Agentic AI for industrial distributors: turning a 3-day quote into 30 minutes
In industrial distribution, the firm that quotes first often wins — yet a single RFQ can take days because the answer is scattered across ERP, pricing, and supplier systems. This is exactly the messy, multi-step work agentic AI is built for.
Why the quote takes three days
In industrial distribution and made-to-order manufacturing, the company that quotes first usually wins. Yet a single request for quote rarely has a single answer waiting. A sales engineer has to read the RFQ, identify each line item and spec, check stock and lead times in the ERP, apply the customer’s contract or volume pricing, and — for anything non-standard — chase a supplier for cost and availability. That answer is scattered across an ERP, spreadsheets, email threads, and supplier portals. So a quote that is technically simple still takes days, and deals leak to whoever responds first.
This is an agent problem, not a chatbot problem
It is tempting to bolt a chatbot onto this, but a quote is not one fixed transformation. The steps depend on the request: is the item in stock, is it a standard SKU or a custom build, which pricing tier applies, does a supplier need to be involved? That branching, “decide what to do next” quality is exactly where an agentic system earns its place — an LLM acting as the decision-maker that orchestrates tools, rather than a single step that rewrites text.
What the agent actually does
- Reads the incoming RFQ (email or PDF) and extracts line items, quantities, and specifications.
- Queries the ERP and inventory for stock levels and lead times on each item.
- Applies the right pricing — the customer’s contract rates and volume tiers, not list price.
- For out-of-stock or custom items, pulls supplier data or drafts a supplier RFQ automatically.
- Assembles a draft quote, with its reasoning and a confidence flag on anything uncertain.
The human stays on the exceptions
The agent does not send the quote. It produces a draft plus the evidence behind every number, and a sales engineer approves or adjusts it. In a business where a mispriced line is expensive, that review gate is what makes the system trustworthy. The engineer’s time shifts from lookups to the judgement calls and complex deals that actually need a human.
The honest version of the ROI
The payoff is real — faster response lifts win rates, pricing gets more consistent, and your best people stop doing data entry. But it depends on groundwork: reasonably clean ERP and pricing data, and the agent having permission to read those systems. The pragmatic path is to automate the standard 80% of RFQs first and leave the genuinely bespoke ones to people. That is where we start when we design these systems — mapping which decisions the model should make, and which it should hand back.