For years, utilization management has been measured by its ability to answer operational questions. How quickly can requests be reviewed? How consistently can policies be applied? How efficiently can organizations balance quality, cost, and appropriate use of care?
Those questions remain important. But they are no longer enough.
Artificial intelligence, evolving regulations, and rising expectations from providers and members are changing what healthcare expects from utilization management. The conversation is shifting beyond administrative efficiency toward something more fundamental: improving the quality of clinical and operational decision-making.
That shift was at the heart of the Utilization Management roundtable I had the privilege of moderating at the Emids Healthcare Summit ‘26. The discussion brought together leaders from across health plans, provider organizations, technology companies, and consulting firms to explore what the next generation of utilization management should look like. While opinions varied on how quickly AI will reshape the function, there was broad agreement on one point. The future will not be defined by how much AI organizations deploy. It will be defined by whether AI helps healthcare make better decisions.
Where clinical expertise has the greatest impact
For decades, the primary objective of utilization management has been to ensure appropriate care while managing cost. Over time, organizations have improved workflows, digitized reviews, and streamlined prior authorization processes in pursuit of greater efficiency.
AI undoubtedly accelerates that progress. But if our ambition is limited to processing requests faster, we risk missing the larger opportunity. The more important question is whether we are helping clinicians make better decisions.
During our discussion, several leaders challenged the assumption that utilization management should continue evolving through incremental efficiency gains alone. If a significant percentage of requests are ultimately approved, perhaps success should not be measured solely by how quickly reviews are completed, but by whether clinical expertise is being applied where it has the greatest impact.
That represents an important shift in thinking. The conversation is moving from managing utilization to improving decision quality. AI is simply making that conversation unavoidable.
AI Raises the Standard for the Entire System
Much of the conversation around AI focuses on what the technology can automate. Its greater impact lies elsewhere. AI is raising the standard for how healthcare operates.
For years, organizations have worked around fragmented documentation, inconsistent clinical workflows, and varying governance practices. Experienced teams have compensated for these gaps through institutional knowledge and manual intervention. AI cannot.
AI depends on trusted data, standardized processes, and clearly defined decision criteria. It doesn’t eliminate inconsistency; it exposes it. As organizations scale AI, variation that was once manageable quickly becomes a barrier to reliable, repeatable outcomes.
This reality was reflected throughout the executive discussion, where leaders pointed to significant differences in documentation practices, governance models, clinical workflows, and even the utilization management code sets used by health plans serving similar populations. In almost every case the constraint was the operating model rather than the technology.
Documentation quality, governance, and process standardization should therefore not be viewed as implementation activities that follow AI adoption. They are the capabilities that determine whether AI can deliver consistent, trusted, and clinically sound decisions at scale.
Does AI reduce the role of clinicians?
No. One of the biggest misconceptions surrounding AI is that greater automation inevitably reduces the role of clinicians. AI is well suited to reviewing documentation, organizing medical records, validating information, and surfacing the evidence needed to support a decision.
Those capabilities can reduce administrative burden and accelerate routine reviews, but they do not replace clinical expertise. They make it more valuable.
As AI assumes repetitive, information-intensive tasks, clinicians have greater capacity to focus on the complex cases where context, experience, and judgment influence outcomes. The role of utilization management shifts from processing every request to applying clinical expertise where it has the greatest impact.
Leaders echoed this view during the discussion, seeing AI not as a replacement for clinicians, but as a way to strengthen decision-making while maintaining accountability for high-impact clinical decisions.
Human oversight is more than a governance requirement. It is the foundation of trust. As AI becomes more deeply embedded in utilization management, confidence among providers, regulators, and patients will depend not only on the accuracy of AI-generated recommendations, but also on the transparency, accountability, and clinical judgment behind every decision.
The Opportunity Is Bigger Than Prior Authorization
Prior authorization remains one of the most visible applications of utilization management, but the discussion made clear that the industry’s opportunity extends much further.
The same principles shaping prior authorization will increasingly influence care management, claims operations, quality programs, and other clinical workflows where decisions must balance evidence, policy, and individual patient needs.
Viewed through that lens, AI in utilization management is a much larger opportunity as it becomes a framework for consistently making better decisions across the healthcare enterprise.
A Different Measure of Success
Healthcare has spent years improving the efficiency of utilization management. The next phase will require improving its intelligence. Organizations will continue investing in AI, automation, and new technologies. Those investments matter. But technology alone will not determine who succeeds.
Success will depend on building trusted data, establishing consistent governance, preserving clinical accountability, and thoughtfully deciding where automation creates value and where human expertise remains indispensable.
Ultimately, the future of utilization management will be measured by the confidence healthcare has in the decisions that are made, more than by how many of them are automated.
This article was inspired by the perspectives shared during the Utilization Management executive roundtable at the Emids Healthcare Summit ‘26. My sincere thanks to the healthcare leaders whose candid discussion and practical insights helped shape the ideas reflected here.
