AI Cements Its Grip on Insurtech as Liability Questions Mount
Artificial intelligence now commands a near-total share of global insurtech investment, with 95.2% of Q1 2026’s $1.63 billion in funding directed at AI-focused companies — a dominance that is reshaping not only how industry capital is deployed but what risks insurers will need to cover next, according to Gallagher Re’s Q1 2026 Global InsurTech Report.
“To all intents and purposes, ‘AI’ and ‘InsurTech’ now form a Venn diagram whose circles overlap almost completely. And for good or bad, insurtech is the label that we have for tech and/or AI as applied to the insurance industry,” commented Andrew Johnston, Global Head of InsurTech, Gallagher Re, and editor of the report. “It is a term that has always encompassed a complex landscape of applied technologies, developed and deployed by (re)insurers, tech companies, and new start-up businesses. In that sense, AI is merely the latest innovation on the insurtech block — but it is certainly a transformational one.”
Capital Chases the AI Wave
The first quarter of 2026 delivered one of the strongest insurtech funding periods in years, with total investment holding roughly steady from Q4 2025’s $1.67 billion — ranking among the two highest quarters for global insurtech funding since late 2022. But that headline figure understates the structural shift happening beneath the surface.
All 10 of the quarter’s largest deals went to AI-focused companies. AI-centered insurtechs raised $1.55 billion across 68 deals, with an average deal size of $25.79 million, slightly above the broader insurtech average. Overall, the mean insurtech deal size climbed 23.3% quarter-over-quarter to $23.23 million — the highest since Q4 2021 — even as deal volume dipped from 102 to 81 transactions.
Early-stage activity was particularly robust, with funding surging 36.1% quarter-over-quarter to $548.5 million, the highest level since Q3 2022. The average early-stage deal hit $14.06 million, a 278.8% year-over-year jump from Q1 2025’s recent low. One standout was Corgi, a commercial insurer focused on startups, which raised $108 million in a Series A — only the sixth early-stage insurtech ever to clear the $100 million mega-round threshold.
Life and health insurtech funding nearly doubled quarter-over-quarter to $718.99 million. The two largest L&H deals of the quarter went to full-stack health insurers Devoted Health ($318 million Series F extension) and Alan ($116 million Series G), while AI-focused health care brokerage Gyde rounded out the top five with a $60 million Series A.
Insurtechs specifically relevant to AI liability and cyber insurance themes raised $444.84 million in the quarter, concentrated in four top-10 deals: Corgi ($108 million), GenLogs ($60 million), Indigo ($50 million), and Harper ($45 million).
Since 2012, companies operating across the digital and cyber risk space have collectively raised $5.77 billion across 263 deals — a track record that frames AI liability insurance as the next evolutionary chapter of a well-established investment theme, not a sudden departure from it.
From Investment to Exposure — AI’s Growing Liability Problem
Rising investment in AI-powered tools is running in parallel with mounting evidence that AI systems are generating real, and often uninsured, losses. A Gallagher survey of corporate AI adoption found that by the end of 2025, 63% of businesses had fully or partially operationalized AI, up from 45% the prior year. Yet fewer than half have adopted formal risk management frameworks, which is a gap with direct insurance implications.
One in five insurance industry professionals surveyed reported that a client had experienced a loss or claim tied to AI-related risks in the past year, with just over half of those claims fully covered by existing insurance. Cyber liability led the field in terms of where practitioners expect AI-related losses to emerge (33%), followed by product liability (30%) and employment practices liability (23%).
Those figures are coming into sharper focus as courtrooms fill with AI-related disputes. More than 200 active legal cases involving AI and machine learning are now working through the system, touching issues of data bias, privacy liability, discrimination, and regulatory non-compliance — spanning cyber, EPL, product liability, and errors and omissions coverage lines, the report noted.
The sources of AI exposure are varied. Businesses face potential liability from algorithmic bias, harm caused by false or misleading AI outputs, unauthorized use of data or intellectual property, and regulatory penalties for non-compliant deployments. The “build vs. buy” decision — whether to develop proprietary AI or license third-party tools — adds another layer of complexity, particularly around who ultimately bears responsibility when a system fails.
Meanwhile, most existing business insurance policies weren’t designed with these exposures in mind, with many wordings effectively drafted for “a pre-AI world.” The parallel to cyber insurance is instructive: just as the market spent years wrestling with “silent cyber” exposures embedded in traditional policies, AI risks are now surfacing across existing coverages that lack the language to address them clearly.
Survey respondents broadly expect the industry to respond through new AI-specific policies (40%), specialized endorsements (40%), adapted coverage wordings (37%), and updated renewal questionnaires (36%).
Navigating What Comes Next
The trajectory for AI liability insurance follows a now-familiar arc: nascent today, significant tomorrow. The cyber market’s evolution from silence through exclusion to affirmative, standalone coverage offers a useful — if imperfect — roadmap, and the speed of AI adoption may compress that timeline considerably, the report said.
For now, AI liability products could logically take root within existing cyber teams at insurers, reinsurers, and brokers, where expertise in digital risk, threat actors, and technical infrastructure failures already runs deep. The concentration of technology providers and cloud platforms creates shared vulnerability across both cyber and AI risks, where an isolated failure can rapidly escalate into a systemic event — a dynamic already familiar from recent cloud outages affecting multiple industries simultaneously.
Meaningful legal and regulatory clarity will be required before the market matures. How courts come to define AI-related “loss,” how policy language evolves to capture AI-specific exposures, and how significant claims shape reinsurance appetite will all be determinative factors. A broader structural question looms behind all of this: whether AI liability, cyber, product liability, and general liability will ultimately converge into a single “digital risks” category — one that better reflects how interconnected these underlying exposures already are.
Obtain the full report here. &

