Tego warns of AI monoculture risk as insurers struggle to cover healthcare AI failures
Healthcare insurers face a coverage crisis as artificial intelligence spreads through hospitals and clinics without corresponding liability frameworks, according to Tego Chief Executive Officer Eric Lowenstein. Speaking at a healthcare conference in London, Lowenstein warned that existing policies built around human error do not address the unique exposures created by AI systems.
The problem is immediate. GenAI-related lawsuits in the United States grew 978 per cent between 2021 and 2025, with year-on-year growth of 137 per cent in 2024-25 alone.
Traditional insurance language assumes a single point of fault - a clinician, a hospital, a patient. AI changes that structure entirely. When a hospital deploys a third-party triage tool that misses chest pain in older women, liability chains now stretch across patient, clinician, hospital, software vendor, and the foundation model provider.
"Insurance has spent 200 years pricing one thing: human error. AI does not fit that frame," Lowenstein said. "It is the first time our industry has had to ask not just who is at fault, but whether the concept of fault still applies."
The coverage gap
Most medical malpractice, cyber, and technology policies predate the current wave of AI deployment. They contain no explicit coverage for AI-related harm, leaving boards exposed if they assume existing policies will respond to losses.
In a hospital class action scenario Lowenstein described, each insurer points to exclusions. The medical malpractice carrier says the AI made the call, not a clinician. Cyber insurers see no data breach. The vendor's tech errors and omissions policy excludes bodily injury. Product liability disputes whether AI qualifies as a "product" in that jurisdiction. The hospital sits beneath all of it, uninsured.
"Silence is exposure," Lowenstein said. "Boards should not assume their existing policies cover AI risk. Most do not address it at all."
Two emerging risks
Lowenstein identified the "look-back problem" as a distinct exposure. When a healthcare provider tests an AI system against historical radiology scans and discovers possible missed findings in past cases, the act of looking itself creates liability. Validating new tools against old records opens doors to claims that did not exist before the validation occurred.
The second risk is what Lowenstein calls "AI Monoculture Risk" - the concentration of many software tools on a small number of foundation models. If a defect emerges in one widely used model, claims could trigger simultaneously across thousands of unrelated insureds, across industries, across countries, and across policy lines.
"In farming, one disease can wipe out the entire harvest when everyone plants the same crop," Lowenstein said. "A single defect in one widely used foundation model could trigger claims across thousands of unrelated insureds simultaneously. That is a fundamentally different shape of risk to anything our industry has priced before."
Limited specialist cover
Dedicated AI liability products remain scarce. Approximately seven specialist AI liability products exist in the global market, most placed at Lloyd's of London, with significant variation in cover, triggers, and exclusions.
Tego is developing a standalone AI liability product for the Australian market aimed at AI providers, vendors, and companies deploying AI applications. The cover targets exposures including inaccurate outputs, intellectual property disputes, model drift, data leakage, and regulatory breaches.
Deloitte projects global AI insurance premiums could reach USD $4.8 billion by 2032, signaling growing demand as organizations adopt AI while insurers work out how to underwrite the risk.
Governance failure
Lowenstein ended with a challenge to boards. Most AI deployments receive approval based on upside potential. Few have contingency plans for failure.
"When the AI fails, harms a patient or stops working, who is accountable, who picks up the cost, and is anyone insured for it? Most boards cannot answer that. That is the governance failure," he said.
For insurance professionals, the message is clear: existing frameworks do not address AI risk. Organizations deploying AI in healthcare settings need dedicated coverage, and insurers need to understand these exposures before claims arrive.
Learn more about AI for Insurance and AI for Healthcare to understand how these systems are being deployed and where risks emerge.
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