What insurance companies your size have already deployed. What it cost. What it returned.
We track every AI deployment in insurance — 168+ documented implementations. The roadmap turns that into a plan specific to your workflows, your size, and your stack. Not a generic playbook. Not a pitch deck.
Start with a fit check. If we cannot build a useful roadmap from your situation, we stop and you pay nothing.
The data gap that makes AI decisions hard
You can find hype pieces, vendor demos, and $50K consulting reports. What you cannot find is what companies your size in insurance actually deployed, what it cost them, and what the outcomes looked like. That is the gap this roadmap closes.
Worst case: you walk away knowing exactly what companies your size deployed and what it returned. Best case: you have an implementation plan your board approves next week.
Regulatory readiness is part of the roadmap
We map relevant dated obligations to the proposed workflows, flag the decisions that need specialist review, and separate current duties from future deadlines.
NAIC Model Bulletin on AI Systems
United States · 2023-12-04
Adopting states expect insurers to govern, document, and test AI-supported decisions.
Primary sourceWhat you get
12 pages. Delivered 72 hours after the discovery call.
One page. Outcome-first. Board-presentable.
The 4 quadrants. Which opportunities are fast wins, which are strategic bets, which to skip.
Companies your size in insurance: what they've already deployed, what it cost, what the outcomes look like. The thing nobody else can give you.
Hours saved, dollars saved, revenue unlocked. Setup time. Build-vs-buy. A short list of vendors we'd actually trust.
For every opportunity: 3–5 insurance companies who've already done it. With real metrics.
Named owners. Milestones. Gate checkpoints. What to ship in the first month to prove the approach.
Current weekly hours. Projected monthly ROI. The math your CFO wants to see.
A dated regulatory-readiness module, plus a priced opportunity inventory covering DIY, advisory, a custom knowledge system, and vendor-supported implementation.
Why this is different
Most AI consultants open ChatGPT and quote you $15,000. We track 168+ real deployments in insurance. Every recommendation maps to a documented outcome at a real company — not a projection, not a case for the technology in general.
Every recommendation is anchored in a documented deployment at a insurance company. With numbers.
Vetted vendors, scoped to your use case and company size. Not a 40-tool list you have to filter yourself.
The full $3,000 credits toward a $7,500 custom knowledge-system engagement or an advisory year within 60 days.
Two ways to start
The full engagement. 45-min discovery call, 12-page roadmap, 30-min walkthrough, one revision round. Delivered in 72 hours.
- 45-min discovery call
- 12-page roadmap with 8 opportunities
- Peer benchmarks from insurance companies your size
- ROI estimates and vendor shortlists
- 30-day quick-wins plan
- Financial impact model
- 30-min walkthrough call
- Regulatory module and priced opportunity inventory
- One revision round
Full payment at checkout. Credits toward implementation within 60 days.
Async, no calls: structured written intake, peer benchmarks, top 3–5 opportunities with ROI. Delivered in 48 hours. The $999 fee credits toward the full Roadmap for 90 days.
- Structured written intake (~20 questions)
- Focused report: top 3–5 benchmarked opportunities
- Peer benchmarks from insurance companies your size
- ROI estimates in your own numbers
- 30-day quick-start plan
- $999 credits toward the full Roadmap for 90 days
Full payment at checkout. Fee credits toward the Roadmap if you upgrade within 90 days.
Guarantee
If your Roadmap or Snapshot does not identify at least 3 AI opportunities benchmarked against peer deployments in your segment, you get a full refund.
Who it's for
underwriters and claims managers at companies doing $5M–$500M in revenue who want a concrete plan, not a pitch deck.
- You're a solopreneur looking for a quick AI tip sheet.
- You need a 50-page McKinsey-style engagement.
How it works
Who's behind this
Peter is a data analyst and the founder of 100Signals, where he builds evidence-based market intelligence for software and IT companies. AI for Insurance applies the same discipline to AI adoption in insurance: every case study in the directory comes from a published source, passes a quality gate, and links back to where it came from — so you can compare real outcomes instead of vendor hype. Peter personally reviews every AI Opportunity Roadmap before it ships.
LinkedIn →Questions
Get a roadmap your board will actually read.
Every recommendation benchmarked against real insurance deployments. Delivered in 72 hours.
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