AI Signals in Insurance You Should Act On Now
Four recent updates point to where value is moving in insurance. Underwriting is getting more data-driven, subrogation is stepping into automation, funding has cooled, and agent tooling is maturing. Here's what matters and how to turn it into results.
What's happening
- Convr AI brought in a new Head of Data and AI Underwriting Solutions (Feb 13, 2026). That's a clear bet on model-driven underwriting and stronger data pipelines.
- CLARA Analytics launched AI-driven subrogation detection (May 9, 2025). Earlier identification means more recoveries with less manual review.
- Insurtech funding dropped to about $3.9B in 2025. Fewer checks, tighter scrutiny-yet AI deals still get attention. Buyers have the leverage; vendors must prove impact fast.
- AI Tools for Agents Demo Day (Dec 3). Distribution tech is catching up-quoting, service, and prospecting workflows are getting streamlined.
Why it matters
Capital is scarce, but AI tied to a clear loss or expense outcome is still moving forward. Carriers and MGAs that connect models to underwriting and claims decisions will win cycle time and consistency. Agencies that automate service and outreach will protect margins without adding headcount.
Moves to make in the next 90 days
- Underwriting: Stand up a pilot for one line and one segment. Feed in submission quality checks, appetite classification, and risk flagging before human review.
- Claims: Add AI-assisted subrogation triage to your first notice and early investigation steps. Route likely recovery cases to specialists on day one.
- Distribution: Automate FAQs, document requests, and renewal reminders. Keep producers focused on selling, not inbox ping-pong.
- Data readiness: Map where key data actually lives (policy admin, claims notes, docs, emails). Fix two data quality issues that block model performance.
- Compliance: Get model documentation, testing evidence, and monitoring in place before procurement closes. It saves months later.
Vendor checklist (use this in your next call)
- Problem clarity: Which decision is improved and by how much? What gets removed from the workflow?
- Data fit: Can it work with what you have today? What's required to reach full performance?
- Proof: Show recent case studies with baseline vs. lift, not just demos.
- Controls: Bias testing, explainability, audit logs, and fallbacks when confidence is low.
- Time to value: Weeks, not quarters. Clear owner, milestones, and exit criteria.
- Security: Data residency, encryption, access controls, and third-party attestations.
Metrics that actually move the needle
- Underwriting: Quote turnaround time, hit ratio, loss pick stability, premium per underwriter.
- Claims Subrogation: Recovery rate, dollars recovered per claim, cycle time to referral, false positives avoided.
- Agency Ops: Average handling time, first-contact resolution, response SLAs met, revenue per producer.
A quick note on subrogation
Early identification is the whole game. If your team finds potential recovery after payments are well underway, your odds drop and costs rise. A simple triage layer at intake can surface opportunities your adjusters would otherwise miss.
Need a refresher on definitions and process? See a plain-English overview of subrogation here: IRMI: What is Subrogation?
Ramp up team skills without pausing production
Pick one micro-skill per role and layer it into live work. For example: prompt libraries for adjuster summaries, or underwriting notes structured for model reuse. If you need curated options by role, here's a collection to browse: AI courses by job.
Bottom line
Focus on one underwriting decision, one claims decision, and one agency task. Prove a lift, document the controls, and then scale. The companies that keep it this simple will quietly out-execute everyone else.
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