70% of B2B buyers say they cannot find relevant products on supplier websites, according to industry research.
The global B2B ecommerce market is projected to reach tens of trillions of dollars by 2030 in recent forecasts.
A large share of B2B buyers now use generative AI as often as traditional search when evaluating potential suppliers.
Scenario 1

The assistant detects whether a message is a part number lookup, specification search, RFQ, or status request. Buyers can speak in natural language, using SKUs, application details, or problem statements.
The bot calls ERP, PIM, and catalog services to pull SKUs, specifications, pricing, and inventory in real time. This prevents hallucinated part numbers or outdated prices that do not match ERP.
The assistant applies contracts, taxes, credit limits, and approvals tied to each account. It can add items to the cart, start RFQs, create quotes, or log service cases, while writing back into ERP.

In recent Epicor CPQ and Magento projects, quote workflows became significantly faster compared to email based processes.
Early pilots often show a meaningful shift toward self service quoting and reordering when chat handles real tasks end to end.
AI assisted sessions typically earn higher satisfaction scores when answers are grounded in ERP and PIM data instead of scripts.

Can it handle both exact SKUs and specification based, application level queries reliably?
Does it read contract pricing, discounts, taxes, and credit directly from ERP in real time?
How does it limit hallucinations for specifications, availability, and pricing in your catalog?
Can it resolve superseded parts and OEM or competitor cross references to current, orderable SKUs?
Does it support multi warehouse stock, realistic lead times, and split shipments?
How are handoffs to human agents designed and how much context passes automatically?
What does a realistic four to eight week pilot and three to six month rollout look like in your stack?
How will the enterprise AI chatbot solution for ecommerce keep learning from failed queries and new product lines?