Every insurance executive has heard the pitch by now: artificial intelligence can summarize claim files in seconds, flag potentially fraudulent claims before an adjuster even opens the folder, and answer coverage questions that used to take a phone call and a hold queue. The technology is no longer the hard part. The hard part is deciding how much control to hand over, and to what.
That's a different question than the one most carriers have been asking. For the last few years, the conversation around AI in claims has mostly been about capability: what can the models do? The more useful question now is about governance: what should we let them do on their own, and how do we prove, after the fact, that they did it responsibly?
Having spent over a decade building the integration layer that connects carrier systems, policy platforms, and claims workflows, I've watched this shift happen from the inside. The claims organizations that get real value from AI are not the ones with the most advanced models. They're the ones that have thought carefully about four questions before turning anything loose on a live claims file.
1. Where should AI recommend, and where should it act?
Not every task in a claims file carries the same risk. Summarizing a thick claim file so an adjuster can get oriented in two minutes instead of twenty is low-risk: if the summary misses something, the adjuster still reviews the underlying documents. Looking up policy terms or prior claim history is similarly low-risk, since the AI is retrieving facts, not making judgment calls.
Coverage determinations and payment decisions are a different matter. These are the moments where a mistake costs money, invites a regulatory complaint, or damages a customer relationship that took years to build. The organizations doing this well draw a clear line: AI can prepare, summarize, and flag, but a person signs off before a check goes out or a claim gets denied. That's not a permanent limitation on the technology. It's a sequencing decision, and it's the right one for now, because trust in these systems has to be earned claim by claim, not assumed on day one.
The mistake I see most often is treating this as an all-or-nothing choice. Executives either wall AI off from anything that matters, which wastes most of its value, or they let it touch everything at once, which is how a small error becomes a large headline. The better approach is to map your claims workflow task by task and assign each one a tier: fully automated, AI-assisted with human sign-off, or human-only. Revisit the map every quarter as the technology and your confidence in it both improve.
2. Can you explain what the AI did, after the fact?
Insurance is one of the most heavily audited industries in the country, and that's not going to change because a claim was processed with help from a language model. If a regulator, an ombudsman, or a policyholder's attorney asks why a claim was handled a certain way, "the AI decided" is not an answer. You need to be able to show what information the system had access to, what it did with that information, and who reviewed or approved the outcome.
This means explainability has to be designed in from the start, not bolted on after a complaint arrives. Every time an AI system touches a claim, whether it's pulling a policy document, drafting a summary, or flagging a fraud indicator, that action should be logged in a way a non-technical auditor can follow: what was accessed, when, by which process, and what a human did with the result. Carriers that build this logging into the architecture from day one find audits and regulatory exams go faster, not slower, because they can produce a clean record instead of reconstructing one after the fact.
3. Are you building on what you already have, or starting over?
Claims platforms are old. Many carriers run core systems that predate the Internet, patched and extended over decades because replacing them outright is expensive and risky. The AI vendors selling a "rip and replace" story are selling something few carriers can actually afford, in time or in risk tolerance.
The more practical path is to treat AI as a new layer that connects to what already exists, rather than a reason to replace it. Your policy administration system, your document repository, your fraud rules engine: these don't need to be rebuilt. They need a well-defined way for an AI system to ask them questions and get reliable answers back. Done well, this also protects you from vendor lock-in. If the connection between your systems and your AI layer is built on an open, well-documented standard rather than a single vendor's proprietary format, you can swap out the AI model or the vendor behind it without re-architecting your claims systems all over again. Given how quickly the underlying AI models are improving, that flexibility is worth planning for now rather than discovering you need it in 18 months.
4. Do you have a phased path, or a leap?
The carriers I've seen succeed with this technology didn't start by automating claims decisions. They started with the lowest-risk, highest-volume task they had: usually document summarization or policy lookup, something an AI can do thousands of times a day with a human able to spot-check the results. That builds two things at once: a track record that gives the organization confidence, and a set of real examples of where the AI performs well and where it doesn't.
From there, the path runs through supervised action: AI drafts a fraud flag or a coverage recommendation, and a person confirms or overrides it before anything happens. Only after a system has built a real track record in that supervised role does it make sense to consider narrower forms of autonomous action, and even then, on the lowest-stakes decisions first. This is slower than the vendor demos make it look. It's also the difference between an AI program that survives its first serious incident and one that doesn't.
The bottom line
None of this is really a technology problem. The models are capable enough today to help with meaningful parts of claims work. The real work for executives is organizational: deciding what level of autonomy each task deserves, building in the ability to explain every decision after the fact, connecting AI to existing systems instead of replacing them, and moving in phases that build trust before they build speed.
The carriers who work through these four questions deliberately, rather than reactively, will be the ones who get real value from this generation of AI, without becoming the case study everyone else studies to learn what not to do.
