AI Success in Insurance Starts With Strong Data Governance

Unstructured data is rapidly growing across insurance carriers—spread across legacy systems, emails, SharePoint folders, and cloud applications. For claims adjusters, this means that relevant case files, historical claim notes, or sensitive personal data may be scattered, outdated, or insecurely stored. This uncontrolled sprawl poses major compliance and cybersecurity risks, as regulations like NAIC’s Model Law, New York’s 23 NYCRR 500, and California’s CPRA demand tighter control over personal data retention, access, and disposal.

However, unstructured data isn’t just a liability. When governed properly, it becomes a strategic asset—especially with the rise of AI in claims processing and fraud detection. High-quality, compliant, and trustworthy data enables AI tools to deliver faster, more accurate insights. For example, training an AI model to automate parts of claims triage or document classification requires clean, well-labeled input. Poor governance leads to low-quality data and flawed outputs.

For adjusters, this shift has real implications. Better governance can mean faster access to relevant claim histories, reduced administrative duplication, and fewer regulatory headaches. Steps like classifying files, applying retention schedules, limiting access, and deleting ROT (redundant, obsolete, trivial data) help carriers manage risk and enable growth. Starting with a targeted pilot—such as cleaning up a backlog of claims documentation—can demonstrate quick wins and foster wider support.

AI is poised to transform insurance workflows, but its success depends on structured, compliant, and clean data. Claims professionals who understand and support governance initiatives will be better positioned to benefit from these tools while avoiding regulatory pitfalls.