12 Days of AI Use Cases Day 6: RFP Requirements

Welcome to the 12 Days of AI Use Cases, 2025 Edition! Today: RFP Requirements.

In this series, we’ll be looking at different use cases for AI – in particular, generative AI and large language models, the software that powers tools like ChatGPT, Google Gemini, and Anthropic Claude. Each day, we’ll look at the use case through the lens of the Trust Insights 5P Framework to see the role AI plays in achieving real, tangible outcomes.

We designed these use cases not only for reading but also as context you provide to generative AI tools as part of a prompt to help you achieve the outcomes you’re after. Ask the generative AI tool of your choice to help you implement this use case and copy/paste it in as part of the Trust Insights RACE AI prompt framework – this goes in the Context portion of our prompt framework.

Let’s dig in!

Purpose

RFP (Request for Proposal) processes consume significant time and resources for both issuers and respondents, often resulting in preventable errors and missed opportunities. We use generative AI to systematically extract, analyze, and verify RFP requirements on the respondent side, ensuring comprehensive compliance checking throughout the response development process. On the issuer side, we deploy AI with structured scoring rubrics to evaluate dozens or hundreds of RFP responses rapidly and objectively. This use case increases accuracy, reduces manual review time, and improves vendor selection outcomes by ensuring responses align precisely with stated requirements.

People

RFP response teams—including proposal managers, technical writers, subject matter experts, and compliance reviewers—use AI to extract and track every requirement from issued RFPs. These professionals benefit from automated requirement checklists that enable continuous verification as they develop responses. On the issuer side, procurement teams, evaluation committees, and department leaders use AI to score and rank incoming proposals systematically.

Internal stakeholders across both organizations gain visibility into the RFP process through structured requirement tracking and scoring frameworks. Finance teams, legal departments, and executive leadership receive clear compliance documentation that supports decision-making. Project managers and department heads access consistent evaluation criteria that reduce subjective bias in vendor selection.

External audiences include vendors responding to RFPs, who benefit from clearer requirement documentation and more consistent evaluation processes. Clients and partners experience improved vendor selection outcomes, leading to better service delivery and stronger working relationships. The broader business ecosystem benefits from more efficient procurement cycles and reduced time-to-contract execution.

Process

1. RFP Respondent Workflow: Upload the complete RFP document to a grounded AI tool (such as Google NotebookLM, Anthropic Claude with Projects, or OpenAI ChatGPT with file upload) that prevents hallucination by restricting responses to provided source material only.
2. Instruct the AI to extract and categorize all explicit requirements, deliverables, evaluation criteria, submission guidelines, formatting specifications, and deadline information into a structured checklist format.
3. Review the AI-generated requirement checklist with the proposal team to verify completeness and clarify any ambiguous items before beginning response development.
4. As the response team drafts each section, reference the AI-extracted checklist to ensure every requirement receives appropriate coverage in the response document.
5. Upload draft response sections to the AI tool and instruct it to perform gap analysis by comparing the draft content against the original requirement checklist.
6. Iterate on response sections based on the AI-identified gaps, adding missing elements and strengthening areas where compliance appears weak or incomplete.
7. Conduct final compliance verification by uploading the complete response document and requesting the AI to cross-reference every checklist item against the final submission.
8. RFP Issuer Workflow: Develop a detailed scoring rubric that defines evaluation criteria, point values, required elements, and minimum compliance thresholds before receiving any vendor responses.
9. Upload the scoring rubric and original RFP document to the AI tool to establish the evaluation framework and ensure the rubric aligns with stated requirements.
10. As vendor responses arrive, batch-upload response documents to the AI tool and instruct it to score each submission according to the predefined rubric.
11. Export the AI-generated scores into a spreadsheet or evaluation matrix that ranks responses from highest to lowest compliance and qualification scores.
12. Review the top-scoring responses manually to validate AI scoring accuracy, assess qualitative factors, and select the shortlist of vendors for deeper evaluation or interviews.
13. Document the evaluation process and AI-assisted scoring methodology to ensure transparency and auditability in vendor selection decisions.

Platform

• Grounded AI Tools: Google NotebookLM, Anthropic Claude with Projects, or OpenAI ChatGPT (with file upload capabilities) configured to prevent hallucination by restricting responses to uploaded source documents
• Specialized RFP Software: Platforms such as Joist.ai, OpenAsset, or similar RFP management solutions that integrate AI-powered requirement extraction and response evaluation
• Document Management Systems: Cloud storage platforms (Google Drive, Microsoft OneDrive, Dropbox) for organizing RFP source documents, requirement checklists, draft responses, and evaluation rubrics
• Spreadsheet Software: Microsoft Excel or Google Sheets for tracking requirement compliance, scoring vendor responses, and building shortlist comparison matrices
• Required Data: Complete RFP documents in machine-readable formats (PDF, Word, or plain text), detailed scoring rubrics with defined evaluation criteria, vendor response documents, and any supplementary materials or clarifications issued during the RFP process
• Collaboration Tools: Project management platforms (Asana, Monday.com, Microsoft Project) for tracking RFP response development progress and coordinating team assignments across requirement categories
• Version Control Systems: Document versioning capabilities to track changes in response drafts and maintain audit trails of compliance verification iterations

Performance

Organizations implementing AI-assisted RFP requirement management reduce response development time by systematically tracking compliance throughout the drafting process rather than discovering gaps during final review. Respondents increase their win rates by ensuring comprehensive requirement coverage, while issuers accelerate vendor selection timelines by automating initial scoring and shortlist development. Both parties benefit from reduced preventable errors, more objective evaluation processes, and clearer documentation of decision-making criteria.

1. RFP Win Rate Improvement: Track the percentage of submitted proposals that advance to shortlist consideration or win contract awards, comparing AI-assisted responses against historical baseline win rates to measure compliance accuracy improvements.
2. Time-to-Shortlist Reduction: Measure the elapsed time from RFP response deadline to shortlist selection, targeting significant reductions (40-60%) in evaluation cycle time through automated scoring and initial compliance verification.
3. Requirement Coverage Completeness: Calculate the percentage of stated RFP requirements that receive explicit coverage in final response documents, aiming for 100% coverage through iterative AI-assisted gap analysis and verification processes.

We hope this use case is clear and helpful. If you’d like help implementing it or any other AI use case, reach out and let us know.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.