Top 3 Hurdles of AI Deployment in Insurance (2026)

The three biggest hurdles to deploying AI in insurance are data quality, security, and ROI. Bevaya co-founder and CEO, Chaz Perera, explains what causes each one and how to handle it before it stalls your program. Insurance teams trying to get AI into production tend to hit the same set of hurdles, whatever their size or use case. Knowing what to expect ahead of time makes each one easier to handle, and it saves the time and stakeholder trust that teams otherwise lose figuring it out the hard way. Data quality: Insurance data is scattered across legacy systems, PDFs, and email, and the mess usually surfaces only in production. Data models built for insurance cut the cleanup, so you fix data as you go. Security: Sensitive policyholder and claims data raises access and compliance questions that grow at production volume. Build in access controls, audit trails, and human oversight from the start. ROI: Many leaders believe in AI but can't put a defensible number on it. Start with a repetitive, high-cost workflow and define the number you'll track before you begin. The session closes with a way to think through which hurdle your program is most likely to face first, so you can plan for it instead of reacting to it. Chaz spent 14 years at AIG in executive global roles across claims, operations, and strategy before co-founding Bevaya, the AI agent platform built for insurance. Chapters 0:00 Intro: Top hurdles of AI deployment in insurance 1:05 What's slowing insurance AI down 2:59 Top 3 hurdles to AI adoption in insurance 3:34 Why insurance faces these AI hurdles 6:07 Hurdle 1: Unclear ROI and business case 8:28 Hurdle 2: Data quality and availability 12:12 Hurdle 3: Security and data privacy 15:11 The cost of standing still on AI 17:45 Q&A Take the platform tour: https://www.bevaya.ai/tour?utm_source... See the benchmarks: https://www.bevaya.ai/benchmarks?utm_... #InsuranceAI #AIinInsurance #Insurtech