, these proof points do double duty: they help the model decide whether to feature your product at all and, if it does, they provide quotable snippets (ratings, certifications, test results) that can be pulled directly into summaries or comparison tables.  

Remember that a lot of this “proof” now lives off-site as well. If your products are reviewed on blogs, YouTube, Reddit, or niche forums, those conversations shape how AI systems describe you. Investing in credible third-party coverage and cultivating high-quality reviews does more than build social proof. It feeds the training data that powers AI recommendations in the first place. 

4. Automate freshness to avoid invisibility 

PDPs that are out of date—on price, inventory, delivery estimates, or product specs—will get skipped. AI tools now favor real-time accuracy. So if your shampoo shows as “in stock” in one feed and “out of stock” on your brand site, that confusion may cost you visibility altogether. 

• For brands: Connect your PDP data to real-time inventory systems and marketplace feeds. Set up automated triggers to refresh PDP content when price or availability changes. 

• For retailers: Sync PDP fields with seller dashboards and flag stale listings via scoring models or AI-based health checks. 

Timeliness isn’t a nice-to-have. It’s how you stay visible. An outdated PDP isn’t just underperforming. It’s invisible to AI. And as AI layers absorb more top- and mid-funnel traffic, the remaining clicks from search are increasingly tied to bottom-funnel, transactional experiences. That makes it even riskier to let inventory, pricing, or availability drift out of sync across feeds, because those are exactly the signals AI systems rely on when deciding what to surface for high-intent shoppers. 

5. Optimize for a multi-agent future 

AI agents are already embedded across search engines, voice assistants, mobile devices, and retail platforms. And they’re only getting more proactive. Soon, they won’t just answer your questions—they’ll anticipate and fulfill them. 

Your PDPs need to be ready for that shift now. Start by: 

• Structuring your content with pre-defined templates.

• Getting insights into current benchmarks and unlock opportunitites/risk with visibility analysis

• Evaluating prompt coverage. Which products are showing up for common category queries? Which competitors are dominating agent responses? 

GEO is the framework that ties this together: it extends classic SEO into a world where AI assistants summarize, compare, and recommend before a shopper ever sees a SERP. In practice, that means designing your PDPs so they’re easy for AI to understand and safe to quote, then measuring success not just in rankings and sessions, but in how often you’re mentioned or cited inside AI answers.  

Early data suggests that even if total traffic drops, the visits you do get from AI-mediated journeys can convert disproportionately well. In one analysis, LLM-originated visits accounted for a tiny share of total clicks but a materially higher share of sign-ups—because those users arrived later in the decision process. That’s exactly the kind of high-intent traffic your GEO-ready PDPs should be engineered to convert. 

Whether you’re managing thousands of SKUs across marketplaces or a curated set of products on your DTC site, your PDPs now serve a dual audience: the buyer and the buyer’s agent. 

Search behavior is changing. Make sure your PDPs are ready. 

AI agents are now the first (and sometimes only) step in a shopping journey. If your PDPs aren’t ready, you won’t make the cart. 

The good news is that GEO isn’t a separate track. It builds on what good PDPs already do: clear writing, accurate data, consistent structure, and real value to the shopper. You’ve got this (and Rithum can help). 

Caitlyn Ford and Brittny Cantor are AI Commerce Innovation Leads at Accenture.