How to use AI to create a virtual review board

Why Most RFP Review Processes Fall Short

According to Loopio’s 2026 RFP Trends Report, generative AI adoption among proposal teams has doubled in just one year, rising from 34% to 68%. Yet most organizations are still using AI only to draft faster, not to review smarter. That is a missed opportunity.

The average proposal team reviews their RFP response the same way: a small internal group reads through the draft, offers feedback from their own perspective, and signs off. The problem is that your internal team already agrees with you. They share your assumptions, your vocabulary, your blind spots.

What you actually need is the outside perspective of the people sitting on the other side of the table — the procurement officer who has read 40 proposals this quarter, the technical evaluator who will immediately spot a hand-wavy architecture section, the skeptical finance lead who wants to see assumptions behind every number.

Those people are not in your conference room. But with the right AI proposal review setup, we brought them in anyway.

Related reading: How We Build AI Workflows for Client Engagements

How We Built the Virtual Review Board

For a recent competitive bid, we structured a virtual proposal color team review using five AI reviewer personas. Each was modeled after a realistic stakeholder archetype based on the issuing organization’s industry, structure, and the evaluation criteria outlined in the RFP itself.

This is distinct from simply asking an AI to “review this proposal.” What we built was closer to a structured red team review for proposals, where each persona had a defined role, a scoring mandate, and specific questions to answer.

The five-persona panel included:

• The Procurement Lead — focused on compliance, completeness, and whether every stated requirement was explicitly addressed
• The Technical Evaluator — looking for methodology gaps, specificity, and claims that lacked supporting evidence
• The Finance Reviewer — stress-testing the cost model, questioning assumptions, and flagging anything that read as a hidden cost or an underestimate
• The Executive Sponsor — evaluating strategic alignment and whether the approach tied back to the client’s stated organizational goals
• The Skeptic — a deliberate devil’s advocate whose only job was to surface reasons to score the proposal lower and identify objections we had not addressed

We fed each persona the full RFP document, the evaluation rubric, and our draft proposal. Each was then asked to score the submission against the rubric, identify the three sections most likely to cost us evaluation points, and write out the questions they would raise in a live review discussion.

The feedback was uncomfortable in the best possible way.

See how we apply this: Our AI Programs and Services

What the AI Proposal Review Uncovered

Running a structured AI review of our RFP response before submission revealed issues our internal team had completely missed.

The Technical Evaluator flagged that our implementation timeline, while detailed, never acknowledged the client’s existing legacy infrastructure — something mentioned twice in the RFP background section. We had overlooked it entirely.

The Finance Reviewer noted that our pricing narrative used confident language but never explicitly defined what was out of scope. In a competitive proposal evaluation, undefined scope reads as risk to reviewers.

The Skeptic pointed out that our differentiators section described things we did well without once explaining why those capabilities mattered to this specific client’s stated problem. It was a capability list dressed up as a value proposition.

None of our internal reviewers caught any of these. Not because they were not thorough, but because they already believed in our approach. The virtual board did not.

We revised the proposal around every major finding. The timeline section was rewritten to explicitly address legacy system integration. We added a clear scope boundary table to the pricing section. The differentiators section was rebuilt around the client’s documented pain points, not our strengths in the abstract.

The Outcome

We won the contract.

We cannot attribute the win entirely to the AI-powered proposal review, since competitive RFP responses are complex and rarely come down to one factor. But the client’s debrief was telling. Evaluators specifically noted that our proposal “directly addressed implementation risk” and showed “a clear understanding of our existing environment.” Those were precisely the areas the virtual board had flagged as weak and that we had gone back to strengthen.