Ask the carriers we've spoken with how much of their claim volume gets a quality review, and the answer is somewhere between one and seven percent. The rest of the files are handled, closed and never formally reviewed.
The industry decided long ago that checking everything was impossible, built a sampling regime around that, and forgot the regime was a compromise. For most files, "approved" simply means "not sampled."
Adding AI to that environment can make things better or quietly worse. The difference lies in how the technology is governed.
Rubber-Stamping Predates the Machines
An ‘Approve’ button on an AI recommendation did not invent rubber-stamping. It industrialized a habit the business already had. Reviewers who click through hundreds of correct suggestions eventually stop looking.
That is why responsible AI cannot be reduced to "a human checks the output." Supervision decays. What doesn't decay is ownership: a professional who writes the decision, with her name on it, and has the evidence in front of her to defend it.
At Elysian, that principle shapes the design. AI does the work around the decision, including intake, document chasing and building the chronology. The experienced claims professional makes the call. AI enhances her judgment; it does not replace it.
What Happens When You Grade Every File
Earlier this year, we tested swapping the model underneath the system that reviews claim files against a rubric written by the most experienced adjusters we know. In testing, the new version looked sharper. Once it was live, it was passing audit questions at a noticeably higher rate than our expert adjusters do when they review the same kind of files. The new model hadn't gotten smarter. It had gotten lenient. It approved more, and approving more looks like performing well right up until it doesn't.
If the only check had been someone clicking Approve on its output, a higher pass rate would have looked like good news. What caught it was a different kind of check: our expert adjusters regularly score a sample of the files the system has reviewed and compare their judgment with the system's. That comparison showed the jump came from the model going easy, not from better claim handling, and we flagged it within a day of our test.
That check is the heart of governance. Reading every file is not enough if no one is checking the reader. The system reads every file we handle, and our experts audit the system.
Transparency: Show the Work, and Show the Gaps
Responsible AI also depends on transparency, and in claims that has two parts.
The first is explainability. When a system flags a file or suggests a direction, it should show the evidence behind that suggestion: what it read, what it weighed and why. An expert can only exercise judgment over reasoning she can see. A bare number invites agreement whereas a reasoned argument invites scrutiny.
The second is honesty about uncertainty. A system that projects the same confidence on every file teaches its users to trust it uniformly, which is exactly wrong. It should tell the adjuster where its picture is thin, so human attention goes where it is most needed. That is the point of reading everything: surfacing the three files out of a hundred that need an experienced person today, and saying why.
Accountability Is a Design Decision
At a recent Fortune Brainstorm AI session, Accenture's global CTO said that the number of large enterprises that have truly rebuilt a core process around AI is close to zero. Asked who is accountable when an agent errs, he split it three ways: the business owner for its value, the technologist for its functionality and the security chief for its security. Three owners for every agent, and none for the decision. Wharton and GBK Collective's 2026 survey of AI at large U.S. companies points the same way: one in five managers and directors said nobody was clearly accountable for an autonomous AI error, or that they did not know who was.
Accountability like that doesn't materialize by accident. It has to be built in. When the human writes the decision and the machine builds the file, the answer is simple: the person who owns the decision is accountable for it.
Claims make this especially important, because the feedback on a decision arrives late. A reserve might not be tested for two years, in a courtroom. By then, the question that matters is not whether a model was confident. It is who made the call, on what evidence, and under what oversight.
A Standard Worth Holding
The standard we would propose for any company putting AI near a decision that matters is this: automate everything around the decision, review everything, and keep the decision with the person whose name goes on it.
Three questions will tell you where you stand:
- Does the human write the decision, or countersign it?
- What share of the output does anyone actually look at?
- Does the system show you what it does not know?
Elysian was built around those questions. We combine deep claims expertise with explainable, evidence-based AI so that experienced professionals can make more informed decisions, while accountability, transparency and governance stay where they belong.
Responsible AI in claims is less about how much a machine can do than about being clear on what it should never be asked to do alone.
