Artificial intelligence has moved past the proof-of-concept phase. Businesses are integrating AI into operations at a record pace, from customer service and logistics to medical diagnostics and HR decision-making. But as the benefits of AI grow, so do the risks, and most companies have not adequately addressed who will bear the legal and financial consequences when things go wrong.
The problem isn't the potential for harm alone. It's that the liability landscape for AI is undefined, shifting and increasingly litigious. When an algorithm produces biased results or a chatbot dispenses incorrect medical advice, it's not always clear who should be held responsible: the business deploying the tool or the developer behind the code. For companies that own or rely heavily on AI, especially those with captive insurance companies, now is the time to scrutinize these risks and evaluate how captives can help fill a widening gap in risk management.
AI failures already have consequences — and lawsuits
The assumption that AI risks are futuristic or theoretical no longer holds. In 2024, a federal judge allowed a class action to proceed against Workday, a major provider of AI-driven hiring software, after a job applicant claimed the platform rejected him based on age, race, and disability. The suit, backed by the EEOC, raises thorny legal questions: Workday argues it merely provides tools that employers configure and control, while plaintiffs claim the algorithm itself is biased and unlawful.
The case highlights the growing legal gray zone around AI accountability, where it's increasingly difficult to determine whether the fault lies with the vendor, the user, or the machine. In another case, an Australian mayor threatened to sue OpenAI after ChatGPT incorrectly named him as a convicted criminal in a fabricated bribery case. The mayor wasn't a public figure in the U.S., and the false output had real reputational consequences.
These incidents are no longer rare. In 2023, the New York Times sued OpenAI and Microsoft for copyright infringement, claiming their models used protected journalism content without permission or compensation. The lawsuit reflects a growing concern in creative and publishing industries: generative AI systems are often trained on datasets that contain copyrighted material. When those systems are then commercialized by third parties or used to generate derivative content, the resulting liability may extend to businesses that integrate those tools.
More recently, the Equal Employment Opportunity Commission issued guidance targeting the use of AI in hiring decisions, citing a spike in complaints tied to algorithmic bias. The guidance emphasized that employers, not vendors, would typically bear responsibility under civil rights laws, even when the discriminatory impact stems from third-party software.
These examples reveal a pattern. AI is being used to make decisions that carry legal weight, and the consequences of failure (reputational, financial and regulatory) often fall on the business deploying the system, not just the one that created it.
A legal and regulatory framework is forming
The global regulatory environment is evolving quickly. In March 2024, the European Union formally adopted the EU AI Act, the first comprehensive legal framework for artificial intelligence. The law classifies AI systems into four risk categories--unacceptable, high, limited and minimal--and imposes stringent obligations on businesses using high-risk systems. These include transparency, human oversight and data governance requirements. Noncompliance (related to high-risk AI systems) can lead to fines of up to 7% of a company's global annual revenue.
While the U.S. lacks a national AI law, states are moving ahead with sector-specific rules. California's proposed Safe and Secure Innovation for Frontier Artificial Intelligence Models Act would require companies to test for dangerous capabilities in large language models and report results to state authorities. New York's Algorithmic Accountability Act aims to address bias in automated decision tools. Several federal agencies, including the FTC and the Department of Justice, have also made it clear that existing laws, from consumer protection to antitrust, will apply to AI use cases.
In Deloitte's Q3 2024 global survey of more than 2,700 senior executives, 36% cited regulatory compliance as one of the top barriers to deploying generative AI. Yet less than half said they were actively monitoring regulatory requirements or conducting internal audits of their AI tools. The gap between risk awareness and preparedness is widening, and businesses with captives are in a unique position to act.
The role of captives in addressing AI liability
Captive insurance companies are not a replacement for commercial insurance, but they provide an essential complement, particularly for complex, fast-evolving risks that the traditional market is hesitant to underwrite. AI liability falls squarely into that category.
For example, a captive can help finance the defense costs and potential settlements tied to AI-generated errors that fall outside the scope of cyber or general liability policies. This might include content liability for marketing materials created using generative AI, or discrimination claims stemming from algorithmic hiring tools. In some jurisdictions, captives may even fund regulatory response costs or administrative fines where allowed.
Captives can also provide coverage when a third-party AI vendor fails to perform as promised and indemnification clauses prove insufficient. In such cases, a captive can reimburse the parent company for business interruption or revenue losses that stem from the vendor's failure: a growing risk as more companies integrate third-party AI into core workflows.
Because captives are owned by the businesses they insure, they offer flexibility to craft tailored policies that reflect the company's actual AI usage, internal controls and risk tolerance. This is particularly valuable given how little precedent exists in AI litigation. As case law develops, businesses with captives can adjust coverage terms in near real time, without waiting for the commercial market to adapt.
Building AI into captive strategy
To incorporate AI risk effectively, captive owners must begin with a clear-eyed assessment of their own exposure. This requires collaboration across legal, compliance, IT, risk management and business units to identify where AI is in use, what decisions it influences and what harm could result if those decisions are flawed.
This analysis should include:
- Inventorying all internal and third-party AI systems
- Mapping potential points of failure and legal exposure
- Quantifying financial impact from regulatory enforcement, litigation or reputational damage
- Evaluating existing insurance coverage for exclusions or gaps
- Modeling worst-case outcomes using internal data or external benchmarks
Once this assessment is complete, captive owners can work with actuaries and captive managers to design appropriate coverage. This may include standalone AI liability policies or endorsements to existing coverages within the captive. It may also involve setting aside reserves to address emerging risks not yet fully insurable under traditional models.
Risk financing alone is not enough. Captives should also be part of a broader governance strategy that includes AI-specific policies, employee training, vendor vetting and compliance protocols. This aligns with the direction regulators are taking, particularly in the EU, where documentation, explainability and human oversight are mandated for many high-risk systems.
Boards are paying attention
AI is no longer just a back-office issue. In 2024, public companies and shareholders sharply increased their focus on artificial intelligence, especially on board-level oversight and shareholder proposals. According to the Harvard Law article AI in Focus in 2025: Boards and Shareholders Set Their Sights on AI, the percentage of companies providing some disclosure of board oversight grew by more than 84% year over year and more than 150% since 2022. This trend spans all industries. Meanwhile, shareholder proposals related to AI more than quadrupled compared with 2023, mostly calling for greater analysis and transparency around AI's impact.
This intensifying scrutiny signals a clear mandate for risk managers and captive owners to deliver solutions. Captives offer companies a flexible tool to fund, control and adapt their responses to the rapidly evolving AI risk landscape and regulatory environment.
Conclusion
AI is changing how businesses operate, but also how they are exposed. As regulatory frameworks tighten and litigation accelerates, businesses must prepare for the reality that AI-related liability is no longer speculative. Captive insurance companies offer a powerful tool to manage that exposure, not by replacing traditional coverage, but by addressing what lies outside its bounds.
For companies that rely on AI, the question is no longer whether liability will emerge–it's whether they are positioned to handle it. Captives provide a path forward, giving businesses the ability to design, fund and control risk management strategies that evolve as fast as the technology they are built to protect.
