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Proposal Automation With AI

TL;DR

• AI can draft technical proposals from RFPs, past wins, and templates. It will also hallucinate scope, miss client-specific requirements, and make commitments you can't keep.
• Use AI for structure and first draft. You verify: Does this match the RFP? Did we promise anything we can't deliver?
• Proposals are legal and commercial documents. Wrong promises = wrong contracts. Verify everything.

Technical proposals — scope, architecture, timeline, pricing — are time-consuming. AI can generate from RFP text, past proposals, and templates. That speeds things up. It also introduces risk. AI doesn't know what you've actually committed to. It doesn't know the client's hidden requirements. It can confidently state things that are wrong. Your job: use AI for productivity, own the accuracy.

What AI Can Do

Structure and drafting:

• "Draft a proposal for this RFP." AI can produce sections: executive summary, approach, timeline, team. Often reasonable.
• Good first pass. You fill in the specifics. You verify every claim.

Past proposal reuse:

• "We did something similar for Client X." AI can help adapt that proposal. You ensure it fits this client. No copy-paste of confidential details. No wrong client names.
• Useful. Verify. Proposals often have client-specific commitments. Don't carry over blindly.

RFP response mapping:

• "Answer each section of the RFP." AI can draft responses. You verify they're accurate and complete.
• RFPs have requirements. Miss one and you're disqualified. AI might miss. You check.

Technical architecture sections:

• "Describe our approach for their environment." AI can draft from your product docs and past designs. You verify it fits their constraints.
• Their infra, their compliance, their timeline — AI doesn't know. You add that.

What AI Misses

Scope and commitment:

• Proposals become contracts. "We will deliver X by Y." If AI wrote it, did you commit? Can you deliver? Fix before sending.
• Legal will review. You still own technical accuracy. Don't promise what you can't do.

Client-specific context:

• Their org, their pain, their decision process. AI has generic playbooks. You have discovery notes. Use them.
• Personalization matters. AI gives generic. You add the "we understand your situation" layer.

Compliance and requirements:

• "Must support FedRAMP" or "Must integrate with SAP." AI might mention. It might not. It might get it wrong.
• RFP compliance is binary. Miss a mandatory = lose. You own the checklist. Verify.

Pricing and commercial terms:

• Never let AI set pricing without review. Wrong number = wrong deal. Or wrong margin.
• Commercial terms are sensitive. Human-only.

The Workflow

1. Draft with AI — From RFP, template, past proposal. Get structure and first content.
2. Verify compliance — Every RFP requirement. Checklist. Don't miss.
3. Verify scope — Did we promise anything we can't deliver? Fix. Get sign-off from delivery if needed.
4. Add client context — Customize. Reference their situation. AI gave generic. You add specific.
5. Legal and commercial review — As always. AI doesn't change that. Proposals are binding. Get it right.

Your Role

• Accuracy owner. Technical proposals represent your company. Wrong architecture, wrong scope, wrong commitment — you're accountable. Review everything.
• Context injector. You did discovery. You know the client. AI doesn't. Add that. Make it personal.
• Gatekeeper. Don't send AI output without review. Ever. One wrong proposal can cost a deal or create a bad contract.

Do This Next

1. Draft one proposal section with AI — Architecture or approach. Verify every claim. List what you had to fix. That's your review checklist.
2. Create an RFP compliance checklist — Mandatory requirements. Use it on every proposal. AI might miss. You won't.
3. Define "never AI" — Pricing, legal terms, client-specific commitments. Document. Share. Enforce.