For the past two years, most corporate playbooks could be condensed into three words: deploy AI relentlessly. However, as AI-fueled risks shift into the uninsurable category, leaders are forced to accept this harsher reality. Strangely enough, carriers known for being innovative partners are silently scrubbing tech that might trigger a lawsuit over how the AI makes decisions.
We're seeing more exclusions surface in policies. And the overarching message is that the market has lost its hunger for uncapped AI risk. With that in mind, this post goes behind the curtain to explain how the insurance industry is quietly reframing the AI revolution and what it means for your bottom line.
Emerging underwriting risks
The insurance model circles the idea of familiarity, continually analyzing how closely new risks mirror old ones. In short, underwriters aim to turn uncertainty into calculable risk. While this steadfast model has worked for decades, technology seems to evolve faster than the actuarial dataset used for assessment.
Unfortunately, generative AI doesn't have the extensive claims history or regular loss cycles that underwriters need. These conditions leave underwriting teams hoping AI risks don't shift before the ink dries on freshly written exposure limits—often multi-million dollar limits.
The biggest hurdle is the domino effect—knowing that if one model fails, they all fail. Consider a single-site incident, such as a localized server crash or a faulty hardware batch. These incidents are discreet and typically remain localized. Conversely, a single failure in a popular AI model can act more like digital contagion, impacting thousands of companies simultaneously. One flawed update can propagate through the market, distorting pricing or diagnostics. Unsurprisingly, it becomes an aggregated catastrophe that insurers struggle to price.
Three core pressure points
Inside most carriers, we hear the same three pressure points keep resurfacing—errors, bias, and fraud. None are new, but AI changes their shape and intensity, magnifying risks that once felt containable. Let's review how each pressure point manifests in today's environment.
1. Hallucinations: Confident errors, real consequences
Every profession accounts for human error. What unsettles insurers about AI isn't that it misfires—it's how it misfires. These systems make confident, detailed mistakes at an industrial scale. A model doesn't hesitate or self-correct; it fabricates citations, invents data, and delivers misinformation that sounds believable enough to follow.
Picture a legal assistant tool that fabricates precedent from nothing, citing "cases" that never existed. The fallout can quickly become uninsurable: misplaced trust, professional negligence, and finger-pointing between developers, vendors, and clients. When mistakes repeat through automation, losses don't accumulate linearly—they stack exponentially.
2. Algorithmic bias: Discrimination at machine speed
Bias has long been recognized as an exposure. The difference today is velocity and invisibility. A single human judgment error can cause an isolated claim; an opaque algorithm, embedded in underwriting, hiring, or credit modeling, can bake that bias into thousands of transactions before anyone notices.
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For insurers, this introduces a perfect storm of hidden risk: numerous injured parties, exposure that remains invisible until regulators intervene, and liability that stretches across both human and algorithmic actors. In short, an AI-driven decision engine can quietly accumulate risk until it detonates.
3. Deepfake fraud: When seeing stops believing
Historically, social engineering losses were treated as low-level cyber nuisances. Deepfakes have completely rewritten that playbook. Once voice and video can be forged with (frightening) precision, instinct-based validation—"trust your gut"—no longer applies.
Now, a fraudster can impersonate an executive on a video call, giving real-time authorization for a wire transfer that looks and sounds legitimate. The notion of reasonable verification collapses. In this landscape, insurers are questioning whether traditional social engineering coverage can even function when human detection itself is impossible. As the line between authentic and synthetic blurs, confidence—and coverage appetite—erodes fast.
Implications for coverage strategy
The real fragility lies in legacy policy language—contracts written before "AI risk" existed. Hidden exposure in those clauses can leave carriers unintentionally covering AI-driven losses. Expect a sharp rise in AI-specific exclusions, endorsements, and carve-outs across cyber, E&O, and professional lines.
The bottom line: if coverage terms don't explicitly address AI today, there's a good chance they will be soon—by exclusion, a recent tech insurance trend we've seen play out. And when hallucinations, biased models, or deepfake scams hit, the financial liability often lands right back on the insured's books.
When AI meets accountability
Today, risk transfer isn't automatic—it's earned. Insurers now expect evidence of
A human "kill switch" is no longer optional—it's essential. We call this human orchestration: giving people the authority to pause or redirect AI when needed. It's the bridge between fast innovation and real-world accountability.
If underwriters can't see where humans can intervene, they'll assume things could spiral—and they're often right. Old defenses like email checks or video calls can't stop deepfakes. The new baseline is out-of-band security: callbacks, device verification, and other steps that synthetic identities can't fake.
Managing the uninsurable
Calling AI "uninsurable" sounds extreme, but it's not a call to scrap the tech—it's a call to drop the illusion that AI can be treated like regular software. At this level, it doesn't act like a tool; it behaves more like a powerful executive—brilliant, persuasive, and risky if left unsupervised.
In this environment, the best "insurance policy" isn't a piece of paper—it's strong governance. That means real accountability, transparent oversight, and a mindset that sees AI as a decision-maker, not just a data engine. Insurance still matters, but only for companies ready to manage their algorithms with the same rigor they expect from their top leaders.
