Close to 53 million prior authorization requests hit Medicare Advantage plans in 2024. No honest reader of that number can believe the current prior authorization model can be run effectively by humans alone, and no serious operator would argue that it should be run by machines alone.
As more health insurers use AI to help manage the prior authorization process, more hospitals, doctors, and patients have cried foul. Yet the interesting question isn't whether AI belongs in prior authorization. Scale alone makes AI an operational necessity.
The better question to ask is how we divide that labor between AI and clinicians, keeping human expertise, judgment, and experience in the loop throughout the process. If AI can help deliver the right outcomes for patients, shouldn't it be part of the process?
I think the answer is this: within a couple of years, prior authorization in the United States will run on a settled split. AI does the evidence work at machine speed, quickly approving requests that meet payers' guidelines, and a board-certified clinician owns every denial. Many health insurers have begun touting this split, and those that haven't done so will fall behind on turnaround and overturn rates, to the detriment of the trust of the physicians whose patients they cover.
The AI half of that split isn't a policy debate. It's already happening.
The 2026 HealthEdge Annual Payer Report puts AI adoption among payers at 91%, with prior authorization and claims adjudication among the highest-impact deployment areas. And UnitedHealth Group, which operates the nation's biggest health insurer, has said it's investing $3 billion in AI in 2026-27 to automate more processes, including speeding prior authorization decisions.
New rules from the Centers for Medicare & Medicaid Services (CMS) on prior authorization timelines and the June 2025 insurer pledge, which saw the largest payers commit to real-time processing and standardized electronic prior authorization by 2027, have arguably contributed to that uptake. Amid tightening deadlines and a growing number of prior authorization requests, AI is increasingly emerging as the practical lever insurers are turning to meet those deadlines at scale.
What's important is that this AI adoption comes with protections. That means clinician oversight and expert judgment. The buck needs to stop with humans, especially when it comes to denials. Some health insurers have quietly been treating AI as a way to automate reviewers out of the loop rather than to equip them. That is the wrong build, and it's unlikely to survive the next regulatory cycle.
The underlying principle behind all this – the idea that "AI flags, a clinician affirms" – is already emerging as the floor in prior authorization. As of April this year, nine states have enacted laws on AI and prior authorization, and while each has its differences, the common thread between them is a requirement for human review of claim denials (KFF). I think we'll see that number continue to grow, even as the Trump administration pushes to preempt state AI legislation. The administration's own framework has drawn a line in the sand. It targets duplicative, innovation-blocking state rules, not the ones that protect consumers from fraud and harm.
The best play for health insurers now is to ensure they're ahead of the curve. CMS's new rules require payers to publicly report turnaround, denial, appeal, and overturn rates. Once those numbers are comparable across payers, any reviewer operating without AI-assisted evidence synthesis will be visibly, quantitatively below the standard set by those who have built for the split.
Those who are operating with AI but without clinician oversight risk falling into a similar trap. Speed without judgment is where prior auth goes wrong, and persistently high denial rates become a financial and reputational liability long before they become a compliance one.
The winning model is a settled division of labor. AI handles ingestion, timeline reconstruction, guideline matching, and first-pass evidence synthesis. The clinician makes the determination and carries accountability for it, in their name, on the record. Administrative AI runs the pipes, clinical AI supports the call, and humans remain in the loop where the loop matters.
For health insurance executives making build decisions this quarter, the tangible moves are straightforward. Fix the process, don't throw it out. Build explicitly for clinician accountability rather than around it. Invest in the neutral review layer instead of trying to litigate it away. And most importantly, do the right thing and be vocal about what you're doing. Transparency is non-negotiable.
Today, the question we hear most often is whether AI belongs in prior authorization. In the next couple of years, the question flips: Why was this contested decision not supported by AI-assisted evidence synthesis? The insurers who can answer that question with a straight face are the ones building for the split now.
