Here’s something most injury victims don’t know: the person reviewing your claim might not be a person at all. And the number that pops out of that process, the one the adjuster presents to you like it’s a fair and reasonable offer, may have been generated by an algorithm trained to minimize what the insurer pays out.

That’s not a conspiracy theory. It’s increasingly just how the industry works. By 2026, an estimated 91% of insurance companies have adopted AI technologies in some form, and the AI-in-insurance market is projected to hit $35.8 billion by 2029. What surprised me when I started digging into this was how little public scrutiny these systems have faced, until now.

In March 2026, a 12-state coalition of insurance regulators launched the first formal examination of how insurers use AI to make claims decisions, including total-loss valuations and injury payouts. A nationwide vote at the NAIC’s fall meeting in November 2026 could determine whether this becomes a nationwide regulatory framework or stays a patchwork. If you’ve got an injury claim open right now, that timeline matters to you directly.

What These AI Systems Actually Do (and Why It’s Not Obvious)

MetricFigureSource/Context
AI adoption in insurance by 202691% of insurance companiesArticle estimate
AI-in-insurance market projection by 2029$35.8 billionArticle projection
FNOL-to-triage time reduction4-8 hours → under 5 minutesCarrier reports cited in article
Bodily injury claim severity increase (2026)9.2% year-over-yearArticle citation
States with no AI-specific insurance regulations22 statesArticle statement (mid-2026)

When you file what’s called a First Notice of Loss, that initial report of an accident, many carriers are now triaging your claim through agentic AI workflows. Carriers using these systems report FNOL-to-triage times dropping from 4 to 8 hours down to under 5 minutes. Sounds efficient. But efficiency for the insurer and fairness for you are not the same thing.

Here’s how it actually plays out. The AI ingests your reported injuries, cross-references your medical codes against a massive database of similar claims, weighs factors like jurisdiction and litigation history, and spits out a settlement range. Something like $18,000 to $23,000. That range then lands in front of an adjuster, who may have dozens of open files. The number carries implicit authority. It looks like it came from somewhere smart and objective, so there’s psychological pressure, on the adjuster and on you, to treat it as the floor and ceiling of what’s reasonable.

Consumer advocates have documented exactly this pattern: unrepresented claimants accepting the low end of algorithmic ranges without understanding that the range itself might be artificially compressed. The research on exactly how much these tools suppress payouts is still developing, but the directional concern is real and regulators are now taking it seriously enough to open formal examinations.

The Soft-Tissue Problem

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I’ll be honest, this is where the issue gets most personal for injury victims. Soft-tissue injuries, things like whiplash, muscle tears, and nerve damage, are notoriously hard to quantify with imaging. So is psychological harm: anxiety after a crash, sleep disruption, PTSD. These are real, they’re compensable, and they can be life-altering. They’re also exactly the kinds of damages that algorithms handle poorly.

Bodily injury claim severity rose 9.2% year-over-year in 2026, which tells you that the real-world cost of being hurt is going up. Medical inflation, longer recovery times, the lasting economic disruption of serious injuries, it’s all trending upward. But the AI systems are, at least in part, trained on historical settlement data that undervalued these injuries when human adjusters lowballed them. Garbage in, garbage out. If the training data reflects years of suppressed soft-tissue payouts, the model learns that those payouts are normal.

This is one of the core concerns that the 12-state regulatory examination is trying to get at, according to reporting from Auto Body News in March 2026. Regulators want to see inside the black box: what data trained these models, how are decisions documented, and are the outputs discriminatory in ways that aren’t immediately visible?

The Regulatory Gap Is Real, But It’s Starting to Close

Twenty-two states currently have no regulations specifically governing how AI may be used in insurance underwriting or claims handling. That’s not a rumor. That’s where things stand in mid-2026. So depending on where you live, your insurer may be operating with essentially no AI-specific guardrails.

The NAIC’s stated position, and this is actually important, is that existing insurance laws apply to AI-driven decisions the same way they apply to decisions made by human adjusters. That means insurers are fully accountable for third-party AI platforms they purchase. They can’t outsource the liability by saying “well, that was the software’s call.” If the AI’s output violates good-faith claims handling requirements, the insurer is on the hook.

That’s a meaningful protection, but it only helps you if you know to assert it. Most unrepresented claimants don’t. And as Lawyer Monthly noted in May 2026, the pace of AI adoption in insurance is outrunning the regulatory frameworks meant to oversee it. The November NAIC vote won’t fix everything overnight even if it goes the right way, but it would at least establish that human review and explainability standards apply to these systems.

What This Means If You Have a Claim Right Now

You’re probably not going to get the insurer to tell you whether an algorithm touched your claim. You can ask, and you should, but disclosure requirements are still thin in most states. What you can do is treat any early settlement offer as a starting point for negotiation, not a verdict.

A few things worth understanding. If an adjuster cites a specific valuation range, ask them directly how that number was derived. Document the answer. If you have soft-tissue injuries or psychological harm, the gap between what an algorithm suggests and what your damages are actually worth can be substantial. Get your own medical documentation, thorough and detailed, because algorithms lean heavily on what’s in the record. What isn’t documented often isn’t compensated.

The research here is mixed on exactly how much representation changes outcomes, but the pattern that personal injury attorneys consistently see is that represented claimants fare better when claims involve the kinds of subjective damages that AI handles worst. A consultation, usually free, can at least help you understand whether your situation warrants professional help.

Regulators are finally asking the right questions about AI in claims handling. Whether the answers come fast enough to help people with claims open right now is a different question. In the meantime, knowing that an algorithm might be in the room is the first step to not letting it have the final word.

Sources

This article is for general informational purposes only and does not constitute legal advice. Laws vary by state. Consult a licensed personal injury attorney in your jurisdiction for advice specific to your situation. Most personal injury attorneys offer free consultations.

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