Artificial intelligence (AI) is rapidly reshaping the way claims and litigation professionals manage information, assess risk, and prepare cases. From summarizing deposition transcripts, identifying key medical records, drafting motions to predicting settlement ranges AI-driven tools promise speed and efficiency at a time when caseloads are increasing and experienced talent is stretched thin. Yet with these benefits has come a parallel wave of concern, especially around accuracy, ethics, and defensibility.
Recent court sanctions and publicized missteps involving AI-generated legal filings have underscored a critical reality for insurance defense attorneys and claims professionals: not all AI is created equal. Understanding the difference between public, consumer-grade AI tools and private, enterprise-grade AI platforms is now essential to responsible adoption.
The Appeal and Limitations of Public AI Tools
Public AI platforms such as ChatGPT, Gemini, and Claude have captured headlines for their ability to generate fluent, persuasive text. However, these tools were not designed for claims handling or legal strategy. They operate in open environments, rely on probabilistic text generation, and lack built-in mechanisms to validate citations or ensure outputs are grounded in verified source material. As a result, they can produce responses that sound authoritative but are factually incorrect, a phenomenon widely referred to as AI ‘hallucination.’
The appeal of these tools is understandable. They are accessible, often free or low-cost, and capable of producing polished summaries, draft arguments, and research memos in seconds. For professionals under constant time pressure, that kind of speed is hard to ignore. But ease of access is not the same as fitness for purpose, and in a regulated environment such as insurance claims, the gap between those two things can create serious consequences.
The Real Cost of Getting AI Wrong
For litigation and claims professionals, the risks of hallucinated or unverified content go far beyond embarrassment. Submitting inaccurate analysis, relying on fabricated authority, or exposing sensitive data can trigger regulatory scrutiny, court sanctions, reputational harm, and possible bad faith exposure. These risks are amplified in an environment where courts, regulators, and corporate clients are actively scrutinizing how AI is used in professional decision-making.
Several high-profile cases have already demonstrated what can happen when practitioners submit AI-generated content without adequately verifying its sourcing and accuracy. Attorneys have faced sanctions, firms have had to answer to upset clients, and the affected cases have been delayed, increasing costs. These cautionary tales have accelerated a more serious industry-wide conversation about what responsible AI adoption requires, and who bears the professional accountability when AI gets things wrong.
Why Private AI Platforms Are Different
In response, many insurers, defense firms, and third-party service providers are shifting away from casual experimentation with public AI tools and toward private AI environments specifically built for regulated industries. Private AI platforms are fundamentally different in both design and purpose. Rather than prioritizing conversational breadth, they emphasize control, transparency, and accountability.
Private AI systems operate within closed, encrypted environments, often within a virtual private cloud, ensuring that confidential claims data, transcripts, and records are never exposed to public training datasets. These platforms use secure application programming interfaces (APIs) to access large language models while maintaining strict governance over prompts, outputs, and data flow. Every interaction can be logged, reviewed, and audited, providing a defensible trail of how AI contributed to analysis or decision support.
The Role of Retrieval-Augmented Generation
Equally important, private AI tools increasingly rely on retrieval-augmented generation (RAG). This approach constrains AI outputs to verified, authoritative sources such as deposition transcripts, medical records, court filings, or curated legal databases. Rather than inventing answers, the system responds only within the bounds of the underlying source material, dramatically reducing hallucination risk while increasing confidence in the results.
RAG-based systems essentially keep the AI on a leash, grounding its responses in factual data and documents your team already has vetted and verified. This is a meaningful distinction from general-purpose AI, where the model draws on a vast, unfiltered body of internet text with no guarantee that any particular response is accurate or relevant.
In claims and litigation contexts, where every factual assertion may eventually face scrutiny, this model is a fundamental requirement.
Practical Benefits for Claims and Litigation Teams
For claims and litigation management teams, this shift has practical implications. Properly implemented, private AI can streamline early case assessment, accelerate transcript review, surface inconsistencies in testimony, and support more consistent decision-making, all without compromising ethical standards or data security. The value lies not in replacing professional judgment, but in augmenting it with tools that are built to respect the demands of the legal and insurance ecosystem.
Consider the practical example of a complex multi-claimant case involving hundreds of pages of medical records and deposition transcripts. A well-configured private AI solution can rapidly cross-reference testimony, flag potential inconsistencies, and produce a structured summary that otherwise would take a paralegal or junior attorney days to assemble. That time savings compounds across a large docket.
And when the underlying AI is constrained to verified source material, the output can be trusted with far greater confidence.
Governance, Accountability, and the Path Forward
As AI adoption accelerates across the industry, responsible use is rapidly becoming a differentiator. Organizations that invest in governed, enterprise-grade AI systems send a clear signal to courts, regulators, clients, and insureds that innovation and accountability are not mutually exclusive.
Governance frameworks should address the ways AI tools are selected and vetted, how outputs are reviewed before use, who is responsible for reviewing AI-assisted work product, and how the organization will respond if something goes wrong.
Human expertise must remain firmly at the helm. AI in the claims and litigation environments should never be seen as a replacement for human judgement. The attorney who reviews an AI-generated summary and the adjuster who evaluates an AI-assisted reserve recommendation remain accountable for the conclusions they draw. The goal is to give those professionals better information, more quickly, and with greater consistency. That goal is achievable with the right tools and governance in place.
The path forward is not about avoiding AI altogether, nor about unchecked experimentation. It is about choosing the right tools, establishing clear governance, and ensuring that human expertise remains the final word. In an era where efficiency pressures are intensifying, those who adopt AI thoughtfully will be best positioned to control costs, improve outcomes, and protect the trust that clients, courts and regulators place in them every day.
About the author
Myrna Rembold is senior vice president, enterprise solutions and western region sales for Lexitas. myrna.rembold@lexitaslegal.com