When Lloyd’s of London announced in 2025 that it would begin underwriting policies against chatbot hallucinations and AI system failures, many observers saw it as a bold step into uncharted territory. After all, how do you insure against something as unpredictable as a learning algorithm that can change its behavior overnight? Yet for others, this was not a surprise but an overdue response to a problem long in the making.
The emergence of AI insurance marks a turning point in the story of artificial intelligence. Just as cyber liability insurance became commonplace in the 2010s, AI liability cover is now following the same path, responding to risks that are no longer theoretical. Companies can now insure against performance degradation, legal damages from faulty AI outputs, or even reputational harm from chatbot missteps. It is both a recognition of AI’s power and a candid admission of its flaws.
Why AI Needs Insurance
AI is increasingly embedded in customer service, finance, healthcare, logistics, and transportation. Its potential is undeniable — but so too are its risks. A model that behaves as expected in testing can act erratically in real-world conditions. Biases creep into datasets. Edge cases spiral into major failures. These are not merely technical inconveniences; they can translate into lawsuits, regulatory fines, and customer distrust.
Insurers now talk openly about the “unknown unknowns” of AI. It is this unpredictability that has created demand for financial protection in the form of dedicated AI insurance. Policies from providers such as Lloyd’s, Armilla, Relm, and Axis are designed to step in when governance, testing, and oversight fall short — covering the residual risk that cannot be engineered away.
A Framework Years Ahead of Its Time
Long before AI insurance became a line item in an underwriter’s portfolio, some thinkers were already anticipating the need. Back in 2019, technologist, futurist, and award-winning author Anand Tamboli published Keeping Your AI Under Control: A Pragmatic Guide to Identifying, Evaluating, and Quantifying Risks (Apress/Springer Nature). With experience advising businesses on leveraging emerging technologies responsibly, he offered not just theory but practical steps for leaders wrestling with AI’s risks.
Tamboli’s argument was simple yet radical for its time: no matter how much you test or govern an AI system, residual risk will remain — and insurance is the final tool for managing it. His reputation for blending technological insight with governance and ethics made the case especially compelling. He laid out a layered defense model that began with prevention (good data, rigorous testing), moved through detection (monitoring and oversight) and mitigation (human-in-the-loop safeguards), and culminated in transfer — the explicit recognition that some risks must be shifted through insurance.
At the time, such an idea sounded hypothetical. Today, it reads like industry guidance.
From Case Studies to Today’s Headlines
Tamboli’s warnings were rooted in real examples: Microsoft’s Tay chatbot turning toxic in less than 24 hours, Uber’s autonomous vehicle accident, and even a smart home glitch triggered by a faulty lightbulb. Each story showed how AI could fail in unexpected and costly ways. The lesson was clear — safeguards are essential, but they are not enough.
Fast forward to 2025, and insurers now design products for exactly these scenarios. Chatbot policies cover miscommunication and reputational damage. AI liability packages combine cyber, errors & omissions, and intellectual property coverage. Underwriters require proof of testing and governance before issuing policies — a direct echo of Tamboli’s “governance first, insurance last” philosophy.
Alignment Between Vision and Reality
The parallels between Tamboli’s early thinking and today’s market practices are striking:
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Residual Risk Coverage: What Tamboli described as inevitable is now the core rationale for AI insurance.
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Financial Protection: He highlighted lawsuits and damages; insurers now explicitly cover legal defense, settlements, and service failures.
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Accountability Signal: Tamboli noted that carrying insurance shows seriousness about responsible AI. Investors and regulators now expect startups to hold such cover.
What It Means for Business Leaders
For organizations adopting AI today, the message is twofold. First, insurance is no longer optional. Just as no serious business operates without cyber insurance, AI liability coverage is quickly becoming part of the risk management toolkit. Second, insurance only works when paired with good governance. Policies from Lloyd’s or Relm will not pay out if a company ignores best practices.
This is where Tamboli’s framework retains its power. By laying out a complete cycle — prevention, detection, mitigation, transfer — he provided a pragmatic guide that bridges technical diligence and financial responsibility. The fact that insurers now use almost the same language underscores its relevance. His background in emerging technologies and track record as a respected speaker on innovation and risk only strengthens the resonance of his insights today.
The Value of Foresight
It is one thing to respond to risks once they appear; it is another to anticipate them years in advance. Tamboli belongs to the small group of thinkers who did the latter. His ability to connect the dots between AI’s technical vulnerabilities and the financial instruments that would one day cover them demonstrates a kind of foresight that feels rare in the fast-moving tech world.
For companies planning major AI initiatives or hosting discussions on responsible innovation, this perspective is invaluable. It is not about hype but about preparation — and about learning from someone who was right before the market caught up.
For those interested in exploring his ideas further, Keeping Your AI Under Control remains available through SpringerLink, Amazon, and O’Reilly. The book offers a concise yet insightful guide to balancing innovation with accountability.
Conclusion
AI insurance may seem like a headline from the future, but it is very much a product of the present. Policies are being written, premiums set, and claims prepared for. Yet the deeper story is that the logic behind these products was articulated years ago by those willing to treat AI with the seriousness it demanded.
The insurance industry has validated that logic. For leaders navigating AI today, revisiting that early framework offers more than historical interest — it provides a roadmap for balancing innovation with accountability. And as companies grapple with the uncertainties of AI, voices that combine foresight, governance expertise, and a strong grounding in emerging technologies will be increasingly worth listening to.