Revenue cycle leaders are balancing two urgent priorities: keeping today’s operation running while building the model for the future. The decisions they make now about ownership, workflows and workforce skills will determine whether AI fundamentally reshapes the revenue cycle or simply accelerates the way work gets done.
That was the framing of Blueprint for a Future-Ready Operating Model, a featured Huron-led session at Becker’s 11th Annual IT + Revenue Cycle Conference: The Future of AI & Digital Health. Three revenue cycle leaders explored what high performance looks like in an AI-enabled environment, the operational changes needed to get there and how organizations can measure success beyond traditional efficiency metrics.
Participants were:
- Sarika Amin, managing director, healthcare performance improvement, revenue cycle, at Huron (moderator)
- Christine Aucreman, system vice president of revenue cycle strategy and operations at City of Hope
- Isaac Segal, managing director, healthcare performance improvement, revenue cycle, at Huron
City of Hope, a Duarte, Calif.-based cancer research and treatment organization, has worked with Huron over the past year and a half to transform its revenue cycle. Huron initially helped accelerate priority initiatives while supporting City of Hope in building the internal capabilities to sustain that progress, including a new strategy team focused on training and AI.
With many foundational improvements now underway, City of Hope is entering its next phase of transformation. “Our foundation’s pretty strong,” Ms. Aucreman said, but the next level of performance “is really going to require increasingly more investments in more advanced tech, automation and really rethinking the way we do our work.”
That work cannot wait for the future. “When we talk about what revenue cycle in 2030 needs to look like, the reality is that we’re building that today,” said Ms. Amin.
Four takeaways from the discussion show what that means in practice.
1. Start small, then build a compounding, orchestrated model
Clear ownership, a strong performance baseline and two or three lighthouse use cases are the entry point for AI. Mr. Segal said organizations should begin by naming a single accountable owner and establishing a baseline so improvements can be measured and reported. At City of Hope, for example, revenue cycle AI decisions have clear ownership under Ms. Aucreman.
The real payoff comes next: building a repeatable mechanism to identify, test and scale what works. Rather than accumulating individual solutions, organizations can begin orchestrating agents across workflows so the benefits compound instead of staying siloed. This is the arc from isolated AI pilots to an integrated, action-oriented operating model.
2. Redesign the workflow before you scale it
Scaling requires a clear understanding of the work itself: the data, decisions, exceptions and third-party integrations that shape the workflow. Mr. Segal emphasized the importance of understanding those processes and handoffs before applying AI, including where outside parties are involved and where AI can act autonomously versus where a human needs to remain in the loop.
From there, organizations can define the future-state process, determine what AI should own versus what stays human and move from automation to agents by starting in observation mode. That foundation also helps ensure data and governance can support greater autonomy as it scales.
The alternative is layering new technology onto existing processes one point solution at a time. “You end up creating silos for yourself,” Mr. Segal said. Redesigning the workflow first creates the foundation for AI to work across the process rather than automate isolated pieces of it.
3. Shift your workforce from doers to thinkers
As AI takes on more execution, the work people do will need to evolve. Mr. Segal described a future operating model built around smaller teams focused on three strategic roles: Exception Operations, handling what agents cannot; Product and Learning, reviewing agent output, closing the loop and identifying improvements; and Governance and Platform, managing agent health, capacity and performance much like a call center manages its workforce.
Preparing for this shift requires more than putting AI tools into employees’ hands. Organizations need to distribute the strategic capability to use, shape and improve those tools across the workforce. Investments in data literacy, vibe coding and citizen development can give teams the skills to build their own solutions and move AI capability closer to the work.
For Ms. Aucreman, that development needs to start with leaders. Revenue cycle leaders often bring deep subject-matter expertise, but the future model will place greater demands on critical thinking, leadership and persuasive communication, particularly as they influence IT, physicians and finance without formal authority. Strategic thinking itself requires practice, she said: “We really need to transition ourselves and the teams we lead from being doers to being thinkers.”
4. Redefine the value equation and how you measure success
The value case for AI extends beyond labor savings. Revenue capture and protection represent a larger opportunity, making it important to select use cases based on their ability to improve financial performance as well as efficiency. “Cost is a component of it, but the revenue lift, the leakage prevention, that really needs to be the focus,” Mr. Segal said. Ms. Aucreman pointed to the same opportunity in elevating net revenue and accelerating cash.
The measures of success will need to evolve along with the value equation. As AI becomes embedded in revenue cycle operations, organizations can move from hindsight-focused dashboards toward action-oriented command centers that surface what needs attention and enable teams to respond. Adaptability itself also becomes an important KPI: not only whether AI delivers a return today, but whether the operating model can continuously learn, adjust and improve.
Where leaders go from here
The path to a future-ready revenue cycle does not require changing everything at once. It starts with an honest assessment of current capabilities, deliberate choices about where to begin and a willingness to build the operating model alongside the technology.
Ms. Aucreman encouraged leaders to start making those changes now rather than waiting for the future model to arrive. “It’s better to front load the pain, do it now versus waiting until it’s here and then you’re stuck having to react,” she said.
The organizations that begin building the ownership, workflows, workforce capabilities and measures of value now will be better prepared for a revenue cycle increasingly shaped by AI.
