- 68% of proposal teams now use generative AI in their RFP process, and AI cuts per-proposal effort by 40-60%.
- The time compression is dramatic: proposals drop from an average of 25 hours to under 5, so you can respond to more opportunities.
- The average RFP win rate is about 45% - which means the constraint is often how many quality proposals you can produce, not whether you'd win them.
- AI drafts and assembles; the tailoring, the win themes, and the judgment about which deals to chase stay human - and that's what wins.
Proposals and RFP responses are where a lot of good businesses lose to worse ones - not on merit, but because they couldn't produce enough quality responses fast enough. That's exactly the constraint AI attacks. The direct answer: AI slashes the time to write a proposal - from an average of 25 hours to under 5 - by drafting and assembling the response, which lets you pursue more opportunities, while the tailoring and win strategy that actually close deals stay human.
The bottleneck is capacity, and AI raises it
Start with a number that reframes the whole problem: the average RFP win rate is about 45%. If you win nearly half the proposals you submit, the limiting factor often isn't quality - it's how many strong proposals your team can physically produce. Each one averages 25 hours. That math means most teams leave winnable deals on the table simply because they ran out of time to respond. AI changes that equation directly.
The time compression is real
The adoption reflects the payoff: 68% of proposal teams now use generative AI in their RFP process, with a third of adopters using it daily. AI-assisted drafting reduces per-proposal effort by 40-60%, and early adopters report response times dropping 30-40% - with proposals falling from around 25 hours to under 5. AI does the heavy lifting: assembling boilerplate and past-response content, drafting narrative sections, and structuring the response to the RFP's requirements. What used to consume a week becomes a day, which means more proposals out the door - and at a 45% win rate, more proposals is more revenue.
What still wins the deal (and stays human)
Here's the line that matters. AI can produce a complete, compliant proposal - but a complete, compliant, generic proposal doesn't win. What wins is tailoring: speaking to this client's specific situation, articulating why you specifically are the right choice, and building win themes that connect your strengths to their priorities. That's judgment AI can't supply - it requires understanding the client and the deal. The winning pattern: let AI handle the 60% that's assembly and first-draft, and spend your saved hours on the 40% that actually differentiates - the tailoring, the strategy, and the human touches. Teams that use AI to submit more generic proposals see little lift; teams that use it to submit more tailored ones win more.
What this means for your win rate
If proposal capacity is capping how many deals you chase, AI is the clearest lever you have - and building it into your specific proposal process is a skill about your business, not code. That's the MakerSquare premise. Our use cases show what building tools around a real workflow looks like.
AI can write the proposal in a fraction of the time. Making it the one that wins is still your job - and now you have time to do it on more of them.
MakerSquare is a 2-week in-person AI builder program in Austin, TX for operators who want to raise their proposal capacity and build the tool that does it. See what two weeks of hands-on building looks like.