Proposals That Close Deals

Before we touch AI, let's agree on what a great proposal looks like. The best proposals aren't product brochures — they're documents that say "I heard you, I understand your problem, and here's exactly how we'll fix it."

Every winning proposal has seven sections:

Executive summary — Two paragraphs that prove you listened during discovery. Reference their specific pain points, not generic industry problems.

Problem statement — Their world without your solution. Quantify the cost of inaction. Make it hurt (a little).

Proposed solution — How your product solves their specific problem. Not a feature list — a narrative that connects features to their pain.

ROI calculation — Hard numbers. What will they save? What will they gain? Show your math.

Timeline and implementation — When does this happen? What are the phases? Who does what?

Pricing — Clear, simple, tied back to value. No surprises.

Next steps — The exact action they need to take, with a date.

Let's see the difference AI makes. Here's a typical proposal opening written by a rushed rep:

"Thank you for your interest in CloudSecure. We are a leading provider of cloud security solutions, serving over 500 enterprise clients worldwide. Our platform provides comprehensive protection against cyber threats, ensuring your organization stays secure and compliant."

Now here's what AI generates when you feed it your discovery notes:

"During our conversation on March 12th, you shared that Meridian Health's move to AWS last quarter exposed gaps in your HIPAA compliance monitoring — specifically, your team is manually auditing access logs across 14 cloud instances, consuming roughly 20 hours per week. With two compliance audits scheduled this year and your security team already stretched thin, the risk isn't theoretical. Here's how CloudSecure eliminates that gap."

Same company, same product. But the second version makes the prospect feel like the proposal was written for them — because it was.

Here's the prompt template that makes this work. This is the single most valuable prompt in your sales toolkit:

"I just finished a discovery call. Here are my raw notes: [paste notes]. Using these notes, write a B2B proposal with the following sections: (1) Executive Summary — 2 paragraphs referencing their specific pain points, (2) Problem Statement — quantify the cost of their current situation, (3) Proposed Solution — connect our product features to their specific problems, (4) ROI Calculation — estimate savings and gains with clear math, (5) Timeline — 90-day implementation plan, (6) Pricing — present [your pricing] tied to value, (7) Next Steps — specific action items with dates. Our product is [brief description]. Tone: confident, consultative, no jargon."

Let's see this in action with a real deal.

A proposal for a Director of Operations looks different from a proposal for a CFO. AI lets you create multiple versions targeting different stakeholders — without rewriting from scratch.

For the operational buyer (Director, VP of Ops): Lead with efficiency gains, workflow improvements, and team impact. They care about how this makes their life easier.

For the financial buyer (CFO, Finance): Lead with ROI, cost reduction, and payback period. They care about the numbers.

For the technical buyer (CTO, IT): Lead with architecture, integrations, security, and scalability. They care about how this fits into the existing stack.

For the executive sponsor (CEO, COO): Lead with strategic impact, competitive advantage, and company-wide transformation. They care about the big picture.

Use this prompt: "Rewrite the executive summary of this proposal for a [CFO] audience. Emphasise [ROI, payback period, and risk reduction]. Keep it under 200 words."

One set of discovery notes, four versions of the proposal, each speaking the language that resonates with that buyer.

Before you send any AI-generated proposal, run it through this checklist:

Accuracy check — Are all numbers correct? Did AI make up any statistics? Verify every dollar figure and percentage.

Personalisation check — Does the proposal reference at least 3 specific things from your discovery call? Names, dates, problems, quotes?

Tone check — Does it sound like you, or does it sound like a robot? Read the executive summary out loud. If it doesn't sound like something you'd say in person, rewrite it.

Competition check — If the prospect mentioned a competitor, does your proposal address why you're different without being negative?

Next steps check — Is the call to action specific and time-bound? "Let's discuss" is weak. "Let's schedule a 30-minute review on Thursday" is strong.

This review takes 5 minutes and is the difference between sending a good proposal and a great one.

Let's be real about the impact. If you're writing 3-4 proposals per week and each one used to take 3-4 hours, that's 9-16 hours per week on proposal writing. At 15-20 minutes per proposal with AI, you've just freed up 8-15 hours per week.

That's not a small efficiency gain. That's an entire extra selling day every week. More calls, more meetings, more pipeline — because you're not chained to a Word document.

And here's the kicker: the AI-assisted proposals are typically better because they're more personalised, more data-driven, and more structured than what most reps produce under time pressure. You're not trading quality for speed. You're getting both.