Proposals, objection handling and RFP answers

0:00 Proposals and objections

Have you ever sent a proposal and heard nothing back? Often it's not the price. It's that the proposal was about you: your company history, your methodology, your features. The buyer couldn't see their own problem in it. In this lecture you'll learn a four step proposal chain that mirrors the buyer's words, offers clear options and survives a skeptical review, plus how to build an objection library once and use it forever, and how to draft RFP answers safely. You'll see a Dubai events agency win a project, and watch me run a buyer's eye review that catches an accidental guarantee.

0:44 Why proposals fail

Why do proposals fail? Three common reasons. They're about the seller, not the buyer, so the buyer has to work to see why it matters to them. They offer one price, take it or leave it, which turns the decision into yes or no instead of which one. And they contain accidental guarantees, enthusiastic lines like we'll double your leads, that create legal risk and make careful buyers nervous. AI can make all three problems worse, by producing polished, generic proposals quickly. Used well, it fixes all three.

1:22 The tailor analogy

Think of a good tailor. They measure you first: that's discovery. They show you a few fabrics and fits at different prices: those are your options. And they only promise what they can actually sew by the date you need it. A proposal built this way fits the buyer, gives them a real choice, and contains nothing you can't deliver. Here's the key idea. AI can draft the structure and the wording, but the measurements come from your discovery notes, and the prices and promises come from you.

2:00 The four-step chain

Here's the chain. Step one: structure from discovery. From your notes and context pack, outline the proposal: their situation and goals in their words, what success looks like by their measures, your approach, good, better and best options, timeline and responsibilities on both sides, investment, which you supply, approved proof, and next steps, with decide for anything the AI needs from you. Step two: draft the first sections only from the notes, quoting the buyer, describing what you'll do rather than guaranteeing results. Step three: the options table, from the scope and prices you provide, with an instruction not to change any price or scope. Step four: a buyer's eye review.

2:48 The buyer's-eye review

The buyer's eye review is the step that wins deals. Ask the AI to read the proposal as the buyer's CFO or procurement lead. What's unclear? What's missing? What would you push back on? And what could be misread as a guarantee? It's a fast way to see your proposal through skeptical eyes before the real skeptic does. Typical catches: responsibilities on the buyer's side that aren't stated, a timeline with no assumptions, a benefit phrased as a promise. Fix those, and your proposal survives the meeting you're not invited to, where the buyer's team decides.

3:30 Objection library and RFPs

Two more tools. The objection library. Most teams hear the same ten objections, so answer them once, well. For each: what's usually behind it, an honest two or three sentence response using only your facts and approved proof, a question to ask back, and versions for email and for a call. No pressure, no dismissing, no invented proof. Review it with your best salesperson and add real phrasings as you hear them. And for RFPs and supplier questionnaires, use a source grounded setup: upload approved past answers, policies and product documentation to a project or Gemini Notebook, and draft answers only from those sources, citing the document. Anything without a source goes to an expert. Never let AI invent certifications or compliance statements.

4:23 Example 1: too expensive

A simple example from the library. Objection: it's too expensive. What's usually behind it? Often it's not the number. It's that the value isn't clear compared with what they spend today, or they're comparing you with a cheaper option that does less. The honest response: acknowledge it, restate the specific outcome they told you they care about, and show what's included, using only real facts. Then the question back: what are you comparing it with? That question turns a dead end into a conversation, and often reveals the real objection.

5:02 Example 2: Farah's kickoff proposal

Now a fuller example, with illustrative details. Farah runs a small events agency in Dubai. After discovery with a tech company planning a regional sales kickoff, she runs the chain. The proposal opens with the client's goal in their words: a kickoff people actually talk about in March, not just attend. It offers three packages, which she priced herself. Then the buyer's eye review, as the client's procurement lead, flags a line: we'll ensure one hundred percent attendee satisfaction. That reads like a guarantee nobody can keep. She replaces it with what the agency will actually do: a pre event survey, live feedback during the day, and a post event report. The client chooses the better package, and mentions the options table made the decision easy.

5:57 Watch me do it

Let me build one. I'm in my sales project, and I paste discovery notes from a call with a private school in Riyadh about a parent communication platform. Step one: the outline. It quotes their goal, parents say they miss important messages in WhatsApp groups, and marks decide on the rollout timeline and pricing. Step two: sections one to three from the notes only. Step three: I paste our real price list and the scope for three options, and ask for a good, better, best table without changing any price. I check: every price matches. Step four: read this as the school's finance director. The review asks who trains the teachers, which I hadn't stated, and flags a sentence, parents will never miss a message again, as a guarantee. I rewrite it to describe the features. Much stronger.

6:57 Common mistakes

The common mistakes. Letting the AI set or optimize pricing and scope. Those are business decisions. Guarantees disguised as enthusiasm, like we'll double your leads. Proof from case studies you don't have permission to share. And recycling old proposals with a find and replace on the client name, then missing one mention. Every proposal should start from this buyer's discovery notes. It's faster than you'd think with the chain, and buyers can always tell.

7:29 Recap and try this now

Let's recap. Build proposals from discovery notes, with the buyer's goals in their words, success measures, your approach, good, better and best options, responsibilities on both sides, approved proof and next steps. You set prices and scope. Run a buyer's eye review to catch unclear sections and accidental guarantees. Build an objection library once, and draft RFP answers only from approved sources. Here's your try this now. Run the four step chain on a real or recent opportunity, and build library entries for your five most common objections. Save both in your sales project, and share the library with your team.