The Pre-Mortem is a weekly series on this blog. Each piece applies five questions to a major technology commitment before the outcome is known.
By the end of this year, twenty million Americans will use an AI companion to check whether their treatment has been approved, understand their benefits, and find out where they stand on a coverage dispute. UnitedHealth Group, the largest health insurer in the United States, calls it Avery. What the company has not published is what happens when Avery gets it wrong, and who carries it.
The Bet
UnitedHealth Group is investing more than $1.5 billion in AI in 2026. Avery is one part of a portfolio of over one thousand AI applications now operating across its insurance, pharmacy, and healthcare delivery businesses. The company expects a two-to-one return, much of it within the next eighteen months.
The scope goes further than the navigation functions Avery handles publicly. UnitedHealth has stated its intention to embed AI across claims decisions, clinical documentation, billing code selection, and fraud detection. The bet is that AI can absorb these regulated, high-stakes workflows faster than the accountability architecture around them can be clarified.
The Assumption
The whole bet turns on this: that an AI companion helping members find their benefits is categorically different from an AI algorithm making coverage decisions.
That distinction matters to UnitedHealth and to the regulatory debate around it. It is also the exact point where the accountability gap lives. Avery’s scope includes claim approval status and benefit explanations. In the sequence of a denied treatment, those interactions are not neutral, they are the moments where a member either understands their rights or does not. The line between navigation and decision sits precisely where the product is deployed.
The Sequence
UnitedHealth has been here before. Between 2019 and 2022, its subsidiary naviHealth deployed an AI tool called nH Predict to manage post-acute care decisions for Medicare Advantage members. A Senate investigation found that UnitedHealth’s denial rate for post-acute care claims more than doubled after nH Predict was deployed. A federal class action, Lokken v. UnitedHealth Group, alleges that the algorithm overrode treating physicians’ recommendations and carried a 90 per cent error rate on appeal: nine of every ten denied claims reversed when challenged.
That lawsuit is still advancing. In March 2026, a federal court ordered UnitedHealth to disclose its AI denial algorithm documentation, including internal AI Review Board materials, documents related to government investigations, and business records reaching back to 2017. Avery launched the same month to 6.5 million members, with a target of 20.5 million by year-end.
The sequence matters. The error rate history of the predecessor tool is documented and in litigation. The commitment not to repeat it with Avery has not been published in measurable form.
The Pager
UnitedHealth states that Avery is governed by a responsible use policy with review and approval from its AI Review Board. That board governs model development. No published framework names which specific individual, body, or governance layer is accountable when an Avery interaction contributes to a coverage outcome that causes patient harm.
The regulatory picture does not close that gap. At least twenty-five states have issued guidance under the National Association of Insurance Commissioners (NAIC) model bulletin. Alabama, Indiana, Washington, and others have enacted specific laws requiring human sign-off on AI-assisted denials, most taking effect in 2026. But the Employee Retirement Income Security Act (ERISA) preempts state action against self-insured employer plans, which cover the majority of employer-sponsored insurance. Federal oversight through the Centers for Medicare and Medicaid Services (CMS) and the Department of Health and Human Services (HHS) covers Medicare Advantage but carries no published standard for AI liability in individual claim decisions. The accountability is distributed. No name is on it.
The Proof
The $1.5 billion figure is confirmed. No committed outcome measure has been published for Avery’s error rate, its impact on denial rates, appeal success rates under AI-assisted decisions, or any patient safety incident reporting cadence.
Per CMS disclosures filed March 2026, the first year the agency required public reporting, UnitedHealth’s prior authorisation denial rate was 16.3 per cent in 2025, 4.8 percentage points above the industry average of 11.5 per cent. The company announced in May 2026 that it will eliminate prior authorisation for 30 per cent of services by year-end. Whether that changes the AI-in-the-loop accountability question for the remaining 70 per cent has not been addressed.
The Verdict
If the governance architecture catches up, if AI Review Board accountability is mapped to individual outcomes, if state AI denial laws close the ERISA gap, and if a committed outcome framework for Avery is published and audited, then this is exactly what responsible AI deployment in healthcare should look like: a major operator taking the accountability question seriously under public and regulatory scrutiny.
Without all three, twenty million people are interacting with an AI system whose error rate is undisclosed, whose predecessor carried a 90 per cent reversal rate on appeal, and where no named human is accountable for what it tells them about their care.
The bet is bold. The architecture to carry the loss has not been built yet.