Viewpoint: Model Independence Is the AI Strategy Insurers Can’t Afford to Ignore

Right now, many enterprises are approaching AI adoption like a vendor selection exercise.

Which model should we partner with? Which platform is smartest? Which large language model (LLM) has the features and are at a price point we’re looking for?

It is an understandable instinct. Enterprise technology decisions have historically been built around selecting a vendor, implementing a platform and committing to an ecosystem for the long haul. But AI is proving to be fundamentally different.

Most organizations are still treating AI like a software procurement decision when, in reality, the technology is evolving too quickly for long-term dependency on any single model to be sustainable. Models improve, specialize and disappear at a pace that makes rigid commitments increasingly difficult to justify — particularly in insurance, where different workflows demand different kinds of intelligence.

This is why building an effective, future-ready AI strategy requires moving past the vendor selection mindset altogether and embracing model independence: the ability to deploy, switch and orchestrate across whichever models best serve any given task.

AI models are evolving too quickly for long-term dependency

The AI market has become defined by speed.

There are constant headlines about new models being released, each promising sharper reasoning, better document processing or lower cost. Many enterprises spot something new and find themselves thinking: this could work better for us.

But for organizations that have already embedded a model into underwriting, claims or document ingestion, that realization comes with a catch. Switching means retooling integrations, retesting outputs and rebuilding workflows that likely took months to configure — a significant operational undertaking most teams are not resourced to absorb regularly.

At the same time, model vendors like OpenAI, Google and Anthropic are retiring older models faster than ever, often with little advance notice. For organizations running operations built on those models, the consequences are immediate. Teams are left scrambling to readjust workflows with little runway to do so.

Insurance organizations, in particular, require stability. Underwriting processes, claims systems, and regulatory environments are not designed to shift every time a new model release captures headlines.

That makes flexibility increasingly important.

Insurance is becoming a multi-model environment

For insurance enterprises specifically, flexibility isn’t just about hedging against market volatility. No single AI model is capable of performing every enterprise task well.

Insurance workflows are complex and highly specialized. The type of intelligence needed to extract insights from loss runs may differ from what is needed for submission ingestion, claims summarization or catastrophe risk evaluation. Models that excel at reading structured documents may not perform as effectively in predictive analysis. Others may offer stronger cost efficiency or domain-specific capabilities.

This is one reason why interest in smaller, specialized language models is growing. Gartner forecasts that by 2027, more than half of enterprise generative AI models will be domain-specific, up from just 1% in 2024. While public attention often focuses on a handful of dominant LLMs, many organizations are also investing in purpose-built models designed for specific business functions or industries, usually available at a fraction of the cost. In insurance, some are optimized for weather forecasting and catastrophe modeling. Others are designed to improve document interpretation or industry-specific reasoning. The big question is no longer which model to standardize on, but which model is best suited for specific tasks.

But what if organizations had the flexibility to quickly swap one model for another without any operational disruptions?

That is what model independence looks like in practice: the ability to keep moving, regardless of what any single vendor decides.

Your competitive advantage does not live inside the model

As access to powerful AI models becomes more widespread, the assumption that competitive advantage lives inside a particular model is increasingly difficult to defend. Most enterprises are working with the same foundational technologies. Over time, access will only become more affordable and more commoditized. What remains difficult to replicate is institutional intelligence.

For insurers, differentiation lives in underwriting philosophy, broker relationships, service strategies, claims expertise, endorsement structures and the rules and guidelines developed over years of operating experience. Historical claims data, internal processes and business context are often far more valuable than the underlying model powering an interaction.

The model itself is simply a tool. Enterprise value lives in the intelligence organizations build around it and how effectively they put it to work.

Building flexibility into enterprise AI

The next phase of enterprise AI adoption requires a different mindset. Rather than managing multiple model relationships, organizations need a single, model-independent platform that allows them to match the AI model necessary to the task at hand.

For many organizations, the answer may lie in finding the right technology partner: one that enables model independence, robust governance and algorithmic transparency, allowing the enterprise to execute the tasks it requires, with both model selection efficiency and control due diligence.

Topics InsurTech Data Driven Artificial Intelligence Carriers