AI is changing how businesses operate, make decisions, and serve customers across insurance and virtually every industry. At the center of this shift is a technology called the large language model, or LLM. If you've used ChatGPT, Claude, Gemini, interacted with an AI-powered customer service tool, or noticed that Outlook is now suggesting entire sentences as you’re writing them, you've already encountered an LLM in action.
But what exactly is a large language model? And how does it connect to the more sophisticated concept of agentic AI that is increasingly driving enterprise transformation in insurance and elsewhere?
A large language model is an advanced type of artificial intelligence trained on vast amounts of text data to understand, generate, and respond to natural language/everyday language. It uses language patterns to answer questions, draft documents, and more – all without being explicitly programmed for each scenario.
When prompted, LLMs can rapidly interpret details, connect them to similar past situations, and provide a well-informed response, even when a specific scenario is new. They power a wide range of business use cases:
Extracting key data from documents like submissions, loss runs, and policies
Drafting communications
Answering complex questions
Classifying and routing information
Summarizing large documents
In insurance, these capabilities translate directly to submission processing, reviewing loss runs, FNOL documentation, and other labor-intensive, content-heavy, and error-prone tasks traditionally performed manually.
LLMs are remarkably capable, but they're not automatically ready for enterprise deployment, especially in regulated industries like insurance where governance is a regulatory requirement. There are real limitations worth understanding.
General LLMs were trained on broad internet data, not insurance documents. That gap shows up fast when you ask them to process a loss run or extract data from an ACORD form.
The governance gap is just as significant, and it becomes more consequential at scale. When LLMs power automated, high-volume business workflows, the absence of a governance layer turns manageable limitations into serious operational risk.
A way to think of it is that a new adjuster might know insurance theory, but you wouldn't put them on complex claims without oversight. AI works the same way.
Without a transparent accountability framework or a process for flagging uncertainties, LLMs lack the necessary guardrails for effective collaboration with human experts or for powering agentic AI.
Even LLMs trained on industry-specific data can hallucinate, meaning generating responses that sound authoritative but are factually incorrect. Without effective governance, AI models can’t explain their reasoning or retain records that would satisfy an insurance compliance officer, state regulator, or auditor.
Truly enterprise-ready LLMs have transparency, accountability, and governance. The key enabling capabilities that make AI accurate, safe, and secure include:
Confidence scoring: One of the most critical – rather than simply producing an output, an AI system should be able to indicate how confident it is in that result. A model that flags low-confidence responses for human review is far more trustworthy than one that presents every answer with equal certainty. In insurance, this matters especially in underwriting and claims, where a confidently wrong answer can mean a mispriced risk or an improper coverage determination.
Human-in-the-loop (HITL) design: Makes intelligent collaboration possible by ensuring people are in command of the decision-making process. AI handles volume, speed, and pattern recognition, while human experts handle exceptions, edge cases, and apply judgment in situations requiring experience and context.
Auditability: Every decision or output can be reviewed after the fact to identify who or what made a decision, when it was made, which inputs were involved, and other factors. In regulated industries like insurance, the ability to answer these questions is a legal and operational requirement.
Traceability and explainability: These take auditability further by tracing data and decision histories from development to deployment, providing the reasoning behind specific model outputs. Both are critical for building trust with insurance regulators, carriers, and policyholders.
Model curation: AI models need ongoing care to stay accurate. That means monitoring for performance drops over time, retraining on new data, testing for bias, and making sure any updates are validated before they go live.
Together, these capabilities empower governance, but they don't replace the human judgment and organizational accountability that enable scaling AI. Enterprise AI readiness is a balance of technology and human expertise. Defining workflows, setting thresholds for human review, and maintaining ongoing oversight are organizational responsibilities that no technology can replace. For insurers, this means deciding which workflows AI owns, which it assists, and where a licensed professional must always have final sign-off.
Agentic AI refers to AI systems that can plan and execute multi-step tasks, interact with other systems, and make decisions within defined parameters, all with minimal human intervention.
LLMs are foundational to agentic AI, providing the linguistic and reasoning capabilities that make agentic behavior possible. In insurance, a single automated workflow can touch policy systems, compliance rules, and customer records all at once. That’s why governance isn’t something you add later. It has to be built in from the start.
Large language models are the intelligence foundation for today's most capable agentic AI frameworks. They are extraordinarily capable – but that capability needs governance before it can be responsibly integrated into insurance workflows. The insurers who understand this distinction and invest accordingly won't just move faster – they'll make better decisions, stay compliant, and earn the trust of regulators and policyholders alike.