Scale or fail: Three keys to breaking out of AI pilot purgatory

The pressure is on for the insurance industry, with 2025 marking the sector's AI transformation phase, and 2026 firmly all about hyper-accelerated shift to scale. Yet despite significant investment in AI, many enterprises are stuck at the initial phase of adoption, with initiatives confined to individual functions.

That is limiting value creation. Insurance agents are buried in administrative work, while AI pilots remain confined to individual functions rather than integrated into end-to-end operations. Worse, failure to scale has led to a new type of technical debt – AI debt – and inconsistent operational models that limit agility.

The solution is for insurers to scale AI across their companies — strategically integrating the technology enterprise-wide to drive long-term transformation and measurable business impact. Our research shows that 49% of insurers "have deployed generative AI — either fully or partially — across functions," indicating some promise, yet many continue to see limited return on investments (ROI). Here are three reasons why.

Drive toward clean, democratized data

Insurers aren't suffering from a data deficit. But volume doesn't equal value, and bad data limits insurers' ability to reap ROI. An MIT Sloan Management Review study found that bad data costs companies 15-25% of their revenue. Staunching the flow of this massive financial leakage requires shoring up the reliability and validity of the data. AI tools can help evaluate even historical data to determine which data are good — meaning complete, accurate and current — and which are bad, potentially including inconsistent or outdated information that could lead to flawed risk assessments or compliance issues. Insurers can then draw intelligence from this "cleaned" data, which resides in a data estate — a holistic and "living" system that outlines how the data are governed, managed and secured, as well as who can access it and how it's used across the enterprise.
 
With a foundation of good data that's integrated across modernized platforms, insurers are no longer limited by variables in their analyses. Now variables can be exponential, empowering teams to find more correlations and more accurate information. It can also help eliminate or disprove triggers — such as a client being flagged as high risk when it's actually not.
 

Adopt an AI-first strategy

Personalized and flexible hybrid customer environments present diverse and dynamic risk variables that are part of the modern-day demands on insurers. Mainframe and legacy platforms simply weren't designed to keep up, but the flexibility and fluidity afforded by adopting AI at scale can address these challenges. Insurers are sitting on decades of data that are siloed across disconnected legacy administrative systems and platforms. Just as critical as the data estate is employing a digital experience layer that creates a library of reusable AI agents and tools that can be used across every claim system.
 
Modernization can be game changing, with clean data and cloud-based platforms, but it needs to start with strategy, not tactics or stop-gaps. Insurers must challenge decades-old assumptions about how their products should be sold, underwritten and serviced. They need to reimagine the process entirely.

While some organizations are tackling these concerns, they're often still operating in siloed projects, chipping away at a specific area like claims, rather than developing roadmaps and iterating in phases. When scaling is led by strategy, the end goal is a significant opportunity for an insurer to create its "why." That could be its uniqueness in a product, user experience, or a point of differentiation for attracting new customers and talent.
 

Partner with ecosystem players

A huge hurdle that prevents insurers from breaking out of these pilot modes is the need for specialized skills and expertise through the AI transformation process. When strategizing about full adoption, insurers should crowdsource perspectives not just from IT but all business units for cross-functional collaboration.

Insurers should consider how to expand teams' capabilities, whether with training or recruiting new talent. Teaming up with external partners who lead AI transformation can also supercharge the pace. Insurers can capitalize on their deep expertise developed by challenging the status quo and constantly testing and evaluating solutions to craft best practices within the industry and beyond. This creates an added benefit of building institutional capacity for insurers and the next generation of AI talent internally.

In 2026, the question for insurers is no longer whether to pursue AI, but how to embed it into the fabric of the enterprise to harness its transformative potential.