AI adoption in insurance is accelerating but ROI is still lacking. A while ago I spoke with a Chief Claims Officer who said “We spent four months on a transformation to take the claim workflow from five clicks to three. How is that giving us a competitive edge?” The problem is that the industry has become too focused on driving efficiency. Not on improving outcomes.
The leading companies in an age of change don’t look at an existing process and ask how to optimize it or take out cost. They use new technological shifts to ask how we can fundamentally improve the outcomes. What can we do now that was not practically possible before AI?
Claims quality assurance is one of the clearest examples. Traditionally, carriers have audited a small sample of claims – around 5%, and sometimes considerably less. The findings arrive weeks or months after the relevant decisions were made. By then, the opportunity to change the claim outcome has passed and QA becomes another rearview mirror: useful for training, compliance, and understanding what went wrong, but limited in its ability to affect the claim itself. Dollar outcomes remain unchanged.
With AI, QA can take on a fundamentally different shape and move from a backend process to a strategic lever that drives better claim outcomes. Instead of just looking at a sample, we can see every claim – and more importantly, instead of looking in the rearview mirror, we do this in real time, while the file is still open and there is still time to act.
The problem with sampling
I recently met with a top-10 carrier whose team described the scale of the problem well. They were supposed to audit roughly 1% of claims. Even that proved difficult at scale, so the team narrowed its review to California and Florida. Even so, in those two states alone they identified roughly $22 million in recoverable opportunity.
That raises an obvious question: what remains undiscovered across the rest of the book?
Traditional QA has always faced a structural constraint. Organizations simply cannot have people manually review every file, every note, every document, and every decision.
Sampling is not the only problem. Consistency is another. Supervisors are often reviewing their own teams, and they know that one claim per month is not representative – so penalizing an adjuster over a claim that may well be an outlier feels unfair. Other supervisors are stricter. Some will double-count a single miss. You know this – and it is why the aggregate scores in your dashboard are hard to act on.
AI removes much of that constraint. Instead of auditing 1% or 5% of claims, carriers can evaluate 100%. And it does so systematically, applying the same standard every time. We can finally get findings across the book that we can trust. But auditing 100% of claims is only an improvement – the real impact comes from moving to real time.
QA becomes real-time
Reviewing every claim sounds transformative. And it is. But if the output is still a report followed by a training session, only the measurement system has improved. Day-to-day actions remain unchanged – and so do dollar outcomes.
No Chief Claims Officer needs another report telling them that adjusters should contact claimants faster. The organization already knows that. Adjusters know it too. The challenge is making sure the right action happens on the individual claim, at the right moment.
One claims leader put it plainly: litigation strategy begins at FNOL. Not when suit is filed. Not when there is a trial date. When you get the claim. Otherwise you are playing checkers, not chess.
That is why the most powerful version of AI-driven QA is real-time QA.
Instead of identifying problems retrospectively, AI continuously evaluates the claim as it develops. When something meaningful appears – a reserve that no longer reflects the probable outcome, a missed subrogation opportunity, a delayed investigation, an unsupported liability decision – the system can surface it directly to the people who can act. Now we can finally act before it is too late while the claim function move from being a cost center to steering capital strategy.
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