The Unsexy Problems in Insurance That Founders Keep Missing

A property owner in Florida is fighting a slip-and-fall claim that's costing her thousands in legal fees. An independent agent in Ohio spends hours each week reconciling commission statements against carrier payments. A life insurance newbie in Texas bought leads that were mostly dead ends. None of this makes headlines. None of it involves AI agents or ChatGPT distribution. But it's where the money is leaking.

CB Insights recently profiled 10 repeat founders building new insurtech companies in 2026. The piece, by Adya Pandey, highlights an interesting split: some founders are applying AI to old workflows like underwriting and claims, while others are insuring brand-new risks created by AI itself. It's a solid read for anyone tracking where smart money is going in insurance.

But it's missing something. Our data shows the most severe pain points in insurance are often the least glamorous. We track over 26,000 problems across 89 industries, and when we filter for insurance, the patterns are striking. The problems with the highest severity scores aren't about AI risk or novel distribution channels. They're about claims fairness, lead quality, and the daily grind of running an agency.

Take claims, for instance. We track 17 distinct problems in insurance claims management alone, many rated severity 4 out of 5. One user logged the problem as simply: "We have to sue on every State Farm claim." That's not a tech problem. That's a trust problem. And it's probably not going to be solved by an AI model, no matter how sophisticated.

Then there's the distribution side. The article touches on Coveresy, which is building AI-powered insurance guidance with human advisors in India, and WaniWani, which lets insurers sell inside AI assistants. Both are interesting plays. But our data surfaces a more basic pain point: agents are buying leads that are misinformed or outright useless. One problem we track describes agents who bought leads from Caboom that were misinformed about free burial expenses. Another describes a new life insurance agent struggling to find contacts and leads at all. Lead verification and acquisition tools might not be as sexy as selling insurance on ChatGPT, but they solve an immediate, painful problem for a huge user base.

Independent agencies are another overlooked area. The featured startups are mostly building for carriers and MGAs. Dearborn Labs embeds engineers inside carriers and MGAs. WriteRisk helps MGAs underwrite specialty commercial property. OmniGeo sells wildfire risk data to insurers, MGAs, and reinsurers. But independent agencies are struggling with fragmented workflows and have no systematic way to reconcile expected policy-level commissions with actual carrier payments. That's a massive operational headache that no one in the CB Insights piece is directly addressing. It's not AI-native. It's just automation and data plumbing. But it's a real business with clear willingness to pay.

Now, don't get me wrong. The startups in that article are doing genuinely interesting work. Ollive's approach to AI errors and omissions coverage is smart—someone has to insure the robots. Recourse Health's focus on the No Surprises Act dispute process is clever regulatory arbitrage. WaniWani's traction is impressive: $8.7M raised and the first insurer-built app approved by OpenAI on ChatGPT. These are all bets on the future of insurance.

But the present is still full of holes. And founders often overlook the boring problems because they don't generate the same excitement. AI underwriting sounds cooler than commission reconciliation software. But our data suggests the latter might have a faster path to revenue. Users aren't just mildly annoyed by these problems; they're actively frustrated. Frustration is a buying signal.

There's also a lesson here about AI as a differentiator. The article implies that AI is the primary driver of innovation. But many of the most severe problems are rooted in fundamental process issues—claim fairness, data access, lead quality—that don't necessarily require advanced AI to solve. A simple, transparent workflow tool might beat a complex AI system for these use cases. Builders should be careful not to over-index on AI as a feature when the core value proposition is just making something work reliably.

If you're a founder or investor looking at this space, here's my advice: use data to find the pain points that are actually hurting people right now. Not the ones that will hurt people in the future if AI goes wrong. Those are bets. The sure things are the problems that already have severity scores of 4 out of 5. Look for the problem statements that sound like complaints, not opportunities. That's where the money is.