Jul 17, 2026 | 5 minute read
B2B AI initiatives are stalling not because the tools are wrong, but because the product data feeding them was never ready. This post explains why catalog data foundation is the actual prerequisite for AI ROI, covers the tribal knowledge problem (product expertise that lives in people's heads and never makes it into any system) that enrichment tools can't solve on their own, and lays out how to build the internal case for funding it.
written by Valerie Levanduski
Most B2B organizations already know their catalog data has problems. The issue is that fixing it keeps losing the budget argument to initiatives with cleaner ROI stories. The traditional case runs on operational efficiency: reduce manual effort, speed up onboarding, cut errors in distributor portals. It rarely wins against initiatives with a clearer revenue line.
The AI era changes the terms. When AI in B2B commerce is on the roadmap, catalog data stops being a back-office problem and becomes a dependency that determines whether those investments work at all. The reframe that works: catalog data investment isn't competing with the AI roadmap. It's the prerequisite for it. Every AI initiative on a typical shortlist (such as smarter search, personalized recommendations, AI-assisted customer service) has a hard dependency on the quality and structure of product data.
Dirty data is a solvable problem. Enrichment platforms and AI-assisted cleanup can handle attribute standardization at scale. The harder problem is data that was never captured at all. In B2B, a significant share of product knowledge lives in people's heads: specs sales engineers have memorized, configuration rules customer service reps know from experience, application guidance that exists only because a tenured employee remembered the right conversation.
AI tools cannot clean what was never recorded. When a search or enrichment system works from a catalog missing this layer, it produces outputs that look structurally sound but fall apart in practice. The attributes are correct, but the detail that helps a buyer confirm fit for their application simply isn't there. That requires process changes upstream in how engineering, product management, and sales capture information, not better tooling downstream.
This means catalog enrichment is two projects: data hygiene, cleaning and structuring what already exists, and knowledge capture, getting tacit product expertise into machine-readable form before any downstream tool can use it.
Most B2B catalogs were built for human consumption: readable descriptions, formatted attributes, downloadable documents. That works for browsers, but AI systems need something different. They need structured, semantically rich content with explicit product relationships, standardized attribute naming, and detailed application context that machines can interpret without inference. What AI agents actually need from your catalog goes well beyond what most organizations have built for today.
Forward-thinking B2B organizations are running two enrichment tracks simultaneously: one for the channels they control today, and one preparing catalog data for the AI-mediated discovery environments they don't. Tools like Product Experience Manager are built for exactly this, managing unlimited catalogs across channels and customer segments without the constraints of legacy catalog structures. The organizations that build for it now will hold a structural advantage as that shift accelerates.
For digital commerce and product data leaders, understanding the problem is rarely the hard part. Getting it funded is. A few approaches that have worked in practice:
Connect to approved AI spend. If your organization has already committed to AI initiatives, map the data dependency explicitly. Show which specific approved use cases require clean, structured catalog data to function. The goal is to reframe data infrastructure as part of the AI budget rather than a separate ask competing against it.
Broaden the coalition before the meeting. Data quality problems in B2B typically touch sales, marketing, IT, operations, and supply chain, but the business case usually gets presented by whoever owns the data problem without those stakeholders in the room. Executives find it easier to reject a single-function efficiency argument than a cross-functional initiative where the operations lead, the IT director, and the sales VP are all making the case together. Building that coalition before the budget conversation is the work.
Run a scoped proof of concept first. A proof of concept applied to a specific product category or a single channel creates visible, concrete outcomes faster than a comprehensive data transformation program. It also reveals the real scope of the problem in a way that's harder to dismiss than a slide deck. Organizations that have successfully funded data foundation work almost always demonstrate a small win before asking for the full investment.
Set baselines before starting. Metrics like zero-result search rates, organic search rankings, PDP conversion rates, time to onboard a new distributor, and inbound support call volume are measurable, tie directly to revenue, and move when product data quality improves. Establishing those baselines before the project starts makes the ROI story concrete rather than theoretical.
Solving the data pre-condition problem ahead of competitors isn't just clearing a hurdle. It builds durable structural advantage.
Better-structured catalog data makes distributor integration easier, and partners gravitate toward suppliers where friction is low. Richer, machine-readable content positions your catalog for AI-mediated discovery before those channels become crowded. As one recent analysis put it, the platforms that lead in AI-driven commerce are those architected for it at the data layer, not those that bolt AI features onto a catalog that was never built to support them.
That work starts with the catalog.
Elastic Path is built to serve as the commerce layer where clean, structured product data gets put to work, across channels, buyer types, and the AI-driven discovery environments reshaping how B2B buyers find and evaluate suppliers.
Schedule a demo to see how Elastic Path delivers unified commerce for leading global brands.