Preparing Product Data for the AI Shopping Surge

In 2025, ChatGPT and Stripe transformed the eCommerce landscape through the launch of Instant Checkout. Marking a step forward in agentic commerce, users now had the ability to buy directly through AI. Once a search and discovery channel, ChatGPT has created an entirely new sales channel that’s expected to surpass traditional search by 2028. From AI-powered checkout to personalized product recommendations, consumers have more choice and flexibility than ever to decide when and where they shop.

Generative AI is being leveraged in various capacities across the retail landscape to up-level the shopping experience and drive consumer value. In fact, 75% of shoppers have already noticed AI recommendations or chatbots online – and the sudden growth isn’t by accident. Of the consumers who have completed an AI-recommended purchase, 84% viewed it as a positive experience. AI’s ability to analyze user behavior and help shoppers find products is transforming item discovery and experience. As of October 2025, ChatGPT and Gemini already accounted for over 63% of AI discovery activity and resulted in over half (52%) of consumers saying they’re likely to buy based on AI recommendations. As shoppers shift methods, brands and retailers must do so too. Companies need to move beyond rigid keyword matching and optimize the eCommerce experience to how users actually search, and buy.

As AI interprets search queries and analyzes shoppers’ requests, brands and retailers have to make sure their back-end systems can keep up with it all. Preparing for the acceleration of AI shopping means ensuring product data is optimized for AI to comb through it, and that the product data is accurate and consistent at every touchpoint. The fact is, the future of online shopping and product search is already here. If brands and retailers want to keep up, they have to prepare today, and it starts with product data.

Product Catalogs Aren’t Ready for the AI-Shopping Acceleration

For years, product data has been optimized for traditional search methods. Think Search Engine Optimization (SEO) strategies built around long-tail keywords or internal linking aimed at bumping up relevancy. Today, focusing on just traditional search channels means risking a decline of 20 to 50% of traffic. Marketers, brands, and retailers need to ensure that they are incorporating AI search models to properly target consumers and optimize visibility.

Despite 47% of U.S. shoppers already using AI tools for at least one shopping task, countless product catalogs have yet to be optimized for GenAI. Currently, many product catalogs lack structured data files, are missing context, or have inconsistent attributes across products. MIT even reported that 95% of GenAI pilot programs fail largely due to poor or fragmented data foundations, which can cost some organizations as much as $25 million, or more, a year.

Unlike traditional SEO, GenAI Engine Optimization (GEO) requires structure, context, and consistency when it comes to data. If product catalogs are missing any of these key elements, it means content won’t be surfaced to shoppers by AI agents, even if it’s what they’re looking for. AI hallucinations and poor recommendations stem from weak product input, not the AI model failing, and it’s up to teams to ensure they are working with the new models. Having just “good enough” product content isn’t sufficient in the AI era, especially when inconsistent results are shown to break down user trust. To see AI shopping succeed to its fullest potential, brands and retailers need to shift their focus toward the foundation of commerce: their product data itself.

While AI owns a bigger chunk of the buying funnel, AI-driven search isn’t eliminating customer relationship building. In fact, AI’s ability to predict customer preferences reinforces true customer trust. So much so, that customers are willing to pay an average of 25-30% more for products with complete and high-quality product information. In today’s shopping landscape, AI is driving enormous potential for brands and retailers – but reaping the benefits requires up-leveling product information like never before.

The Elements Needed for Success in the AI Shopping Era

For AI shopping to work best, it needs rich contextual information that can help the agent identify who the product is for, why it’s relevant to them, and why it’s different from other products. When all this information is readily available, AI can make a strong, data-backed recommendation to shoppers.

Knowing if your data is AI-ready or not means understanding if it meets the criteria AI needs in order to produce well-informed responses. For AI shopping, this means asking yourself, and your team, seven crucial questions that will help determine whether product information is strong enough to support accurate shopping recommendations.

1. Single Source of Truth & Governance: Is there one central system where product data, including validation rules and version history, lives and can every system rely on it? AI agents evaluate thousands of Stock Keeping Units (SKUs) in seconds. If attributes are duplicated, inconsistent, or fragmented across systems, the models lose confidence in the data and can make incorrect inferences. Data structures should be consistent across all products and adaptable to change over time. This way, AI models won’t break as product catalogs evolve.
2. Model & Taxonomy: Are categories, attributes, units, and value lists defined, consistent, and shared across teams so products can be easily compared? Models rely on shared meaning. If the words “material” or “fabric” exist as separate concepts, for example, then models will struggle to compare products. Consistent definitions across teams helps reduce AI bias and ambiguity, while improving the accuracy of recommendations.
3. Completeness & Normalization per Channel: For each channel, are required attributes complete, normalized, and easy for AI to compare across SKUs? AI can’t infer what isn’t there, so double check that there’s a high data volume per SKU and that values are normalized and easy to compare. More data points per product allow AI models to recognize subtle patterns, improving prediction accuracy.
4. Rich Content & Digital Assets: Does each product include rich descriptions, images, videos, and guidelines that are structured and easy for AI to interpret? AI needs rich fields, like intended use cases and materials, to enable stronger AI analysis. Much of today’s information, however, is trapped in unstructured formats, like PDFs or images. This type of content often requires extensive cleaning to become AI-readable. Structuring data upfront can reduce errors and long-term effort.
5. Localization & Region-Specific Readiness: Are languages, units, sizes, and regional requirements clearly structured and governed with human review where needed? AI models trained on global data need regional context; otherwise, it can produce incorrect recommendations. Ensure there’s workflow-driven governance that blends automation and human oversight, catching any unit changes or non-localized attributes. Human oversight is critical to ensure AI outputs remain accurate as data is translated, converted, and localized.
6. Supplier Data Onboarding & Discoverability: Do suppliers submit data in standardized formats with consistent identifiers that AI can easily connect and compare? AI performs best when it can cross-reference multiple data sources. Supplier data that arrives inconsistent or semi-structured weakens the entire model. To get ahead of this, teams need standardized input templates and consistent identifiers to ensure third-party data is AI-friendly. As a bonus, connecting supplier data with other sources, like marketplaces or customer data, can improve AI accuracy and reduce bias.
7. AI Agent & GEO Discoverability: Is product data machine-readable, enriched with structured markup, and built to adapt as AI-driven discovery evolves? AI models perform best when data is delivered in predictable, structured formats (think tables, rows, standardized files), rather than inconsistent formats like PDFs or Word documents. As AI-powered discovery grows, architecture needs to be future-proof so models and agents can continue to interpret the data for years to come.

Rules of Thumb for the Best Results

Shoppers have their choice when it comes to AI tools, turning to ChatGPT or store-specific assistants, such as Amazon’s Rufus. While brands and retailers own the product catalogs AI can gather information from, not all of them have their own AI interfaces. Meaning they don’t have full control over the AI tools analyzing their products, just the data that’s fed into the models themselves.

Staying competitive in today’s commerce means ensuring products are visible to AI agents and, more importantly, that the data behind them is accurate. All data must come from a credible, verifiable source with its own track record of accuracy. Whether it’s coming from a supplier or data provider, it must adhere to data collection standards and regulations (such as GDPR in Europe or the California Consumer Privacy Act). If datasets contain biases or inaccuracies, the AI tools could perpetuate them and ultimately spread inaccurate information.

For best measure, brands and retailers should routinely audit data to ensure it remains consistent and accurate. Data formats should be followed at all times, and there shouldn’t be any unintended changes to the data over time.

Adhering to these best practices means having a strong foundation for commerce, then for AI to work. When data is accurate, results are valuable, and that’s what keeps customers coming back to the brands and retailers they trust.

Looking Ahead

The AI shopping revolution is here. As consumers increasingly turn to AI assistants for shopping queries, the technology will continue to grow and expand its capabilities. With time, they may even prove to be the most important factor contributing to a purchase decision.

Companies need to adapt quickly to keep pace with the commerce changes, and for many, this means taking a hard look at product information readiness. Traditional search is changing, and today, the brands that lead the way aren’t the loudest in the room, but the most meticulous. If data isn’t ready for AI integration now, it won’t show up in front of tomorrow’s buyers.

While AI models continue to evolve, one thing is clear: success lies in a strong commerce foundation, and the strongest brands will turn data into intelligence and intelligence into trust.