There is a growing trend on LinkedIn and across insurance circles to use the term “AI risk” as if it describes a single coherent category of loss. The phrase is understandable, because artificial intelligence is new enough to feel distinct, broad enough to touch nearly every industry, and unique enough to make familiar losses occur in unfamiliar ways, but the phrase is imprecise, and that matters.
When people refer to “AI risk,” they may be talking about a chatbot giving inaccurate advice to a customer, an algorithm screening out job applicants in a discriminatory way, a deepfake used to trick a finance employee into wiring funds, an autonomous device causing bodily injury, or generative AI producing content that infringes someone else’s intellectual property. Each of those examples involves AI, but they are not the same risk. In one scenario, the problem may be professional negligence. In another, it may be employment discrimination. In another, it may be fraud, bodily injury, privacy injury, intellectual property infringement, breach of contract, or a defective product.
AI may explain how the loss occurred, but it does not necessarily tell us what kind of loss occurred.
Insurance depends on that difference. Policies are not built merely around the tools an insured uses; they are built around covered causes of loss, legal theories, damages, exclusions, conditions, and triggers. A new tool can certainly alter the frequency or severity of a claim, and AI may do that more dramatically than many other tools, but the presence of AI does not automatically transform every resulting claim into a new species of loss.
This is where definitions become useful and keep us disciplined. A peril is a cause of loss, such as fire, windstorm, collision, theft, or a falling object. A tool is the instrument used to perform an action, whether that instrument is a spreadsheet, a hammer, a software platform, a vehicle, a stethoscope, a pen, or an AI model. A consequence is the harm that follows, such as financial loss, bodily injury, property damage, privacy injury, reputational harm, regulatory penalty, lost opportunity, or defense costs.
The mistake is confusing the tool with the peril.
A hammer is a useful analogy here. A hammer can be used to frame a house, break a window during a robbery, injure another person, damage property after being dropped from a job site, or help construct a structure that later fails because the work was defective. In each case, the hammer is relevant to the facts, but we do not ordinarily treat “hammer risk” as a standalone insurable peril. We look instead to the actual claim: bodily injury, property damage, faulty workmanship, theft, negligence, or some other covered event that produces a legally recognizable harm.
AI should be treated with the same discipline, even though the technology is far more complex and the consequences may be less intuitive.
This does not mean AI is irrelevant. It can materially change an insured’s risk profile because it can increase frequency by allowing more activity to happen faster, increase severity by scaling one error across thousands or millions of iterations, and can be difficult to monitor, audit, or unwind. A poorly governed AI tool can turn a flawed hiring model into systemic employment discrimination machine that discriminates in mass before the insured is aware.
The point is that AI often modifies existing exposures. It does not define the peril.
The better question is not whether a policy “covers AI,” because that question is usually too broad to be useful. The better question is what injury occurred, what legal theory is being alleged, what damages are being sought, and which part of the insured’s insurance program was intended to respond to that kind of claim.
If the allegation involves unauthorized access, a privacy breach, ransomware, or system intrusion, the analysis likely begins with cyber. If the allegation involves bad advice, misrepresentation, poor work product, or failure to meet a professional standard, the analysis likely moves toward professional liability. If an AI product, SaaS platform, software tool, or technology service fails to perform as promised, tech E&O may be the home for the loss. If the AI system is used in hiring, promotion, discipline, termination, compensation, or workplace monitoring, it might sit in employment practices liability. If the issue involves board oversight, public disclosures, securities claims, regulatory scrutiny, or failure to govern a material operational risk, D&O may become part of the conversation. If AI generated content creates allegations of defamation, copyright infringement, trademark infringement, right-of-publicity violations, or advertising injury, the analysis may move toward media liability or IP coverage. If an AI enabled product causes bodily injury or property damage, the relevant policies may include general liability, products liability, workers compensation, or other casualty coverages. If AI is used to impersonate an executive, manipulate an invoice, create a deepfake, or induce a fraudulent funds transfer, the issue may be crime, social engineering, funds transfer fraud, or cyber crime.
The same broad technology category can appear in every one of those examples, but the coverage answer changes because the loss changes.
That is why broad “AI coverage” labels can be misleading. They may sound innovative, and they may be useful as a marketing play, but they can obscure the real underwriting and coverage questions. If insurers treat AI as a single peril, the market may end up with broad exclusions, narrow endorsements, and coverage gaps. An “AI exclusion,” drafted too broadly, could unintentionally remove coverage for ordinary negligence because an AI enabled tool was used somewhere in the process. A cyber only AI endorsement could create false comfort by addressing one narrow slice of the exposure while leaving non cyber AI claims untouched. The market does not and should not pretend AI is one peril. It needs to identify where AI changes the insured’s existing exposure and be precise.
This requires moving beyond the basic question of whether the insured “uses AI” to asking about where AI sits inside the organization, what function it performs, whose interests it affects, what data it uses, how autonomous it is, and what happens when it is wrong.
An AI tool that summarizes internal meeting notes is a very different exposure than one that approves loans, prices insurance, recommends medical treatment, screens applicants, drafts customer facing advice, monitors employees, controls industrial equipment, or generates marketing content at scale. Likewise, an internal drafting tool that is reviewed by a qualified professional is a different risk than a customer-facing chatbot that provides unsupervised guidance, or an automated decision system whose outputs are difficult to audit or explain.
AI is not an ethereal category outside of the pre-2022 risk landscape. It is a tool and, in many cases, an accelerant. It can make losses faster, broader, harder to detect, and harder to explain. It can blur the line between human judgment and software output or between professional advice and automated recommendation. Those features make AI unique, but they do not eliminate the need to identify the actual injury, the legal theory, and the applicable coverage.
At some point, every claim still has to land somewhere. Someone was injured, someone lost money, someone’s data was exposed, someone was discriminated against, someone’s IP rights were infringed, someone relied on bad advice, someone transferred funds under false pretenses, or someone alleges that the board failed to supervise a material risk. The presence of AI may make the facts more complicated, the loss more scalable, and the underwriting more difficult, but it all boils down to the foundational question of what happened, and what kind of harm did it cause?