RFQ / proposal cycle

Current pain

Sales repeatedly chases engineering, costing and approvals while requirements sit across attachments and earlier bids.

Business impact

Slow responses consume selling time, delay pipeline conversion and increase the risk of omitted requirements or weak pricing discipline.

Redesigned Human + AI workflow

1. AI extracts requirements and links each one to the source document and revision.
2. AI retrieves approved reference answers and drafts a compliance matrix, assumptions and proposal.
3. Engineering resolves deviations. Commercial owners approve price, margin, terms and commitments.
4. The bid owner issues the approved proposal and captures win/loss learning.

Change and adoption

Create one approved answer library, name content owners and set review deadlines. Train reviewers to check citations and omissions. Never allow AI to commit pricing or contractual terms without approval.

Systems and data

CRM opportunity; RFQ attachments and revisions; approved technical library; costing/CPQ records; price lists and approval rules; proposal versions and win/loss reasons. Separate customer-confidential material by access rights.

KPIs with clear definitions

• Proposal cycle time = elapsed business hours from complete RFQ intake to approved issue, reported as median and P90.
• First-pass completeness = proposals accepted internally without material rework / proposals reviewed.
• Review effort = reviewer hours per issued proposal.
• Margin discipline = proposals within approved margin policy / issued proposals.

Measurable outcomes and verification

Compare similar RFQs by complexity and product family. Measure cycle time, rework and reviewer effort first. Assess win rate over a full sales cycle with sample size disclosed. Count only approved incremental contribution margin in a value case.