Insurtech Leaders Say AI Has a Data Problem

The takeaway from Insurtech Insights USA 2026 was blunt: insurers and distribution partners need cleaner, governed, accessible data before AI can scale into underwriting, claims, compliance, and financial workflows.

The insurance industry has moved well past debating whether artificial intelligence belongs in its core operations. At Insurtech Insights USA 2026, which drew more than 6,000 attendees to the conversation, the more pressing question was whether insurers and their distribution partners actually have the data discipline required to use AI responsibly and effectively. If you've spent any time watching carriers demo flashy AI pilots, you know the gap between a conference presentation and a production deployment can be enormous.

The conference wrapped with a clear consensus: AI momentum is real, but weak data foundations, legacy systems, and fragmented workflows remain the primary barriers to practical deployment. For agents, wholesalers, managing general agents (MGAs), and program administrators, this signals a shift from broad AI excitement to a more focused effort on operational readiness. Sound familiar? It should, because most distribution professionals have already felt these friction points firsthand.

Why Data Has Become the Real AI Bottleneck in Insurance

The core challenge for many carriers and MGAs is that their systems were built for transactional processing, not the probabilistic analysis modern AI demands. Data is scattered across siloed policy administration systems, claims platforms, email inboxes, and spreadsheets. Think of it like trying to run a high-performance engine on contaminated fuel; the machine might turn on, but it won't perform the way you need it to. AI isn't creating a new data problem here. It's exposing weaknesses that have festered for years.

The concern is widespread. A recent report found that 83% of insurance executives worry their AI models are trained on inaccurate or incomplete data. Investment in technology continues to grow, with the global insurance software market projected to reach USD 20.41 billion by 2031, up from USD 14.14 billion in 2025. But as multiple speakers at the conference emphasized, spending alone can't solve the foundational data issue. You can throw money at the shiniest AI tools on the market and still end up with garbage outputs if what's feeding those tools is messy.

What "Clean, Governed, Accessible" Data Actually Means

For insurance operations, these terms carry specific weight. Clean data is standardized, accurate, and free of duplication. Governed data includes clear ownership, lineage, and controls that make it auditable and secure. Accessible data means it can be used across different teams and workflows without requiring extensive manual extraction or manipulation. If you're picturing a mid-size MGA where one team exports submissions from a portal, another re-keys them into a policy admin system, and a third pastes notes into a spreadsheet for reporting, that's exactly the kind of environment where "accessible" falls apart fast.

Where Insurers Are Being Told to Start With AI

A consistent theme from industry leaders, including voices from OpenAI and major carriers, was to prioritize moderate- and lower-risk use cases first. This approach lets organizations build experience, demonstrate value, and refine data practices without exposing the business to significant financial or compliance-related consequences. The goal? Early wins pave the way for more complex applications down the road.

Here are the lower-risk AI use cases that insurers and distribution teams can test first:

• Submission triage and routing—a natural starting point for high-volume wholesalers
• Document classification and intake support
• Internal knowledge search for underwriting or service teams
• Producer and customer service copilots with human review baked in
• Claims status communications and administrative summarization
• Workflow prioritization and anomaly flagging

Conference speakers warned against moving too quickly into areas such as autonomous pricing, financial reporting, or regulated determinations, where a lack of auditability could create serious issues. While AI-powered underwriting automation can reduce manual effort by 50%, and AI in claims processing can cut cycle times by 40–60%, those benefits hinge entirely on trustworthy inputs and strong governance. Skip the data discipline, and those efficiency gains can quickly become liability accelerators.

How Legacy Systems and Fragmented Workflows Still Slow Modernization

The data problem in insurance is inseparable from the industry's reliance on legacy core systems. Many organizations still depend on manual data rekeying and spreadsheet-based handoffs between policy, billing, and claims platforms. Ask any operations manager at a regional MGA, and they'll tell you the same thing: applying AI on top of these broken workflows often accelerates bad processes rather than fixing them, creating more problems faster.

Industry reports confirm that the top barriers to scaling AI are data readiness at 45%, security and privacy at 43%, and legacy system integration at 41%. For MGAs, wholesalers, and program administrators, modernization often depends less on buying a new AI tool and more on integrating the systems already running submissions, user portals, and partner-facing experiences. So if you've been wondering why that slick AI demo you saw last quarter hasn't translated into real-world results at your shop, the answer is probably sitting in your tech stack, not your strategy deck.

Technology providers that work inside insurance operations say the issue often isn't a lack of enthusiasm for AI; it's the condition of the underlying systems and data. “The clever teams we partner with aren't usually held back by a lack of business ideas. They struggle because traditional options are so one-size-fits-all that they pawn off the same tired services on everyone, resulting in disconnected operations,” said a spokesperson for Brain Box Labs, a firm specializing in modern application development and custom insurance software. “The practical path is to focus on a personalization experience centered around an insurance software capable of melding well with your pre-existing digital operations. You have to ensure you are not overdoing it with too many features that confuse the user, planning around the end-user first to lay out a seamless, successful workflow.”

What Agents, Wholesalers, MGAs, and Program Administrators Should Watch Next

So far, you've seen the data problem, the recommended starting points, and the legacy barriers holding things back. For distribution professionals, the conference takeaways translate into a greater focus on operational discipline. The push for better data will directly impact submission workflows, appetite matching, and compliance reviews, especially in complex commercial and specialty lines.

Key areas to monitor include a greater emphasis on structured data intake, auditable decision trails, and improved user experiences for both producers and insureds. Not where you expected the AI conversation to land, right? But that's exactly the point: the competitive edge isn't in who adopts AI first; it's in who prepares for it most thoroughly.

The industry's cautious approach creates a clear distinction between manageable starting points and higher-stakes applications that require significant maturity:

The Next AI Winners May Be the Firms That Fix Their Data First

Artificial intelligence remains a major opportunity for the insurance sector to enhance efficiency, improve risk selection, and deliver better customer outcomes. But the firms most likely to benefit aren't necessarily those with the most ambitious pilot programs. They're the ones investing in the less glamorous—yet essential—work of improving data quality, enforcing workflow discipline, and pursuing modernization that solves real operational needs.

For insurance marketplace participants, particularly those operating in specialty and program business, building a foundation of clean and reliable data is quickly becoming a competitive advantage. This discipline is no longer just a technology requirement. It's a prerequisite for sustainable, AI-driven growth. And if there's one message that came through louder than anything else at Insurtech Insights this year, it's that the data work can't wait.