AI for Proposals and Pitches

TL;DR

AI can generate 80% of a proposal in 10% of the time. The remaining 20% — the customization that wins deals — is where your expertise shines.
RFP responses, pitch decks, and case study documents all benefit from AI assistance.
The risk isn't that AI writes bad proposals. It's that everyone's proposals start looking the same. Differentiation comes from human insight.

How AI Accelerates Proposals

The Old Way

Receive RFP or opportunity (Day 1)
Research the prospect (Day 2-3)
Outline the proposal (Day 3-4)
Write the first draft (Day 5-8)
Get internal reviews (Day 9-11)
Finalize and submit (Day 12-14)

The AI Way

Receive RFP or opportunity (Day 1)
AI researches prospect and generates brief (Day 1, 30 minutes)
AI drafts proposal from your content library and the prospect brief (Day 1, 1 hour)
You customize with specific insights, case studies, and pricing (Day 2-3)
Internal review (Day 4-5)
Finalize and submit (Day 5-6)

Two weeks become one. But notice: the human steps didn't disappear. They got better.

Using AI for Each Proposal Section

Executive Summary

AI input: Company background, prospect's stated challenges, your solution overview. AI output: First draft executive summary. Your edit: Add the specific insight that shows you understand their situation. Reference their CEO's recent blog post, their latest earnings call, their competitive challenge.

Technical Solution

AI input: Requirements from the RFP, your product documentation, technical architecture details. AI output: Mapped solution to requirements with technical detail. Your edit: Validate feasibility, add realistic implementation timelines, flag assumptions.

Case Studies

AI input: Your case study database. AI output: Selected most relevant case studies, reformatted for the prospect's context. Your edit: Add specific metrics, name-drop recognizable customers (with permission), highlight parallels to the prospect's situation.

Pricing

AI input: Your pricing model, deal size parameters, competitive pricing intelligence. AI output: Pricing structure with options. Your edit: Strategic pricing decisions — where to discount, where to hold firm, what to bundle. This is judgment, not calculation.

Pitch Deck Generation

AI tools (Gamma, Beautiful.ai, Claude + Google Slides) generate pitch decks from:

Your company narrative
Prospect-specific data
Visual templates

What works: Structure, data visualization, slide layout. What needs you: Storytelling flow, emotional arc, the "why us?" that resonates with this specific audience.

The 10-Slide AI-Assisted Pitch

Slide	AI Generates	You Add
1. Cover	Company name, prospect name, date	Personal touch
2. Their problem	Industry data, market trends	Their specific pain (from conversations)
3. Consequences	Data on cost of inaction	Emotional resonance
4. Our solution	Product overview	Customized to their use case
5. How it works	Technical architecture	Their integration context
6. Case study	Relevant customer story	Personal connection
7. Results	Metrics and outcomes	Projected impact for them
8. Differentiation	Competitive comparison	Your unique angle
9. Pricing	Options and structure	Strategic positioning
10. Next steps	Timeline and process	Clear, confident ask

Two weeks: research, outline, write, review, submit. Manual drafting for every section.

Click "AI-Assisted Proposal Workflow" to see the difference →

Quick Check

What's the biggest risk of AI-generated proposals?

Do This Next

Build a proposal content library. Collect your best executive summaries, case studies, technical descriptions, and pricing templates in one place. Structure them so AI can access and remix them. This becomes your "proposal AI knowledge base."
AI-draft your next proposal. Take a real opportunity. Feed the RFP and your content library to Claude or GPT-4. Generate a first draft. Time the entire process. Compare quality and speed to your traditional approach. Iterate on your prompts until the output consistently saves time.