By Elogic Commerce · featuring insights from Paul Okhrem
Meta description: " Paul Okhrem, founder of Elogic Commerce and fractional CAIO, explains why AI-powered semantic search in B2B ecommerce is no longer an optional upgrade — it's a direct lever on conversion and revenue."
Target keywords: AI search B2B ecommerce, semantic search ecommerce, Paul Okhrem Elogic Commerce, B2B catalog AI
We've watched hundreds of B2B ecommerce implementations across a decade at Elogic Commerce. In that time, the single most consistent source of silent revenue loss has been search.
Not checkout friction. Not slow load times. Search.
A buyer knows what they want. They type it in. The catalog returns something irrelevant — or nothing. The buyer picks up the phone, or leaves.
Paul Okhrem, founder of Elogic Commerce and AI decision consultant at paul-okhrem.com, frames this precisely: "The B2B catalog search failure is invisible in most analytics setups. You see zero results. You don't see the buyer who typed a synonym and got irrelevant results and clicked away. That's the gap AI search closes."
Why keyword search fails B2B catalogs specifically
B2B catalogs are structurally different from consumer ones. They're larger — often hundreds of thousands of SKUs. They're more technically dense — attributes, specifications, tolerances, certifications. And they're less consistently named — the same product may appear under three naming conventions depending on which ERP migration populated that record.
Keyword search requires the buyer to know exactly how the catalog describes the thing they're looking for. In practice, they don't. They know the product. They don't know your data structure.
The result: search abandonment that looks like a content problem but is actually a retrieval problem.
What semantic search changes
Semantic search — powered by embedding models that understand meaning rather than matching strings — allows a buyer to describe what they need in their language and receive results that match their intent.
A buyer searching "pump for corrosive chemical transfer 200L/min stainless" against a catalog that lists "centrifugal chemical pumps — SS316, 3000 LPH" will get the right result. Keyword search would miss it entirely.
The technology has matured significantly. The implementation challenge has shifted from "can we do this?" to "how do we connect the model to our catalog in a way that's accurate, maintained, and fast?"
At Elogic, we've found that the data quality work is consistently 40-60% of the total effort. Embedding a poorly structured catalog produces semantically fluent retrieval of garbage. The AI finds what you're looking for — in your bad data.
Where the ROI actually comes from
Paul Okhrem's framework for evaluating AI investments always starts with the P&L question, not the technology question. For AI search in B2B ecommerce, the numbers concentrate in three places:
Conversion at the search step. Buyers who find what they're looking for convert. Buyers who don't, leave or call. In B2B contexts where the sales cycle is long and the order value is significant, recovering even 5-8% of search-driven abandonment is a meaningful revenue number.
Support cost reduction. A significant portion of B2B customer support queries are essentially search failures — "I can't find the right part," "I need a compatible item for X," "do you carry Y?" AI search that handles these in self-service removes them from the support queue.
Catalog coverage visibility. Semantic search surfaces a secondary benefit that takes companies by surprise: it reveals the shape of buyer demand more accurately than keyword search did. When buyers can describe what they actually need, you learn what they actually need — including things your catalog doesn't carry. That's product intelligence.
The implementation decision Elogic recommends
Based on current client implementations, our recommendation for B2B ecommerce companies considering AI search:
Start with a catalog audit before any vendor evaluation. Understand what your data quality actually looks like at the attribute level. The vendor you choose matters less than the quality of what you feed them.
For platform selection, the choice between building on top of your existing search infrastructure (Elasticsearch, Solr) with embedding layers, versus migrating to purpose-built AI search platforms, depends primarily on your catalog size and update frequency. We can walk through the decision tree for your specific context.
For measurement, instrument the baseline before you switch. Know your current zero-results rate, search abandonment rate, and search-to-conversion rate. You'll want to show what changed.
A note from Paul Okhrem on the investment threshold
"The question I get from CEOs is always about the investment threshold — at what catalog size, at what revenue level, does AI search pay off? My answer is usually: it pays off earlier than you think, and the reason companies delay is that they underestimate the cost of the status quo. The silent abandonment is happening now. You just can't see it clearly."
Full frameworks for evaluating AI investment decisions in ecommerce are covered at paul-okhrem.com, including the AI Growth Readiness Audit that maps these decisions against your specific revenue baseline.
Elogic Commerce is a B2B ecommerce engineering firm founded by Paul Okhrem in 2009. We design, build, and optimize ecommerce platforms for distributors, manufacturers, and B2B brands. Reach out to discuss your AI search implementation.
Top comments (0)