Your Catalog Data Problem Is Already an AI Problem

Key Takeaways

• B2B buyers using AI-powered answer engines arrive knowing what they want, which drives higher conversion rates than most traditional traffic sources.
• Unstructured and API-inaccessible catalog data will not be surfaced by AI discovery engines, making product data quality a direct revenue exposure rather than just an operational nuisance.
• Successful catalog data initiatives require an internal champion who can navigate cross-functional stakeholders, build the business case, and drive incremental progress rather than waiting for a top-down transformation mandate.
• Scope shrinks dramatically when the project starts from what customers need to make purchasing decisions rather than from a broad data audit.
• Many mid-market B2B companies with homegrown systems have better underlying data hygiene than peers who relied on third-party platforms, and they are often better positioned to move quickly.
• Clean, structured catalog data drives LLM discoverability, which drives high-intent traffic, which drives conversion. Each improvement builds the internal case for further investment.

The Discovery Channel You Are Not Optimizing For

LLM-referred traffic is already converting in B2C, and the pattern is consistent: buyers who arrive via an AI answer engine are high-intent. That behavior is moving to B2B fast. A buyer asking an AI which supplier can fulfill a specific industrial component will find whoever has structured, accessible catalog data. Suppliers whose product data cannot be read or indexed by those systems will not appear in the results at all.

As Elastic Path's Intelligent Commerce architecture makes clear, buying journeys are increasingly beginning with AI-driven discovery rather than storefront navigation, which makes the catalog the primary source of truth that both humans and AI systems rely on. Answer engine optimization is the new SEO, and it rewards the same thing SEO always has: clean, structured, accessible data.

Elastic Path itself is seeing this. Buyers searching for a composable commerce platform built for complex B2B catalog requirements are finding Elastic Path through LLMs. The company that helps its customers become AI-discoverable is itself becoming AI-discoverable.

Three Things That Actually Have to Be in Place

Getting catalog data AI-ready is an organizational capability. Three foundations tend to separate companies making progress from those that are not.

1. A leader who owns the problem. Catalog data sits at the intersection of IT, sales, product management, and digital commerce, which means no single team owns it by default. Without someone willing to navigate that terrain, identify stakeholders, and build the internal case, the initiative stalls. The organization needs a leader who sees both the problem and the career opportunity in solving it.
2. A scope defined by customer decision-making, not data completeness. The information you need largely exists already, embedded in what your sales organization does every day. The question is whether it has been translated into structured product data. Starting from what a buyer needs to make a purchase decision — rather than from a full data audit — makes the project tractable.
3. API-accessible infrastructure. AI agents parse structured data, apply pricing rules, and transact programmatically. Many B2B organizations have repositories that simply are not exposed that way. Getting catalog data into a system readable by discovery engines is the technical prerequisite everything else depends on. B2B commerce needs a system of work for AI with structured product data, pricing rules, and real-time availability that AI agents can act on.

The Mid-Market Advantage Nobody Talks About

The assumption that larger companies with more resources are better positioned for AI readiness does not always hold. What Elastic Path sees among its mid-market customers is often the opposite, and it is consistent with the broader B2B eCommerce platform landscape.

Companies that built their own B2B commerce channel rather than deploying a packaged solution often have better data hygiene for a straightforward reason: they had to confront the data problems themselves rather than assuming a vendor would handle it for them by default. That active ownership produced cleaner underlying data and teams that already understand the infrastructure. This is not an argument for building alone. Elastic Path's services team can help structure that data, and Product Experience Manager gives teams a system to manage it going forward, so companies do not have to start from scratch. But the habit of taking ownership rather than expecting a platform to solve data quality automatically is what separates companies that move fast from those that stall. Those companies are now in a position to move faster than peers who relied on third-party platforms and never developed the organizational muscle for data ownership.

What to Measure

The core KPIs have not changed: average order value, conversion rate, and self-serve transaction rate. What is new is LLM-sourced traffic as a trackable channel. Elastic Path's customers are beginning to measure what percentage of inbound traffic arrives from AI answer engines and how it converts relative to paid search and referral. LLM-referred buyers tend to convert at meaningfully higher rates because they arrive oriented and already qualified. The cost of inaccessible product data is no longer just operational friction. It is directly measurable as missed revenue.

Start Smaller Than You Think You Need To

The most reliable path to launching nothing is trying to fix everything first. The right approach is to build the core catalog, assess the gaps, and enrich incrementally.

Elastic Path's Product Experience Manager is built for this pattern. It handles complex B2B product data and functions as the data fabric connecting catalog infrastructure to the AI-driven discovery layer. It enables teams to publish structured, LLM-optimized product, pricing, and bundle data so the same trusted catalog feeds storefronts, sales teams, and AI buying experiences without duplication or custom integration.

The AI-readiness gap in B2B commerce is real, measurable, and closing faster for the companies that started than for those still scoping. Your buyers are already using AI to find suppliers. The question is whether they are finding you.