From structured data management to generative product visibility: what is changing for PIM in the AI age

In conversations with customers, I’m increasingly noticing that the focus is shifting: It’s no longer just “How do we manage our data?” – but “How do AI systems even find our products in the first place?”

The way products are discovered is undergoing a fundamental shift. Users no longer search using just short keywords; instead, they formulate specific questions, describe their needs in complete sentences, or navigate search, shopping, and assistant interfaces through dialog. Language models, chatbots, and AI-powered product recommendations are further driving this development – and the numbers show just how far it has already progressed.

By 2025, about half of all consumers will have used AI tools while shopping (PartnerCentric), 80 percent plan to do so by 2026 (Capital One Shopping). Google AI Overviews now appear for the majority of search queries, and platforms such as ChatGPT Shopping, Amazon Rufus, and Perplexity have established themselves as standalone product advisory channels.

For businesses, this represents a significant shift. In the future, visibility will no longer come solely from a list of results but increasingly from generative responses and advisory product selection. Therefore, effective product communication today relies more than ever on the quality, structure, and consistency of the underlying product data.

The New Rules of the Game: From SEO to Generative Visibility

A new discipline is currently taking shape under the terms Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). The difference from traditional SEO is fundamental: Instead of focusing on keyword rankings and link strength, the emphasis is on semantic clarity, entity recognition, and being cited as an authoritative source. The goal isn’t to be one of the ten blue links—it’s to be the answer.

This doesn’t mean that traditional SEO is losing its importance. On the contrary: GEO builds on the same foundations—high-quality information, clear structure, and trust. The content favored by AI systems is the same content that performs well in traditional searches: fact-based, well-structured, and thematically unambiguous. What is changing is the way this content is evaluated. While a traditional Google search averages four to five words, queries to AI assistants average around 23 words (Otter.ai & Nectiv). Users are having conversations with AI instead of performing keyword searches.

For companies with complex products that require explanation, this is a particularly relevant insight. Generative visibility is not based on new marketing measures, but on the quality and structure of existing product data.

PIM remains the foundation—but the perspective on the data is changing

The good news is: For many companies, this isn’t starting from scratch. Product data is already structured and maintained in the PIM, variants are managed, content is translated, and data is automatically distributed to various channels. It is precisely this structure that enables product information to be utilized in new AI and commerce contexts.

The real change, therefore, often lies not in introducing entirely new data, but in taking a broader view of the existing information. The key question is no longer just: What data do we need for the catalog, shop, portal, or data sheet? Instead, it’s increasingly: What information does a system need to understand a product, compare it, categorize it, and suggest it as an answer to a specific user query?

This shifts the focus from pure data management to an additional perspective on providing answers and advice. This is also reflected in the assessments of leading analysts: PIM is increasingly being positioned as the backbone for AI-driven commerce channels. At the same time, current surveys show that eight out of ten companies cite data limitations as the main obstacle to scaling agentic AI (McKinsey & Company). Both points underscore just how crucial a well-maintained PIM system will be for the next phase of digital commerce.

From Clean Master Data to Responsive Product Data

Today, product data must increasingly be able to answer questions such as:

• What is the product suitable for?
• How do two variants differ specifically?
• Which version meets which need?
• Which alternative makes sense in which situation?
• Which features are particularly relevant to the purchasing decision?

This is a key issue, especially for products that require explanation, are technical in nature, or come in many variants. Historically, many product data models have aligned primarily with master data, classifications, and channel exports. For generative visibility, this alone is often no longer sufficient.

A concrete example illustrates the scope of this issue: An analytics provider reports on a kitchenware retailer where, following the launch of Rufus, individual product segments saw conversion drops of up to 28 percent—while products with fully maintained material attributes remained stable. Amazon’s own figures also underscore this trend: Users who interact with Rufus are 60 percent more likely to complete a purchase (Nova Analytics).

This is why connectable data is in demand: information that is structured, current, and formulated in such a way that search systems, assistants, and AI applications can derive meaningful answers from it. In practice, five requirements are emerging for this.

1. Clear identifiers and clean variant logic

Products and variants must be described unambiguously and consistently. This includes reliable identifiers such as GTIN, EAN, or MPN; clear relationships between the main product and its characteristics; and traceable variant attributes such as size, color, material, or technical specifications.

It is not only important that variants are managed accurately internally; they should also be presented externally in a way that allows systems to reliably distinguish between the base product and a specific characteristic. This is precisely where precise variant descriptions, variant-specific titles, images, and features are becoming increasingly important.

The significance of this is also reflected in hard numbers: Google itself reported up to 20 percent higher conversion rates for correctly entered GTINs. These identifiers are indispensable for AI shopping assistants—they serve as the basis for deduplication, price comparison, and cross-channel mapping.

Without such clear structures, duplicates, unclear mappings, or incorrect recommendations can quickly arise. This is precisely why variant logic is evolving from a purely modeling issue in PIM to a visibility issue in commerce.

2. Semantic Precision Instead of Marketing Prose

Language models tokenize text and establish internal relationships between tokens. A clearly formulated label such as “Max. operating temperature (°C): 250” becomes a usable anchor point for reasoning. Phrases like “premium quality” or “innovative technology,” on the other hand, are semantically empty—an LLM cannot make sense of them.

This is a significant shift: While traditional product descriptions often aim to persuade, AI systems reward objective specifications, explicit use cases, and comparable attribute-value pairs. Marketing clichés and subjective superlatives play hardly any role for AI shopping assistants like ChatGPT Shopping, Google AI Fashion, or Perplexity—objective specifications and comparable facts are clearly preferred.

For generative responses, therefore, additional information that goes beyond traditional mandatory fields is often helpful—such as typical use cases, compatibility details, decision-making criteria, or how the product differs from similar ones. This creates a second view of the product: not just as a data record, but as a response to a specific informational or purchasing interest.

3. Machine-Readable Data on Websites: How Product Data Becomes Visible to AI

Internal structures alone are not enough. The data must also be made available externally in a machine-readable format. Structured data on websites plays a growing role here—that is, machine-readable mark-up that tells AI systems what can be found on a page: which product, at what price, and with what availability.

Its importance for AI visibility can now also be observed empirically: Pages with cleanly implemented, structured data appear significantly more frequently in AI overviews than those without. Industry analyses suggest that products with complete Schema markup—including review and rating data—appear significantly more frequently in AI recommendations; some evaluations indicate this frequency is three to four times higher.

The depth of interconnectivity will become particularly important: It is not individual awards, but interconnected structures—product → manufacturer → organization—that enable AI systems to verify facts.

The key factor here is the view and format in which data is exported from the PIM: at the correct level of granularity, with clear variant resolution, and in a consistent structure.

4. Consistency Across All Channels

A common weak point lies not in data maintenance itself, but in data export. Prices, availability, variant names, or product focus areas differ between the PIM, the feed, and the website. It is precisely these kinds of inconsistencies that become problematic in AI-supported environments—because the more systems evaluate multiple sources in parallel, the more sensitively they react to conflicting information.

Requirements for up-to-date information are also becoming more stringent. AI shopping assistants check availability, price, and delivery time the moment a request is made—outdated data leads to immediate exclusion from recommendations. The era of weekly feed updates is over; most platforms now expect daily or even more frequent updates.

As a result, PIM is increasingly being used not only as a data source for feeds but also as a real-time interface for AI systems.

5. The Questions Products Must Be Able to Answer

In addition to technical fields, content that directly answers typical user questions is becoming increasingly important: short FAQs on usage and selection, comparisons between variants, information on suitability for specific applications, or advisory texts such as “When is which version suitable?”

Such content makes product data more compatible with conversational systems. FAQs are useful here, but not as a standalone solution. What’s crucial is that real questions are linked to reliable product information.

It’s also interesting to note that Google announced new data attributes for Merchant Center in early 2024: answers to common product questions, compatible accessories, product substitutes, and use cases. The PIM is the logical place to centrally manage this content and deliver it consistently.

Data Quality as the Foundation

One point deserves special attention because it is often underestimated in the context of generative visibility: the quality of the source data. AI systems amplify both good and bad data. While a catalog description with missing information was previously simply incomplete, in an AI system it may lead to incorrect recommendations or complete exclusion from search results.

This aligns with what we see in projects: The most common cause of poor AI results is not the models—but the data on which they are based. Current surveys confirm this: The majority of failed AI projects—estimates range from 70 to 85 percent—are due to data-related issues, not the models themselves.

This is precisely where the value of a well-designed PIM system becomes apparent: It is the place where data quality is ensured from the very beginning, rather than having to be fixed after the fact.

What This Means for Existing PIM Projects

For many companies, this does not mean completely reinventing PIM, but rather a targeted expansion—and one built on a foundation that is already solid in many cases.

Existing PIM structures often already cover a large part of the necessary foundation: features, relationships, variants, multilingual texts, media, translations, and automated derivations. Systems like crossbase are designed precisely for such complex requirements and already provide key prerequisites through a flexible data model, product relationships, text modules, API integration, and channel-specific output. The architecture is in place—it now needs to be expanded to include a new perspective.

The next step is to build on this strength in a targeted manner:

• What information is still missing to address specific user queries?
• What differences between variants are currently understood internally but not described explicitly enough externally?
• Which use cases, reasons for purchase, or decision-making aids should be added?
• What data needs to be exported in a different view—for example, for structured data on the website or product feeds?
• How current and consistent are price, availability, and variant data across all output channels?

From a PIM view, this is precisely where the real strategic challenge for the coming years lies. It’s not just about maintaining more data, but about further developing the existing database so that it functions reliably in various search, shopping, and assistance contexts.

Conclusion: PIM as the Operational Foundation for Generative Visibility

The development toward generative search, consulting, and shopping experiences does not diminish the importance of PIM – it enhances it.

After all, the more systems are expected not only to find products but also to understand, compare, and recommend them, the more important structured, consistent, and enriched product data becomes. The foundation for this already exists in many companies. What’s new, above all, is the requirement to supplement existing data models with a response perspective and to convert them into additional, machine-readable views.

In my view, this also presents a real opportunity for companies that are already working effectively with PIM: They aren’t starting from scratch, but rather on a foundation that many others have yet to build. Systems like crossbase, which specialize in complex, technical, and highly variant products, are particularly well-suited for this next phase. A flexible data model, integrated AI support, structured multilingual capabilities, and automated channel output – that is exactly the foundation AI systems need to reliably understand and recommend products.

The key question going forward, therefore, is no longer simply whether product data is complete in terms of completeness. What matters is whether it is modeled, updated, and presented in a way that allows it to answer specific questions, clearly distinguish between variants, and support purchasing decisions – regardless of whether the query comes from a human or an AI system. This is precisely where structured data management transforms into generative product visibility.