AI best practices for B2B websites. What actually converts.

Generic LLMs and built-in chatbots (Drift, Intercom, Salesforce Einstein) are trained on internet-scale averages. They give plausible-sounding but wrong answers about your specific products. Best practice: feed the AI your actual catalog, spec sheets, compatibility matrices, and historical quotes. A custom-trained AI Sales Agent typically converts 5-15% versus a generic chatbot's 1.5%.

The lowest-leverage AI on a B2B website is a chatbot bolted next to a still-required contact form. The highest-leverage AI is one that REPLACES the form — conversational intake that extracts requirements, qualifies fit, and either routes to a rep or produces a quote. The 14-field form is the leak; AI intake is the fix.

Old-school configurators are decision-tree wizards ("Step 1 of 7: Choose material"). Modern AI configurators are conversational: the buyer describes what they're trying to do, and the AI proposes the right configuration, flags incompatibilities, and explains tradeoffs. The wizard makes engineers click 47 times; the co-pilot lets them paste a spec.

If your AI hands every lead to a rep with "interested in product X," you've moved the qualification bottleneck from the form to the rep's calendar. Best practice: the AI extracts budget range, timeline, decision authority, and use case IN THE CONVERSATION. The rep gets a qualified lead with context, not a name and email.

B2B buyers research at night, on weekends, between meetings. 73% of evaluation activity happens outside the window when a rep can answer the phone. AI on the website turns that traffic from "left a voicemail" into "qualified lead with a quote in their inbox." If your site is silent after 6pm, your AI strategy is failing the biggest opportunity.

B2B buyers (engineers, procurement, ops leaders) won't trust an AI that says "this is the right part." They will trust an AI that says "this is the right part because it matches the temperature range and pressure tolerance you specified, here's the spec sheet, here's a similar customer use case." Show the work.

If you sell through a channel, the AI has to know the routing rules — by region, by product line, by deal size, by industry vertical. Best practice: the AI qualifies the lead, then routes to the right destination (distributor inbox, manufacturer rep, internal sales) with full context. Don't make a human re-route what the AI already knows.

A 5-second wait for a chatbot response feels like 30 seconds when an engineer is evaluating you against three competitors. Stream tokens as they generate, show typing indicators, return citations as the AI finds them. The perceived speed difference between streaming and non-streaming is the difference between "this is fast" and "this is broken."

Use frontier models (Sonnet 4.6, GPT-5) for catalog reasoning and conversation; use faster cheaper models (Haiku 4.5) for classification, routing, and lead scoring. The hybrid pattern produces near-frontier quality at one-tenth the cost. Don't rebuild every time a new model ships — design the abstraction so the model is swappable.

Most AI chatbot vendors report "messages sent," "average conversation length," "satisfaction score." The only metric that matters for B2B website AI is qualified leads / quote requests / RFQs out the door. If the AI is chatty but the pipeline isn't growing, the AI is theater. Measure what closes, not what engages.