AI is Transforming Life Sciences. AI Insurance Coverage Hasn’t Kept Up.

Artificial intelligence (AI) is no longer experimental in life sciences. Medtech innovation is increasingly centered on AI-driven diagnostics and AI-enabled platforms, as organizations move from pilot projects to embedding AI across operations.

In drug discovery, 2026 has become a turning point, with so-called “digital twins” moving from experimentation to standard practice in clinical development.

That pace of adoption is creating something the life sciences insurance market has not yet resolved: a growing number of uncovered AI-related liabilities.

The coverage question worth asking

For a life sciences company deploying AI in drug discovery, clinical data analysis or diagnostic tools, the question of how to cover AI-related exposure is not straightforward. The challenge is that AI-related claims don’t map cleanly onto existing policy structures. Product liability, technology errors and omissions (E&O), cyber and professional liability can each address part of the picture — but none was designed with AI in mind, and the gaps between them are real.

In life sciences, those gaps are particularly acute. An AI tool used to assist in clinical diagnostics sits at the intersection of product liability, professional liability and potentially medical malpractice — depending on how the output is used and who relies on it. For instance, an algorithmic error in a manufacturing quality control process could generate contingent product liability exposure. An AI-assisted research deliverable that produces a flawed result for a sponsor client could trigger an E&O claim, which is precisely the type of exposure that professional liability insurance for AI is designed to address.

Coverage for each of these scenarios differs. Life sciences organizations that map those exposures to their current program are significantly better positioned than those discovering the gaps when a claim arises.

How the insurance market is responding

The insurance market is evolving in response to AI risk, and understanding the current state of AI insurance coverage helps organizations build resilient programs.

Many carriers are adopting AI exclusion insurance policy language, including in management and professional liability policies, directors and officers (D&O), employment practices and fiduciary liability coverage. That expansion means the exposure question extends well beyond product and clinical liability lines into governance and leadership coverage.

At the same time, the market is developing new solutions. Specialized AI liability insurance coverage structures are emerging, including policies designed specifically for organizations with third-party liability exposure from AI use where existing programs exclude it. Life sciences organizations engaged in that conversation now have more options available to them.

What forward-thinking life sciences organizations are doing

The organizations navigating this environment most effectively approach AI risk the same way they approach other complex exposures — they map existing structures against what they truly need and close the gaps.

That starts with understanding how AI is being used across the organization — in research and development (R&D), quality control, clinical data analysis and diagnostics — and identifying which exposures each one creates. An AI tool embedded in a product workflow creates different liability than one used in an internal research process. The coverage should reflect that distinction.

It also means reviewing current programs for AI exclusions being added at recent renewals and assessing whether the organization needs technology E&O insurance, cyber liability or emerging AI-specific coverage products to address what standard programs no longer cover.

For life sciences service providers such as Contract Research Organizations (CROs), Contract Manufacturing Organizations (CMOs) and laboratory services firms, contractual obligations to sponsor clients compound the question. Service providers reviewing their coverage structures in light of AI adoption are significantly better positioned to protect those relationships and manage the third-party financial loss exposure that AI-assisted deliverables can create.

The governance dimension

Coverage is one part of the answer. AI governance risk management is the other. Insurers increasingly evaluate AI governance practices — whether organizations have controls, documentation and oversight mechanisms in place — when determining coverage applicability for AI-related losses.

Life sciences organizations that build structured AI governance risk management into their operations are not only managing risk more effectively but also becoming more insurable. Those investing in governance, documentation and oversight are finding that these practices translate directly into better coverage terms and stronger market relationships as the life sciences insurance landscape matures.

Moving forward

The right conversation is not whether you’re using AI. It’s whether your insurance coverage reflects the exposures AI creates — and whether it will hold up when you need it most.

Connect with HUB’s Life Sciences practice to build risk strategies that are as precise as the services you deliver.