AI Personalization in B2B eCommerce: A Complete Framework for Modern Digital Commerce

Traditional search engines are very literal. They match keywords in a query to keywords in titles, descriptions, or attributes. That can work in a tidy B2C catalog. In B2B, where queries are more technical and data is messier, it often breaks down.

Intent-based and semantic search change the game:

• They interpret the meaning behind a query, not just the exact words.
• They map natural language to attributes, categories, and compatible products.
• They can personalise ranking based on account, role, and past behaviour

Imagine a buyer typing: “410 stainless steel hex bolts for high-temperature exhaust system” rather than a precise part number. An AI search layer can recognise material, grade, form factor, and application, then:

• Map that intent into the correct category and attributes.
• Filter to products that match those constraints.
• Rank results based on relevance, availability, and previous purchases.

💡 A good starting point is to pick one or two high-value query types—like technical part searches or long natural-language questions—and make sure AI search handles those beautifully before you widen the scope.

Virto offers AI-powered intent search (including through integrations) that identifies what the user is really looking for and maps their query to the most relevant product facets and attributes, which significantly improves discovery in complex B2B catalogs. This is one of the core “AI features for product content, categorization, and localization” that sit on top of its structured data model.

Recommendations in B2C tend to focus on simple patterns: “people also bought”, “similar items”, or “complete the look”. In B2B, useful recommendations have to respect far more context.

Common B2B patterns include:

• Compatibility – components that are certified to work together.
• Alternatives and substitutions – when a part is obsolete or out of stock.
• Bundles and kits – pre-configured sets for a known job or project.
• Replenishment – prompts based on usage or contract schedules.
• Account patterns – what similar customers in the same industry buy.

For example, if a specific bearing is out of stock, a good recommendation model will:

• Suggest compatible alternatives that meet the same technical specs.
• Respect the account’s eligible assortment and pricing.
• Take into account lead times and any regulatory flags.

Virto’s xRecommend module is built around this B2B logic of compatibility, substitutions, and replenishment, so recommendation scenarios stay aligned with real-world constraints instead of just visual similarity.

Pricing is a sensitive area in B2B. The aim is not to let AI “experiment” with random price levels, but to allow it to optimise within clearly defined contractual boundaries.

Typical use cases include:

• Suggesting discounts within allowed ranges for specific segments or basket sizes.
• Highlighting price breaks (“add three more units to reach your next tier”).
• Identifying where small adjustments are likely to have the biggest impact on conversion or margin.

In practice, AI might:

• Analyse historical orders and win/loss data.
• Flag SKUs where discount patterns could be tightened without hurting sales.
• Propose targeted offers for segments with high churn risk or low engagement.

Contract pricing and customer-specific price lists in Virto Commerce form the guardrails for any AI pricing personalization. AI can propose optimisations and prompts, but the underlying rules stay in charge.

Once you know what each account is allowed to buy, AI can help decide which products to highlight, in which order, and in which context.

That might look like:

• Ranking products within a category based on the account’s industry, role, and past behaviour.
• Highlighting “top picks” or “frequently purchased together” items that are actually relevant for that buyer.
• De-emphasising or hiding items that are technically available but rarely relevant.

For example, an automotive OEM buyer might see:

• Only SKUs that are part of their contract and compliant with their standards.
• Within that set, AI-lifted suggestions based on other automotive OEM buyers’ behaviour and their own history.
• Complementary items to complete a typical job bundle.

Account-specific catalogs in Virto Commerce allow assortment personalization at the customer or organisation level. AI can then work inside that frame, rather than guessing from a global catalog.

Segmentation is where AI helps you see patterns you might not spot manually.

Instead of only relying on static fields like “industry” or “country”, AI can cluster accounts and users based on:

• Behaviour (browse patterns, search terms, repeat orders).
• Value and margin.
• Engagement with content or specific product lines.
• Risk indicators like declining order frequency.

That leads to practical segments such as:

• High-value but low-engagement accounts.
• “Churn risk” accounts whose activity has dropped.
• Emerging segments that are buying newer product lines.

💡 Personalization that relies on customer profiles is only sustainable if you can show where data came from, why you’re using it, and that you’re respecting local privacy rules.

You can then tailor experiences and campaigns: for example, give the “churn risk” cluster clearer navigation, more helpful prompts, or proactive offers to remove friction, rather than blanketing everyone with the same banners.

Finally, AI can influence what the user actually sees and does when they log in.

Because you understand the account model, roles, and workflows, AI can help:

• Personalise dashboards and landing pages by role.
• Decide when to suggest “add to cart” vs “request a quote”.
• Surface reorder templates or lists at the right moment.
• Prioritise approvals for approvers who handle many requests.

For example:

• A buyer might see a home page full of reorder suggestions, project-based lists, and quick-add grids.
• An approver might see a concise list of pending approvals with AI-generated summaries of impact on budget or stock.
• An engineer might see technical documentation, compatibility checks, and RFQ shortcuts.

Virto Commerce’s corporate accounts and workflow capabilities provide the structure for these personalised journeys, while AI helps decide which tasks and prompts to surface first for each user.

AI personalization in B2B is still early, but several trends are already visible.

The first big shift is in catalog operations themselves. Instead of writing descriptions, categorising SKUs, and localising content entirely by hand, more teams are adopting AI-assisted flows to handle the bulk work so humans can focus on supervision.

Analysts expect this to compound. McKinsey’s work on personalization has shown that companies that effectively apply data and personalization can lift revenues by 5–15% and improve marketing ROI by 10–30%, which creates strong pressure to scale content and catalog readiness without linear headcount growth. It is hard to reach that level of sophistication if every attribute and translation is written manually.

Over time, “enrich → categorise → localize” is likely to become a mostly automated pipeline with human checkpoints, not a series of separate manual tasks.

The second trend is the spread of account-based personalization across the entire experience, not just in the recommendation block.

B2B buyers increasingly expect suppliers to understand their context and make interactions faster, whether they are researching, ordering, or getting support. Recent Deloitte research on digital customer experience in industrial manufacturing found that 98% of manufacturers have now started their digital transformation journeys, driven in part by the need to improve customer experience and support more convenient, personalised interactions

The direction of travel is clear: search results, category views, pricing, assortments, and even workflows will all become account-aware by default. For many organisations, this means shifting from anonymous “web shop” thinking to something much closer to a personalised service portal for each account.

The third trend is a move from reactive to proactive buying support.

AI models trained on order history, seasonality, and external signals can help:

• Predict when a given account is likely to need to reorder.
• Flag where lead-time risk is rising and propose substitutions.
• Suggest optimal order timing and quantities to smooth demand.

As more B2B buyers expect suppliers to act like partners rather than passive storefronts, predictive procurement support becomes a differentiator. External research on AI in ecommerce indicates that AI-enabled commerce, including predictive and personalised capabilities, is set to grow rapidly, with AI-enabled ecommerce projected to reach roughly $8–9 billion in the near term and expand at over 20% compound annual growth in the coming decade.

💡 The most useful predictive prompts feel like a smart assistant for planners and buyers: they surface timing and risk insights but still leave humans firmly in control of the final decision.

In that context, “personalization” and “supply assurance” increasingly blend together.

Finally, we are starting to see early forms of “agentic” assistants in commerce: AI agents that can understand goals, take actions, and coordinate sequences of steps on behalf of buyers or sellers.

Forecasts suggest that by 2030, nearly half of online shoppers in some markets could be using AI-powered shopping agents, with a significant impact on ecommerce volumes. While most examples today are in B2C, the pattern is directly relevant for B2B: guided buying for complex equipment, project-based carts built from a short description, and assistants that can navigate contracts, assortments, and technical constraints.

Composable platforms like Virto Commerce are gradually evolving toward this AI-assisted commerce model, where catalog operations, discovery, pricing, and procurement workflows are increasingly automated while still anchored in robust B2B rules.

Tools such as Virto’s Oz AI assistant are early examples of this shift, helping buyers and internal teams navigate catalogs and workflows through natural, context-aware conversations rather than just clicks.

To make this more concrete, here are three anonymised AI personalization ecommerce examples based on published patterns and common B2B use cases.

A multi-vendor B2B marketplace relies on supplier feeds. Product data is inconsistent, descriptions are thin, and categorisation is messy. Buyers struggle to find what they need, and conversion from search to order is weak. Similar issues are common across B2B marketplaces and platforms.

Foundation set: The operator defines a unified taxonomy, key attributes per category, and minimum content standards. Supplier feeds are normalised into a PIM-like layer.

AI layer: They introduce AI-generated descriptions to fill gaps, AI-assisted categorisation to align SKUs to the unified taxonomy, and incremental semantic search on top of this cleaner data.

Outcomes: The marketplace sees a higher share of SKUs with complete attributes and a noticeable uptick in search-to-order conversion and filter usage. Content teams spend more time supervising and less time typing, which helps them keep up as new suppliers join.

This pattern is common among Virto Commerce marketplace customers, where AI content and categorisation capabilities help stabilise product data before more advanced personalization is rolled out.

An industrial distributor expands into several new regions. Local teams need product content and documentation in multiple languages, but manual translation is slow and inconsistent. Time-to-market for new locales is measured in months.

Foundation set: The distributor consolidates a global catalog with a clear attribute model and defines which products are in scope for each region. Assortments and pricing per region are modelled in the commerce platform.

AI layer: AI localization tools generate first-pass translations for product descriptions, attributes, and key UI elements. Local specialists review terminology and make corrections where needed, rather than writing from scratch.

Outcomes: New regions can go live significantly faster, local teams feel more ownership over the catalog, and buyers see a more coherent experience in their own language. This type of AI-assisted localisation is becoming a standard expectation in multilingual ecommerce, not a luxury. 

A large enterprise customer portal serves thousands of users across dozens of business units. Every account has contract-specific pricing and assortments. Buyers and approvers constantly ask sales to confirm eligibility and prices.

Foundation set: The supplier invests in a clear account model, including organisational hierarchies and roles, and moves all contract pricing and assortments into the ecommerce and OMS stack.

AI layer: AI search becomes contract-aware, showing only eligible SKUs. Recommendations focus on compatible parts and replenishment within contract assortments. Simple AI-driven prompts highlight price breaks or more efficient bundles.

Outcomes: Support tickets about “what can I order and at what price?” decrease, buyers rely more on self-service, and average order value and repeat order rates move in a healthier direction. 

Virto Commerce’s contract pricing and customer-specific catalogs are designed for exactly this kind of scenario, where AI personalization has to stay firmly inside pre-agreed commercial rules.

You do not need to do everything at once. A staged approach usually works better and feels less risky.

Start by improving product content, attributes, and categorisation in the parts of your catalog that matter most. Use AI to generate or enrich descriptions, suggest category mappings, and accelerate localisation, but keep humans in the loop.

Make sure your account model, pricing rules, assortments, workflows, and customer profiles are correctly represented in your ecommerce and order management systems, rather than scattered across spreadsheets and side systems.

💡 It’s also worth remembering that the way you use both customer and company data is constrained by law and sector regulations. Implementing AI on top of behavioural and profile data carries risks not just for reputation and customer trust if something goes wrong, but also for legal and compliance exposure if you ignore consent, retention, or data-minimisation requirements. Treat governance and compliance as part of the foundation, not an afterthought.

Once data is stable in a few segments, pilot AI-powered, intent-based search there. Watch metrics like “no results” rate, search-to-order conversion, and support tickets about “can’t find X”.

Extend pilots with AI-driven recommendations focused on B2B patterns: compatibility, substitutions, replenishment, and bundles, always inside allowed assortments and contract pricing.

Finally, use AI to refine pricing prompts within guardrails, personalise assortments for each account, build behavioural segments, and adapt UX for different roles and journeys.

Many of these steps can be implemented incrementally using Virto Commerce AI capabilities for B2B catalogs and personalization. Once your foundation is ready, you can explore these capabilities and use it alongside the ideas in this framework rather than treating AI as a one-off experiment.

If you want a broader view of where AI is headed across digital commerce, beyond personalization alone, you can also look at our deep dive into AI in ecommerce as a companion piece.

In the end, AI personalization in B2B ecommerce is less about clever models and more about getting the basics right—clean catalog data, a solid account and pricing model, clear assortments, and governed customer profiles. Once that foundation is in place, AI becomes an accelerator: better search, smarter recommendations, more helpful pricing prompts, and journeys that adapt to each role and account without breaking contracts or compliance.

If you’d like to see how this looks in practice, you can explore the full AI feature set in Virto Commerce—from product content enrichment and categorization to intent-based search and B2B-aware recommendations—or get in touch with our team or schedule a demo call to talk through how these capabilities could fit your own roadmap.