The Insurance Market Already Named Who Owns Your AI
Insurers just repriced AI risk. The bill lands on the company that runs the model, not the one that built it.
Insurance filings are the most honest documents in the AI economy. A vendor’s landing page tells you what a system is supposed to do. An underwriter’s endorsement tells you what happens when it does not, and it says so with capital behind the claim. This year the underwriters wrote their claim down, and it settles a question most companies are still treating as open.
The question is simple to state and expensive to ignore: when AI inside your operation produces a bad outcome, whose loss is it?
For two years the honest answer inside most organizations has been a shrug spread across a committee. IT owns the tool. Legal owns the contract. A working group owns the policy document. No one owns the decision the AI actually makes. That was survivable while AI was a pilot. It stopped being survivable once 88% of organizations reported using AI in at least one function, up from 78% a year earlier. The pilots became infrastructure. The ownership question did not get answered. It got deployed around.
The insurance market just answered it for you, in two moves.
Move one: your existing policy stopped covering AI
In January, Verisk’s ISO, the body that writes the standard forms most commercial insurers build on, released new endorsements, CG 40 47 chief among them, that let carriers exclude generative AI from commercial general liability. This is not a fringe product. By spring, carriers including W.R. Berkley, Chubb, Travelers, Berkshire Hathaway, and Cincinnati Financial had filed to adopt AI exclusions, and regulators approved the large majority. The coverage many companies assume protects their AI use is being removed by endorsement, quietly, at renewal.
Sit with what an exclusion is. It is an insurer looking at a category of risk and deciding it is too uncertain, too correlated, or too large to keep inside a general policy at the current price. That is not indifference to AI. It is a judgment that the risk is real, material, and separable enough to name and carve out.
Move two: a market opened to price it directly
In June, Mayflower and Hadron launched what they describe as the first dedicated affirmative AI liability program in the US: explicit coverage across directors and officers, employment practices, and errors and omissions, written for enterprise AI. The launch is not the story. The underwriting method is. They price the policy by scoring the deployer’s AI for specific failure modes, bias, drift, and hallucination, against NIST and ISO frameworks.
Read that plainly. An insurer has built an actuarial model of your AI risk. Not the model maker’s. Yours, the deployer’s. They are pricing the gap between the AI you run and the governance you have wrapped around it, and they are confident enough in that price to put reinsurance behind it.
Put the two moves together and the logic is unambiguous. The market pulled AI out of the pool where risk is shared and vague and moved it into one where risk is named and individually priced. That only happens to a risk with a clear owner. You cannot price what you cannot attribute. The underwriters attributed it. To you.
What the underwriter actually asks
To get that price, you fill out a questionnaire. Strip away the formatting and it asks a handful of questions, in substance:
Where is AI used in your operation, and which of those uses touch a customer, an employee, or a regulated decision?
For each one, who is the named person accountable when it is wrong?
What is the human review step before the output leaves the building?
What do you log, and could you reconstruct why the system did what it did?
Not one of those is a technical question about the model. Every one is a governance question about you. The underwriter is not curious how the model works. It is establishing whether anyone is holding the wheel, because that is what decides whether it pays out, and how often.
Which is why two companies running the identical models will not get the identical quote.
Firm A and Firm B
Firm A and Firm B use the same three vendors and the same foundation models. Firm A can name an owner for each consequential AI use, can show the review step, and can produce a log. Firm B connected the tools, saw the productivity, and never assigned the accountability, because it felt administrative and no one made it anyone’s job.
The underwriter cannot see intentions. It can see controls, or their absence. Firm A prices a small gap. Firm B prices a large one, or cannot complete the questionnaire and does not qualify. Same models, same industry, different number, because the risk was never really about the model. It was about who owns the decision the model makes.
So the quote is a diagnostic you did not order. An outside party, with money at stake and no incentive to flatter you, has assessed your AI governance and returned a number. If it comes back high, that is not the insurer being cautious. It is the insurer telling you what your ungoverned surface is worth.
The courts drew the line first
None of this is the insurance industry inventing a position. It is the industry catching up to one the law has been sketching for a while.
When Air Canada’s customer-service chatbot told a passenger he could claim a bereavement fare after the fact, and the airline argued it should not be bound by its own bot’s error, the British Columbia Civil Resolution Tribunal rejected the argument outright. Air Canada was responsible for the information on its website, whether it came from a static page or a chatbot. The bot was not a third party. It was the company. That is the cleanest statement of the principle: when your AI speaks to a customer, you own what it says.
A separate case cuts the other way, and it pays to state it precisely, because it is narrower than it first looks. When Mark Walters sued OpenAI over a false statement ChatGPT generated about him, a Georgia court granted OpenAI summary judgment on several independent grounds, one of them that OpenAI’s repeated, prominent disclaimers meant no reasonable reader would take the output as a statement of fact. That is a trial-level defamation holding, not a rule about who owns AI liability. Read for direction rather than doctrine, the two cases point the same way: the maker that discloses its limits steps back, and the party that puts the output to work answers for it. Insurance is now priced on that same line.
What the premium is actually measuring
Here is the part to carry into your next renewal conversation.
Insurance is a fine thing to buy. It turns a volatile loss into a fixed cost, and for AI exposure that is prudent. But be precise about what it does, because the marketing will blur it. A policy transfers the cost of a failure. It does not transfer the ownership of the decision that caused it.
Walk the failure past the payment. The regulator opens an inquiry and asks who approved the output. The board asks who was accountable. The customer, and eventually a court, holds your company, not your carrier, to what your system did. At none of those tables does the check answer the question being asked. Coverage answers the invoice. It does not answer the question.
The market has done you an inadvertent favor. It has put a dollar figure on a thing your organization kept treating as unpriceable, and therefore ignorable. The exposure was always yours. Now it has a number next to it, and the number moves for exactly one reason.
The only move that lowers it is the one no policy can make for you: decide, before the system runs, who owns the decision it makes.
AI operates. You own the decision. The underwriter already assumed you did, and priced it.
Thursday’s piece goes to the sharp edge of this, and it is subscriber-only: how the underwriter ends up doing your AI inventory for you, and why the check that pays the claim still cannot answer for the decision. Subscribe and it will reach you.
Sources
• “88% use AI, up from 78%”: McKinsey, The State of AI (Nov 2025)
• ISO exclusions CG 40 47 / 40 48 / 35 08 (eff. Jan 1, 2026): Verisk / ISO general liability filing
• Mayflower & Hadron AI liability program (June 24, 2026): Business Wire
• Moffatt v. Air Canada, 2024 BCCRT 149: CanLII, full decision
• Walters v. OpenAI (Gwinnett County, GA, May 19, 2025): Loeb & Loeb
Notes: carrier-level specifics are press-sourced (primary record is each carrier’s SERFF state filing). Walters is a trial-level defamation ruling, read for direction, not a liability-allocation holding.