Health systems, health plans, and policymakers are enthusiastic about the potential of emerging AI tools to address administrative waste in the U.S. healthcare system. New capabilities that enable activities such as documentation using ambient scribes, chart abstraction, billing, and parsing of complex health plan policies have generated high expectations that technology will drive meaningful administrative efficiency gains.

In January 2026, the Peterson Health Technology Institute (PHTI) convened senior leaders from health systems, health plans, technology developers, investment firms, and federal agencies to discuss how technology and policy can enable AI to reduce administrative costs, accelerate payment cycles, and promote appropriate high-value care.

The workshop focused on two administrative use cases where AI adoption is accelerating: prior authorization and medical billing. The workshop was held under the Chatham House Rule to encourage candid dialogue.

Download the full report to read more, including discussion highlights and detailed themes.

Executive Summary

Of the estimated $350 billion in U.S. healthcare administrative waste annually, $266 billion is attributed to administrative complexity and $59–$84 billion to fraud and abuse. Billing and transaction costs are a significant driver of administrative complexity, with the cost per healthcare bill in the United States far exceeding that of peer nations. This is a result of unique payment rules, documentation requirements, and compliance standards that vary across health plans.

Health plans and healthcare providers are increasingly deploying AI tools to facilitate burdensome administrative processes. For example, in prior authorization, providers are using AI tools to automate submissions, while plans use AI to triage and evaluate prior authorization requests. In medical billing, providers use ambient scribing and AI-assisted coding tools to capture increasing clinical complexity and automate billing, while health plans use AI to assist reviewing and processing claims.

Though we are still in the early stages of administrative AI adoption, it has become clear that rapid AI deployment by both providers and health plans to support prior authorization and medical billing transactions risks increasing levels of system activity without reducing costs. Under existing incentive structures, AI automation could increase the volume of prior authorization back-and-forth, rather than making the process more efficient. AI-assisted coding tools could accelerate coding intensity and charge capture, which—even if accurate—would have an inflationary impact on healthcare costs.

Against this backdrop, participants gathered to explore the following questions:

  • How can AI simplify and reduce administrative processes by removing wasteful steps, while maintaining necessary oversight and accountability?
  • To what extent has current adoption reduced administrative friction?
  • What market-shaping actions can mitigate inflationary risk to ensure that AI adoption delivers net savings?

The following takeaways emerged from the discussion:

Prior Authorization

  • AI may reduce the cost for individual organizations to execute prior authorizations, but it has not reduced overall system-level costs.
  • Real-time prior authorization at the point of care is an emerging model, but current proofs of concept are narrow and not yet scalable.
  • Data standards and digitization of medical policies can reduce information asymmetry, but AI’s impact is limited by variation across medical policies.
  • AI is exposing and exacerbating fundamental issues within the underlying prior authorization process.

Medical Billing

  • Provider deployment of AI is increasing billing intensity and inflating medical spending.
  • Health plans are beginning to respond to AI-driven increases in billing intensity with across-the-board downcoding and other reimbursement reductions, but the impact of these cuts is not yet known.
  • Reimbursement policy is the strongest lever to drive administrative efficiencies and system-level cost savings.