Operational gaps in insurtech platforms

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐰𝐚𝐧𝐭𝐬 𝐀𝐈. 𝐕𝐞𝐫𝐲 𝐟𝐞𝐰 𝐢𝐧𝐬𝐮𝐫𝐞𝐫𝐬 𝐚𝐫𝐞 𝐩𝐫𝐞𝐩𝐚𝐫𝐞𝐝 𝐟𝐨𝐫 𝐰𝐡𝐚𝐭 𝐀𝐈 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐞𝐱𝐩𝐨𝐬𝐞𝐬. I see this pattern everywhere right now. Insurance leaders rush to deploy AI models for underwriting, claims, or pricing. The models perform well in testing. Everyone celebrates the innovation. Then reality hits. AI doesn't hide data problems. It amplifies them. That clean dataset you thought was ready? AI finds the gaps instantly. Those manual overrides your team made for years? AI reveals the inconsistencies. That tribal knowledge sitting in someone's head? AI exposes how much you've been depending on it. Here's what most insurers miss: AI implementation isn't a technology project. It's an organisational mirror. When AI starts making recommendations, it forces uncomfortable questions: • Why do we have three different definitions for the same risk factor? • Why does our data quality drop after the first renewal? • Why can't we explain this pricing exception from 2019? • Why do different teams use completely different assumptions? These questions existed before AI. We just didn't have to answer them. The insurers winning with AI in 2026 aren't the ones with the fanciest models. They're the ones willing to fix what AI reveals. They treat AI deployment as a forcing function for organisational clarity. Before launching the next AI initiative, ask yourself: • Are we ready to face what our data actually looks like? • Can we handle the transparency AI will create? • Do we have the discipline to fix foundational issues before scaling? AI won't transform your business if your business isn't ready to transform itself first. What's the hardest truth AI has revealed in your organisation? #AIinInsurance #InsuranceLeadership #InsurTech #DigitalTransformation #DataStrategy

After 20 years in insurance operations, I'm seeing a fundamental shift that most carriers are missing. The old playbook for operations was simple: offshore repetitive tasks, optimize cost-per-FTE, measure efficiency in headcount reduction. That playbook is dead. The new reality: Modern insurance operations is about identifying where to apply #AI and #automation to shift from linear to non-linear delivery models. The winners aren't competing on labor costs — they're competing on which use cases actually move the needle. Three things I'm seeing in the market: 1. #Gen AI and #Agentic AI are moving into production — selectively The best outcomes aren't "AI everywhere." They're targeted deployments in underwriting exceptions, claims triage, and policy admin workflows where AI handles volume and humans handle complexity. Companies trying to automate everything are failing. 2. Vendor AI solutions promise near-perfect accuracy. Production reality is 60-80% on average. Every vendor demo shows flawless outcomes. Then you deploy and accuracy drops because real insurance data is inconsistent, incomplete, and full of edge cases the model never saw in training. Carriers struggle to evaluate which solutions actually work vs. which just performed well on sanitized demo data. The gap isn't the technology — it's understanding your specific data quality and process reality. 3. AI companies don't factor in domain and process nuances Tech firms building AI for insurance treat underwriting, claims, and policy admin as generic document processing problems. They're not. Each has decades of business rules, regulatory requirements, and process exceptions that AI models trained on generic data completely miss. The companies winning are those that combine AI capabilities with deep insurance domain expertise. The carriers figuring this out are seeing 40%+ efficiency improvements while improving customer experience. The ones stuck in 2015 thinking are bleeding market share. What am I missing? If you're operating in insurance or building technology for insurance, what's the reality gap between vendor promises and production results? #InsuranceTechnology #AIinInsurance #InsuranceOperations #GenAI

There's a coverage structure most Insurtech MGAs have that will fail them at claim time. I call it the Two-Carrier Trap. Two policies. Two carriers. Tech E&O with one. MGA E&O with the other. When the claim comes in, the tech carrier says "that's an operational issue, not ours." And the MGA carrier says "that's a tech failure, not ours." You're caught in a finger-pointing battle while nobody pays. But for an Insurtech, the tech is the operation. You can't separate them. The risk is singular. An insurtech we work with had this exact setup. They came to us after they'd outgrown their startup coverage. They'd bought direct back then, went online, checked the box. Vanilla program. Generic terms. And the Two-Carrier Trap baked in. We took them to the global market. US, Bermuda, Lloyd's. Arbitraged the markets, to get a better form, not just a better price. Found a carrier willing to build a single unified policy. One carrier. All E&O perils under one roof. → No coverage gaps between policies → No finger-pointing at claim time → No arguing over which carrier responds P.S. We write about structural gaps like this in our newsletter: https://lnkd.in/e8qkpZkY

💫2026: How GenAI’s Role Changes in Insurance Simple efficiency gains are done. The basic productivity phase of GenAI is largely complete. What comes next is not smarter AI. It is exposed operations. ⸻ 🔍 What GenAI really surfaces in 2026 As GenAI moves beyond basic automation, it begins to reveal operational gaps insurers have lived with for years: 　⚠️ processes that work only because people fill in the blanks 　⚠️ decisions based on tacit understanding rather than explicit rules 　⚠️ operations that can be explained, but not consistently repeated This is not a technology issue. It is an operational reality check. ⸻ ⚙️ Why this matters now In 2026, GenAI removes the human buffer that used to absorb ambiguity. What was once: • “handled case by case” • “managed by experienced staff” • “good enough in practice” becomes visible, inconsistent, and hard to scale. ⸻ 🎯 Executive takeaway GenAI does not change insurance operations. It reveals which operations were never fully defined in the first place. GenAI is no longer an efficiency tool. It is an operational stress test. #GenAI #Insurance