I spent years at Meta helping disaster response organizations like the Red Cross put data to work to help others. After a hurricane or a wildfire, the hard question was never whether people needed help. The hard question was where or how much and how soon. I came to insurance because the property claim is where the hard questions get answered, one family at a time. And because the people who answer those questions are getting harder to find.
Fewer adjusters, harder claims
Every year about 21,600 claims adjuster and examiner jobs become available in the United States due to attrition, according to the U.S. Bureau of Labor Statistics. Meanwhile, colleges only graduate about 2,100 students a year in insurance and risk management, and that number is falling. That gap roughly translates to about 10 open claims roles for every graduate, assuming the unlikely reality that every graduate chooses claims.
BLS expects about 6% fewer of these claims jobs by 2035, partly due to the implementation of software and AI, but those tech-driven efficiencies do not solve the problem. There are still nowhere near enough people to fill the claims jobs that remain open.
Compounding the issue is the fact that the work is getting harder. Swiss Re Institute reports wildfires, storms, and floods drove a record 92% of global insured losses in 2025. Each of those events represents thousands of files a limited number of claims professionals must manage at once, and the more complex cases need an experienced hand. Nobody gives a new adjuster a complex loss in her first year. That judgment takes years to build, and it walks out the door when she retires.
Insurers can't hire their way out of this situation. Thus, the approach has to change by preserving the expertise that exists and pointing it where it matters most.
What AI is for
The strongest argument for AI in claims does not involve replacing adjusters. It is that AI gives them capacity.
Most of the AI conversation in claims is about paperwork: summarizing files, finding documents, flagging gaps. Useful, but small tasks. The larger opportunity is the estimate itself: measuring the room, identifying materials, scoping the damage, building the line items. The work is repetitive. It takes hours on every file, and it is harder than it looks because each line item has to match the carrier's guidelines. Repetitive, slow and rule-bound. These are exactly the types of tasks for which automation was built. Computer vision and AI can now produce a first draft of the estimate from a handful of interior photos, at the fidelity of a veteran adjuster, in hours versus weeks. Doing so lifts the cognitive load from the adjuster, thus freeing her for the human side of the claim while also leveraging her expertise and judgment.
What technology cannot do, and should not do, is make final decisions. Is the claim covered? Would this rule apply? What does this family need to hear from the insurer today? That has always been the adjuster's job and must remain so.
It also keeps knowledge in the building. A first-year adjuster who reviews a fully scoped complex loss learns how one is built, instead of creating their own, one file at a time.
Where the caution is right
The industry is right to be careful, and careful should mean a couple of things.
Accuracy comes first. In an estimate, the costliest mistake is rarely the measurement. It is a line item: the wrong one chosen, or the right one missing. Replace the flooring but forget to detach and reset the vanity, and the contractor sends a supplement. Include a line the carrier's guidelines don't allow, and the file gets flagged. One shortchanges the policyholder. The other leaks money. Both happen today in files no AI ever touched.
So, the right benchmark for AI tools should not be an imaginary process where people and legacy systems never make mistakes. It should be how claims run today, with controls that match the consequences of an error.
Second, every line item must be traceable, and every gap has to be visible. For each line, the adjuster should see the photo showing the damage behind it and the guideline it must satisfy. So should the auditor, the reinsurer and, if it comes to it, the judge. Claims executives ask me one question more than any other: will the number hold up in an audit, a complaint review, a dispute, or in front of a regulator? Traceability helps ensure the answer remains yes. An AI that cannot show its work does not belong in a claim file. Glass box, not black box. In claims, defensibility comes first, followed by speed.
Build around both
The choice on how to solve the issues facing claims is not between people or AI. There simply are not enough experienced adjusters to run claims the old way. At the same time, AI cannot carry the judgment, accountability or empathy a claim requires. Nor should it.
The model I believe in has four rules:
One: AI does the repetitive, rule-bound work and drafts the estimate.
Two: adjusters make every consequential decision, i.e., coverage, cause of loss, and what the policyholder is owed.
Three: experienced adjusters train, test, and govern the AI.
Four: success is measured in accuracy, resolution quality and adjuster capacity, not in the share of tasks automated.
Careful is not the same as slow. The talent gap is not waiting for perfect technology. Neither is the family standing in a flooded kitchen, nor the thousands like them in week six after a CAT, when the queue is still deep. The bellwethers will not be the carriers that automate the most. The real leaders will be the ones that empower adjusters to do their best work: look at the damage, decide what is fair and tell the policyholder so, quickly and accurately. The moment after loss should be the start of rebuilding, not the start of waiting.
