AI for RFP responses

Loopio's 2026 RFP Response Trends & Benchmarks report surveyed 1,533 proposal and sales professionals, mostly outside construction. Its headline numbers:

• 79% of teams used generative AI in their response process in 2025, up 10 points from the year before.
• Teams spent an average of 33 hours per RFP, two hours less than the prior year.
• The average win rate fell to 39% from 45%, as teams submitted more RFPs and were less selective.

The takeaway: AI is cutting writing time, but it doesn't fix bid/no-bid discipline. Submitting more responses with AI didn't raise win rates in that sample. Construction RFPs (design-build, CM at-risk, best-value) carry more technical and pricing content than typical software RFPs, so treat these as context, not as construction norms.

1. Ingest the RFP documents and addenda.
2. Extract requirements, deadlines, forms and evaluation criteria. A person confirms the list against the RFP.
3. Decide go or no-go before drafting starts.
4. Assign an owner to each section.
5. Draft from approved answers, with each claim linked to its library entry.
6. Review: experts approve technical claims and a manager approves the price.
7. Submit after a final compliance check against the confirmed requirement list.

NIST's Generative AI Profile (AI 600-1) calls the core risk confabulation: "confidently stated but erroneous or false content." In an RFP, that looks like:

• Invented facts: certifications, EMR figures, bonding capacity or project experience you don't have.
• Outdated content: reused answers that name old products, prices or staff.
• Missed requirements: extraction skipping something buried in an appendix or an addendum.
• Confidentiality: some RFPs restrict how their content may be processed or shared.

Each risk needs a control: source every claim from an approved library, put review dates on reused answers, reconcile the requirement list against the RFP by hand, and follow the client's confidentiality terms.

AI drafting is only as good as the material it draws from. Before automating, build a library of approved answers: company overview, capabilities, project descriptions, security and compliance statements, staff resumes and standard terms. Give each entry an owner and a review date. When a draft cites a project or a certification, it should come from this library, not from the model's general knowledge.

Tag entries by market, service and client type so drafts start from the most relevant material. Retire answers that reference discontinued offerings, former staff or expired certifications. A smaller, current library produces better drafts than a large, stale one.

• Sealed low-bid work (ITBs): award goes on price and responsiveness, so there's little narrative to draft. The value is in takeoff and the forms checklist.
• One-page quotes to a GC: a scope sheet and price matter more than prose.
• Solicitations that prohibit third-party processing of their documents.