When AI in B2B Ecommerce Creates Value (and When It's Just Marketing)

Artificial Intelligence is one of the most discussed topics in ecommerce. Many companies are experimenting with chatbots, assistants and recommendation systems, but moving AI from experimentation to production remains hard: Gartner reports that on average only 48% of AI projects make it into production (and getting there can take months).

*Source: Gartner press release, 7 May 2024*

In B2B contexts and technical ecommerce catalogs, the issue is rarely “lack of AI”. It’s AI that isn’t grounded in company data (catalog, PIM/ERP, manuals, policies, orders). A chatbot that “sounds smart” but doesn’t understand compatibility rules, equivalents, nomenclature, or commercial constraints creates frustration, not revenue.

AI creates value when it reduces friction in the processes that drive product search, selection and purchasing.

If you run a technical/B2B ecommerce, the question isn’t “should we add a chatbot?”

It’s: *“How do we provide reliable, semantic access to our data (catalog, manuals, policies, orders) at the moment users decide?”*

The challenge of technical ecommerce: unresolved complexity

A B2B ecommerce platform differs from B2C in key ways:

• thousands of SKUs and technical variants
• compatibility rules, certifications, regulations
• documentation spread across PDFs, datasheets, manuals
• ERP/PIM/CRM integrations (pricing, availability, customer terms)
• complex commercial logic (contracts, price lists, discounts, MOQ)

Why generic AI fails

Without controlled access to company data, AI tends to:

• generate plausible but technically incorrect answers
• ignore compatibility or regulatory constraints
• misalign with customer-specific pricing/stock/terms
• return suggestions that don’t match industrial intent

A concrete example

A buyer searches: *“gasket resistant to 200°C for DN50 PN16 flange”*.

A generic system returns keyword matches.

A system with semantic retrieval + generation (RAG):

• interprets technical requirements (temperature, size, pressure)
• filters the right attributes/materials
• surfaces relevant manuals/datasheets/certifications
• handles equivalents and alternatives
• aligns results with availability and commercial terms (when integrated)

The difference isn’t cosmetic. It’s operational.

Product search: the most underestimated bottleneck

In complex ecommerce, search often breaks because users query in different ways (part numbers, symptoms, compatibility, requirements) that don’t map cleanly to catalog taxonomy.

Keyword search struggles with technical language, synonyms and equivalents.

Baymard’s 2024 benchmark shows that many sites don’t properly support common search behaviors: 41% of sites fail to fully support 8 key types of search queries.

*Source: Baymard Institute, “Ecommerce Search Query Types”, 2024*

Keyword vs semantic (for technical catalogs)

Traditional keyword search

• returns literal matches
• misses synonyms, variants and equivalents
• performs poorly on requirement-based queries (temperature, standards, certification)

Semantic search with RAG

• interprets technical intent
• retrieves evidence from product data and documentation
• handles equivalents and attribute logic
• can respect constraints (when integrated with company systems)
• produces contextual, decision-ready results

AI + technical documentation: from hidden cost to competitive advantage

In B2B, a large share of value is buried in documents:

• manuals and guides
• compatibility sheets
• certifications and compliance
• policies (returns, warranty, shipping)
• internal support knowledge bases

When information exists but isn’t accessible at the point of decision:

• users abandon because they can’t validate technical fit
• support teams absorb repetitive questions (operational cost + slower buying)

RAG can turn documentation into an active asset: contextual answers grounded in source material and aligned with internal knowledge.

Beyond support: AI as an operational tool

AI produces ROI when embedded into processes (search, selection, pre-sales, post-sales) and when it can retrieve the right internal information.

High-impact areas:

1) Product search and selection

• less time to find the right item
• fewer compatibility mistakes

2) Buyer decision support

• compare technical alternatives
• guide requirements and constraints
• assist selection using documentation

3) Sales efficiency

• faster access to technical info during negotiations
• help with complex configurations/quotes
• fewer errors in proposals

4) Post-sales friction reduction

• guided troubleshooting
• install/maintenance answers
• fewer returns caused by wrong selection

Why many AI projects fail

The most common issue isn’t “the model” — it’s execution:

1) Unstructured or misaligned data

• inconsistent catalog attributes across systems
• documentation not indexed/versioned
• pricing and availability not synchronized

2) Lack of integration

• AI can “talk” but can’t verify stock/pricing/compatibility
• responses diverge from operational systems

3) UI-first thinking instead of process-first

• vanity metrics (conversations) over business KPIs
• no mapping of the real customer journey

4) Vague objectives

• “improve experience” without measurable targets
• unclear ROI and prioritization

A practical approach: process before technology

Successful implementation starts with three questions:

1) Where does friction happen?

• abandonment in technical categories
• searches with unusable results
• repetitive support requests (answers already exist)

2) Which data is available and trustworthy?

• catalog attributes and technical structure
• documentation and versioning
• ERP/PIM/CRM integrations
• search logs and support logs

3) Which goals are measurable?

• reduced search time
• higher conversion in complex categories
• fewer repetitive tickets
• fewer returns caused by wrong selection

When AI really makes sense (and when it doesn’t)

AI is effective when

• the catalog is large and technical
• users search by requirements/compatibility
• documentation is extensive and scattered
• support handles many repetitive questions
• complex configuration/compatibility rules exist

AI is not a priority when

• the catalog is small and navigation already works
• low decision complexity
• core data isn’t structured yet (start with data quality)

Conclusion

In B2B ecommerce and complex catalogs, AI isn’t a marketing badge. It becomes a growth lever when it reduces real friction and brings the right internal information to the moment of decision.

The right question isn’t *“do we need AI?”*

It’s *“which processes would improve with semantic access to our data?”*

References (sources)

1. Gartner (7 May 2024) — “Gartner Survey Finds Generative AI is Now the Most Frequently Deployed AI Solution in Organizations”

https://www.gartner.com/en/newsroom/press-releases/2024-05-07-gartner-survey-finds-generative-ai-is-now-the-most-frequently-deployed-ai-solution-in-organizations

2. McKinsey (5 Nov 2025) — “The State of AI: Global Survey 2025”

https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

3. Baymard Institute (12 Sept 2024) — “The 8 Most Common Search Query Types (… 41% of Sites Fail …)”

https://baymard.com/blog/ecommerce-search-query-types

4. Baymard Institute — “E-Commerce Search UX (Research Overview)”

https://baymard.com/research/ecommerce-search