How I Use AI to Predict RFP Success (Instead of Just Writing First Drafts)

Written By Christina Carter

Did you have "show and tell" in first grade? I certainly did, and it offered great insights into my classmates’ lives.

Although I’m not here today to show you my favorite Hotwheels car, I am here to demonstrate something almost as exciting: how I use ChatGPT to create an AI-driven qualification matrix based entirely on past win/loss data.

What I love most is that AI isn't guessing. Instead, it’s identifying meaningful patterns across hundreds of previous RFPs, enabling quick, data-backed Go/No-Go decisions in minutes, not hours (or days, if you're anything like my past sales teams).

But if colleagues ask how you're suddenly so good at predicting wins and losses, it's your call whether or not to reveal your secret.

Why Proposal Qualification Matters

Proposal teams constantly operate with limited resources. Even during prosperous times, we rarely have enough hands to tackle every opportunity. Each minute spent chasing unwinnable RFPs is valuable time lost on opportunities that genuinely align with our strengths.

Consider these industry stats:

  • 81% of high-performing teams (with win rates above 50%) employ a structured Go/No-Go process.*

  • 29% of proposal professionals name "knowing which RFPs to focus on" as their greatest challenge.*

  • AI-driven qualification helps streamline and automate these critical decisions, significantly reducing wasted effort.

(Source: Loopio & APMP RFP Response Trends & Benchmarks Report)

Building an AI-Driven Qualification Matrix

I've moved beyond traditional static spreadsheets to create an adaptive, AI-powered qualification process:

Step 1. Deep Research on past wins and losses: I input historical data, such as win rates, reasons for losses, and competitive dynamics. AI then highlights patterns correlating with success.

Here’s a video on how I do this ->

Step 2. Constructing a qualification matrix: The AI categorizes essential factors like incumbent relationships, geographic location, competition, pricing, and technical alignment.

Here’s a video on how I do this ->

Step 3. Automated scoring of new RFPs: Incoming opportunities are instantly evaluated against my AI-generated criteria, resulting in clear Go/No-Go recommendations.

Step 4. Continuous optimization: Now, with rapid analysis at my fingertips, I can regularly update my qualification model, keeping it sharp and relevant.

How AI Makes This Possible

There's a misconception that AI struggles with numbers. Sure, AI isn't your calculator (don’t ask it to perform precise arithmetic), but it excels at pattern recognition within large datasets far beyond manual capabilities.

AI is particularly adept at:

  • Summarizing historical RFP data to identify winning and losing trends.

  • Prioritizing qualification factors based on historical impact.

  • Continually refining its accuracy with fresh data.

This transforms qualification from guesswork into a precise, data-driven process, helping proposal teams consistently pursue the right opportunities.

What About Privacy?

A common concern with AI is data security. If you're analyzing historical RFP data using AI, anonymization is key here:

  • Remove identifying details (client names, specific company information).

  • Opt for private or offline AI instances when possible, so data doesn't leave your environment.

  • Integrate AI securely with existing platforms like CRMs or analytics tools to retain control over sensitive insights.

Further Reading

Christina Carter
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How to Choose the Right Proposal Management Tool