Underwriter
Quoting & Proposal Preparation
What You Do Today
Build the quote — coverage terms, conditions, exclusions, pricing, payment plans. Write the proposal letter. A thorough quote on a complex commercial account takes 2-4 hours of documentation.
AI That Applies
Automated quote generation from underwriting decisions — pre-populated coverage forms, auto-generated proposal letters, standard terms applied based on risk classification. LLM-drafted proposal narratives.
Technologies
How It Works
The system ingests underwriting decisions — pre-populated coverage forms as its primary data source. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The terms and conditions you set.
What Changes
The mechanical part of quoting goes from 2 hours to 30 minutes. The AI drafts the proposal letter from your underwriting notes.
What Stays
The terms and conditions you set. Which exclusions to apply, what deductible to require, whether to add a warranty. The quote reflects your underwriting decisions.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for quoting & proposal preparation, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long quoting & proposal preparation takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your chief underwriting officer or VP Underwriting
“What data do we already have that could improve how we handle quoting & proposal preparation?”
They're setting the AI strategy for risk selection
your actuarial lead
“Who on our team has the deepest experience with quoting & proposal preparation, and what tools are they already using?”
They build the models that AI underwriting tools are measured against
a senior underwriter with deep book knowledge
“If we brought in AI tools for quoting & proposal preparation, what would we measure before and after to know it actually helped?”
Their judgment is the benchmark — AI should match it, not replace it
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.