The conversation around AI in healthcare is moving fast — but not evenly. A growing segment of utilization management and revenue cycle leaders has already moved past the adoption question, into harder territory: how to scale what’s working, prove its value to skeptical clinicians, and build the governance structures that make it last.
The leaders who gathered at XCHANGE ’26 — Xsolis’ annual user conference held in San Antonio in May 2026 — aren’t early adopters weighing a first deployment. They are hospital and health system executives, utilization management and care coordination leaders, physician advisors, and revenue cycle professionals actively implementing AI in clinical and operational workflows — in many cases for years.
What the most experienced AI adopters are worried about
Among attendees who responded to a post-event survey, 43% identified competing priorities and limited IT bandwidth as the biggest barriers to expanding AI adoption — not skepticism about the technology itself. Their top priorities for the next 12 to 18 months: operational expansion, ROI measurement, and governance. Even in this experienced cohort, clinical staff skepticism around AI accuracy and workflow relevance was the most frequently cited source of internal resistance — a reminder that technology adoption and clinical trust do not move at the same pace, and that trust requires ongoing investment long after implementation is complete.
For a group this far along, these aren’t beginner problems. They are second-generation challenges — harder questions that come with maturity, less about proving the technology works and more about extending its value and building the infrastructure that makes results repeatable.
When AI changes the provider-payer relationship
These second-generation challenges dominated the conversation at the conference. Chief among them is one the broader industry rarely discusses honestly: the provider-payer relationship, and what it looks like when AI sits at the center of it.
Most of these organizations have moved beyond using AI solely to inform internal decisions and are now leveraging it as a bridge — sharing analytics directly with payers during concurrent authorization to reduce friction, align on level-of-care decisions, and rebuild relationships that were once defined almost entirely by adversarial back-and-forth.
The panel Aligned by Analytics, Driven by Outcomes brought hospital leaders from MultiCare Health System and Inova Health System together with Xsolis COO Chris Bayham to examine what building that model requires — and what it takes to sustain it.
Dr. Debbie Schardt, AVP of Revenue Cycle and Utilization Management at MultiCare — one of the earliest health systems to implement AI-driven utilization management — described a journey that began not with the most difficult payer, but the most receptive one. The strategy was deliberate: establish trust through shared data before attempting to change behavior. “When you show them the data and you show up with information they can leverage,” she said, “it makes all the difference.”
MultiCare now works with multiple payers under AI-informed precision utilization management agreements — a model in which both parties automate alignment on clear-cut cases, freeing resources to focus on complex cases where human judgment matters most. Getting there took years of relationship-building, data transparency, and a willingness to absorb early friction.
Brian Donovan, VP of Managed Care and Payer Relationships at Inova, offered a parallel perspective from the managed care side. His turning point came while building accountable care models, when he learned that letting people pressure-test data themselves was far more effective than leading with demands. “Relationships are the key to everything,” he said. “You can’t go into something new and expect that leverage alone is going to carry the day.”
Both leaders converged on the same near-term priority: reducing administrative burden — not as an operational nicety, but as the clearest area of shared interest between providers and payers, and one where AI has a clear, quantifiable role.
The reorientation hospital executives need
For hospital executives still focused on getting AI implemented, the message is a useful reorientation. Durable results come from investing in the harder work post-implementation: governance structures, cross-functional relationships, and the clinical trust that transforms a useful tool into an institutional capability.
The bottleneck, this group made clear, is no longer the technology. It is the organizational capacity to operationalize it, govern it, and bring clinical teams along for what comes next.
At the Becker's 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health, taking place September 14–17 in Chicago, healthcare executives and digital leaders from across the country will come together to explore how AI, interoperability, cybersecurity, and revenue cycle innovation are transforming care delivery, strengthening financial performance, and driving the next era of digital health. Apply for complimentary registration now.
