For years, “digital transformation” in insurance has meant migrating to the cloud, deploying analytics, and automating back-office processes. But a deeper shift is now emerging—one that could redefine the very foundation of what insurers cover.
Imagine a future where AI models themselves are insured — much like cars, homes, or even human health. As artificial intelligence becomes inseparable from how societies function, a new category of risk is rising: the risk of AI failure. Providing coverage for these algorithmic risks could transform the insurance industry as profoundly as the introduction of auto and health insurance did in the last century.
Why AI Models Might Need Insurance
AI systems are now embedded in life-critical and business-critical functions—from medical diagnosis to autonomous driving, financial decision-making, and cybersecurity defense. But they are not infallible. A self-driving car might misread a signal. A diagnostic algorithm could miss a tumor. A generative AI chatbot might give dangerously wrong advice or deny a legitimate claim.
These errors already occur. The Stanford AI Index has reported a staggering 2,500% increase in AI-related incidents and controversies since 2012—proof that AI failures are neither theoretical nor rare.
If an AI model’s error can cause financial loss, reputational harm, or even injury, why wouldn’t we insure against it? Just as businesses carry liability coverage for human or mechanical errors, tomorrow’s organizations may carry AI liability and performance insurance—policies that pay when an algorithm makes a costly mistake.
Possible coverages include:
- Liability for AI errors that cause bodily injury, property damage, or financial loss.
- Performance guarantees that compensate clients if a model underperforms or “hallucinates.”
- Business interruption coverage for losses caused by AI system downtime or malfunction.
Lessons from Auto and Health Insurance
The idea of insuring AI may sound futuristic, but it follows a familiar pattern: each technological leap creates new categories of insurable risk.
Automobile Insurance
When cars first appeared, there was no concept of motor insurance. But as roads filled with vehicles and accidents rose, liability policies became essential. By the 1930s, many governments made car insurance mandatory. This new class of risk forced insurers to innovate—building actuarial models, safety incentives, and entire regulatory systems around driver and vehicle risk. Today, auto insurance remains one of the industry’s largest segments.
Health Insurance
Health insurance followed a similar trajectory. Early hospital-prepayment plans in the 1920s evolved into today’s global health-insurance industry, reshaping how societies manage medical costs and risks. What began as protection against hospital bills became a cornerstone of modern welfare systems.
Both examples show that when a new domain of risk becomes widespread, insurance adapts, grows, and ultimately transforms. AI may be the next such frontier.
What It Means to Insure an AI Model
So what does AI insurance actually look like?
1. AI Liability Coverage
This protects against claims that an AI system’s decision caused harm. For instance, if a machine-learning model denies a mortgage application unfairly or an autonomous vehicle crashes due to a software fault, liability could fall on the developer or operator. Global reinsurers have already launched dedicated AI liability policies, covering algorithmic bias, negligence, or model malfunction.
2. Performance and Robustness Guarantees
Some insurers now back AI performance warranties—where the insurer compensates a client if an AI system fails to meet promised accuracy levels. Underwriters validate the model’s robustness and data integrity before coverage, introducing a new discipline: AI model validation as underwriting.
3. AI Failure and Downtime Coverage
If a core AI system collapses—say, an algorithm managing inventory, trading, or energy dispatch—the financial damage can be immense. Emerging AI insurance products already include business interruption protection for such failures, effectively treating algorithmic malfunction like equipment breakdown.
4. Specialized AI Risks
Beyond failure and liability, AI introduces risks tied to bias, IP infringement, data privacy, and regulatory compliance. Many current insurance contracts are “silent” on AI—neither including nor excluding it—which leaves dangerous coverage gaps. Expect future policies to explicitly define AI-related events, or even exclude them unless a dedicated AI policy is purchased.
How AI Insurance Could Transform the Industry
The implications extend far beyond adding a new line of business. Insuring AI could reshape the insurance industry’s role, expertise, and relationships in several ways.
1. A New Multi-Billion-Dollar Market
Analysts project that AI insurance could generate up to $4.7 billion in annual premiums by 2032, with potential to exceed $140 billion globally by 2034. It may become as ubiquitous as cyber insurance is today—opening new growth for insurers while cushioning declines in traditional lines.
2. A New Underwriting Paradigm
Traditional actuarial models rely on historical loss data—something AI lacks. Underwriters will need new methods: scenario simulations, algorithmic audits, and probabilistic modeling. Expect the rise of the AI actuary, blending data science with classical risk modeling to quantify how algorithms behave under uncertainty.
3. Risk Mitigation and AI Governance
Insurers will demand that clients implement strong AI governance frameworks before coverage—just as cyber insurers now require multi-factor authentication and data-backup practices. This could make insurers de facto regulators of AI safety, pushing companies toward independent audits, bias testing, and human oversight.
4. Cross-Industry Partnerships
AI insurers and tech providers are already beginning to collaborate. We may soon see AI systems sold with embedded insurance—“Insured AI-as-a-Service”. Such partnerships would blur the line between product warranty and risk transfer, making insurance part of AI’s value proposition.
5. Influence on Law and Regulation
As lawmakers wrestle with AI accountability, insurance could become a key compliance mechanism. Future regulations may even mandate AI liability coverage for certain use cases (autonomous vehicles, medical AI, or financial advisory tools). Conversely, voluntary AI insurance could serve as a trust signal, proving that a company stands behind its algorithms.
6. Challenges Ahead
Early AI insurance faces steep hurdles: limited claims data, systemic risk (a single AI flaw affecting thousands of firms), and unclear legal liability. Like cyber insurance a decade ago, the market will evolve through trial and error. Transparency, incident reporting, and data sharing will be critical to making AI insurance viable.
From Insuring Cars and Health to Insuring Algorithms
We may be witnessing the dawn of a new insurance epoch—one that moves from protecting physical assets and people to protecting algorithms and data-driven decisions.
In the coming decade, companies may find it as essential to insure their AI as they do their vehicles or staff. Insurers, in turn, will evolve into guardians of AI trust, underwriting not just financial loss but the credibility of algorithmic systems themselves.
Far from being disrupted by technology, the insurance industry could once again become its enabler—absorbing the new risks that power progress.
As one industry report aptly put it: “Just as cyber insurance protects the digital economy, AI insurance will safeguard the economic value created by artificial intelligence.”
The future of insurance may not only be digital—it may be intelligent.
This article is part of LAMAH Intelligent Solutions’ ongoing research on AI transformation, digital trust, and governance innovation.
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Disclaimer:
The views and information expressed in this article are provided for general informational and educational purposes only and do not constitute professional, legal, financial, or investment advice. LAMAH Intelligent Solutions and the author(s) make no representations or warranties as to the accuracy, completeness, or suitability of the information contained herein and accept no liability for any loss or damage arising from reliance on it. Readers are advised to seek independent professional advice before making any decisions based on this content.
