AI is worth testing for first-pass RFP work when the team can give it a current answer library and a clear review owner. Start with one real bid pack. Compare the time from intake to approved submission, including corrections and sign-off. A fast first draft is not a business result if the team then has to verify or rewrite every claim. Teams with a maintained answer library can test drafting; teams starting from scattered files should first test whether they can find and approve the right material.
What a practitioner discussion actually describes
In a public consulting discussion, one practitioner reports spending most of their time writing RFP proposals and using ChatGPT occasionally for paragraphs or parts of a document. A commenter reports that their company is experimenting with an agent trained on previous proposals, but says it is not an end-to-end solution. These are self-reported experiences from one discussion. They do not establish a typical saving, accuracy rate or return on investment. [1]
Choose the work before choosing the tool
| Work | AI trial | Human decision |
|---|---|---|
| Requirements | Extract questions and build a checklist from the bid pack | Confirm completeness and deadlines |
| Reusable answers | Draft from approved, current company material | Check fit and remove outdated claims |
| Evidence and promises | Flag gaps and suggest where proof is needed | Select case studies, verify numbers and approve commitments |
Run one test that counts review time
- Pick a real bid pack and keep the same approved source material for both versions.
- Record the manual path from intake to approved submission.
- Use AI for the checklist and first-pass answers, then record every correction and review step.
- Compare total elapsed time, accepted reuse and factual or compliance errors after sign-off.
- If the AI path improves the full process, scope a repeatable implementation with named owners and acceptance criteria.
Our analysis: prepare the inputs before the pilot
A proposal assistant can only produce a dependable first pass if it has a dependable source of answers. Gather one current bid pack, the approved answer library, relevant service descriptions, permitted case studies and the latest pricing or commercial rules. Name a person who can say which version is authoritative. Separate material that may be used in an external proposal from internal notes and confidential client information. If those boundaries are unclear, settle them before putting documents into any tool.
The team also needs an escalation rule. A request for a factual capability can be matched to approved material; a request for a new service commitment, delivery date or security assurance should go to its owner. Write those rules down in plain language so a reviewer can distinguish an answer that needs editing from a promise the business has never approved.
Illustrative example: what a useful first pass looks like
Imagine a bid asking how your company handles onboarding, support, information security and a requested integration. An AI-assisted first pass could turn the bid into a checklist, point the onboarding answer to an approved process document, and flag that the integration is not covered by the answer library. The human proposal owner would then check the cited process, ask the technical lead about the integration, and approve the final wording. This is an illustrative workflow, not a report of a Feazible customer or a measured result.
A weak first pass would confidently invent an integration, reuse a retired case study or hide the source of a security statement. Those failures matter even if the draft arrives quickly. The pilot should make gaps visible, rather than reward fluent prose for concealing them.
Our analysis: measure the approved answer, not the generated draft
| Measure | What to record | Why it matters |
|---|---|---|
| Time to approved submission | Start at intake; stop after final review and sign-off | Captures editing and review, not only drafting |
| Accepted reuse | Mark answers accepted, revised or replaced | Shows which source material is actually reusable |
| Corrections | Log factual, commercial and compliance changes | Reveals hidden verification work |
| Missing requirements | Check the final response against the bid pack | Prevents a polished answer from skipping questions |
| Reviewer effort | Record who reviewed each answer and for how long | Shows whether work moved rather than disappeared |
Use the same source pack and the same quality bar for both paths. A manual baseline from an unrelated, easier bid can make the assisted run look better than it is. If a parallel test is impractical, compare similar questions inside one bid and record the limitation. Decide in advance what outcome would justify continuing: fewer total hours without more errors, better requirement coverage, or faster retrieval of approved material may each matter, depending on the team. Do not convert a single pilot into a promised return on investment.
Our analysis: when to pause the build
Pause if answers change frequently and no one maintains the library; if proposals depend mainly on one-off strategic judgment; if reviewers cannot trace a statement back to approved material; or if the tool would expose sensitive bid information outside an agreed environment. A process that produces drafts faster but increases corrections or makes sign-off harder has not solved the buying problem. In that case, fixing source ownership and review workflow may be more valuable than buying automation.
Our analysis: scope specialist help around the bottleneck
If the pilot shows a repeatable opportunity, ask a specialist to define the document intake, source retrieval, draft traceability, exception handling and approval steps. Ask for a demonstration on your own bid pack, not a generic proposal. Agree what counts as an acceptable answer, how unsupported questions are flagged, who can update the library and what happens when a source is superseded. The deliverable should include a way to inspect and correct the system, not merely a prompt that produces polished text.
Feazible’s AI Opportunity Audit can help identify whether this is a worthwhile opportunity and where the trade-offs sit. Bring a recent bid pack, examples of approved answers and a rough map of the current approval process. That gives an audit something concrete to evaluate. Any implementation would need its own scope after the opportunity is assessed.