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Delays in quoting are also a commercial problem
In industrial B2B, a request for quote may arrive with technical annexes, tables, commercial conditions, old references, and requirements scattered across email and PDF. The first cost is operational: reading hours, clarifications, and re-entry. The second is commercial: if the response takes too long, the customer has already moved forward with another supplier.
Many organizations accept this bottleneck as inevitable because the RFQ is 'complex.' But complexity should not mean permanent disorder. Much of the time is lost not in deciding price, but in reconstructing what is actually being requested.
What changes with intelligent RFQ processing
AI adds value in the decomposition and preparation phase. It reads tenders, annexes, and emails; extracts requirements; structures line items; detects inconsistencies; and gives the team a much cleaner base to validate availability, engineering, and pricing.
- Document intake — It turns heterogeneous documents into one unified, queryable file.
- Structured extraction — It identifies quantities, specifications, conditions, deadlines, and exclusions with source references.
- Quote preparation — It generates an initial proposal or a missing-information checklist so the team intervenes on an ordered base.
The anti-pattern: automating the offer without controlling exceptions
Poorly governed automated quoting can look like efficiency and end up eroding margin. If the system does not distinguish between standard products, variants, plant constraints, or atypical commercial terms, the risk is not only being wrong: it is being wrong quickly.
That is why useful automation is not about promising 'quotes without humans,' but about separating what is repeatable from what is exceptional. Engineering, pricing, and operational feasibility still need explicit control in the cases where a bad interpretation is expensive.
How to start in practice
It makes sense to start with an RFQ family where there is enough repetition to learn and enough friction for the return to be visible. For example: spare parts, standard components with variants, or recurring internal tenders in one business line.
From there, the goal is not to close the full loop in one sprint. It is to reduce preparation time, improve consistency across sales teams, and leave evidence of where exceptions or ambiguities still appear.
Diagnose, audit, MVP, and scale
The sensible pattern is to first diagnose how RFQs enter today, how long they take to move from email to a useful proposal, and where they get stuck; then audit templates, catalogs, commercial rules, and document sources; then launch an MVP on one concrete RFQ type; and scale when extraction rate, time saved, and pre-quote quality are consistent.
That approach prevents AI from becoming another layer of complexity. The goal is not to impress with a demo, but to answer faster with more control and less wear on the technical-commercial team.
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