Strategic priorities focus on AI, workforce concerns
Revenue cycle leaders we interviewed at Becker’s CEO + CFO Roundtable this year were consistently clear about one recurring theme – the traditional, labor heavy revenue cycle model is no longer tenable. Margin compression, staffing shortages, payer friction and rising patient expectations are converging, and AI has moved from the sidelines to center court.
Across systems large and small, executives described the same reality; they must modernize revenue cycle operations rapidly but with care and discipline to conquer systemic complexity and growing risk.
We spoke with more than two dozen healthcare executives at the Becker’s event. What follows are theand concerns discussed in our conversations – and what they have to say about the future direction of healthcare revenue operations.
Transforming from a labor-first to tech-first revenue cycle
For most revenue cycle leaders we spoke with, the starting point is blunt. The math on labor-intensive revenue cycle operations is broken. Health systems are navigating persistent staffing shortages, wage inflation and ongoing margin pressures. Many organizations simply cannot hire or retain enough people to manage coding, denials, prior authorization and back-office workflows adequately. As a result, executives increasingly see technology as the only sustainable way to keep up with volume and complexity.
“What these tools do is allow us to simplify that work and use an AI agent to streamline processes by quickly accessing and analyzing information that would take hours of somebody’s time.”
Dan Liljenquist, Chief Strategy Officer, Intermountain
The healthcare revenue cycle transformation goes far beyond just adding some automation on top of existing processes. Leaders describe a transition from labor-first to tech-first models, where AI, automation and advanced analytics handle the bulk of repetitive rules-based work, and people are redeployed to higher-value tasks and roles.
Revenue cycle use cases that demonstrate real value are moving quickly from pilots to production:
- AI-enabled denials management and appeals reduce manual touch and dramatically shorten cycle times.
- Automated coding and pre-coding workflows accelerate throughput and reduce reliance on hard-to-recruit specialists.
- Workflow automation eliminates redundant steps, drives new efficiencies and reduces administrative waste.
Importantly, many executives see ambient documentation and AI-assisted coding as key proof points for transformation. When AI tools can demonstrably reduce documentation burden and improve accuracy, they generate organizational confidence and support for broader transformation.
For larger systems, this often means redesigning roles at scale, rethinking span of control and investing in upskilling. For smaller and rural organizations, it is less a strategic choice and more a necessity; technology becomes the only viable way to backfill roles that simply cannot be hired.
Making AI adoption safe through disciplined governance
If technology is the answer to the labor problem, governance must be the answer to the risk problem. Executives repeatedly emphasized that the conversation has shifted from should we use AI to how do we use AI safely, responsibly and in line with our mission? This is likely due to recognition that revenue cycle AI touches sensitive information and relationship areas – clinical documentation, coding accuracy, payer interactions and ultimately, patient trust.
To manage this reality, organizations are standing up formal AI governance structures and aligning them tightly with data management.
Common elements include:
- Multidisciplinary AI committees that bring together finance, clinical leadership, compliance, legal, IT, security and frontline operations.
- Clear criteria for prioritization, where projects are assessed based on impact on patients, financial value and staff experience – not just on who shouts loudest.
- Guardrails are built in from intake, including standards for model evaluation, bias and error monitoring, auditing and escalation paths.
“We’ve formed an interdisciplinary committee. A lot of what that committee does is prioritize. What we’ve really done in a thoughtful way is set up metrics so that we can evaluate and rank the projects so that we can figure out what we should focus on going forward.”
Nick Barcelona, CFO, WVU Medicine
Executives are now realizing that AI is more than just a point solution, it is a powerful enterprise operating capability that must be carefully managed as it is assertively deployed. Governance frameworks that were once optional are quickly becoming table stakes, particularly as regulators, boards and internal stakeholders ask more pointed questions about risk, transparency and accountability.
For revenue cycle leaders, having a strong governance story is increasingly essential to securing investment, maintaining trust with clinicians and scaling AI for true transformation.
Keeping AI human-first and serving staff
Even as they push for automation, healthcare executives are acutely aware of the human dimensions of AI. Workforce anxiety in the industry is real. Many staff worry that AI is being deployed to replace them, not support them.
Revenue cycle leaders are therefore framing AI explicitly as an augmentation tool, something that changes the work, not something that eliminates the worker.
Examples that resonated strongly with staff include:
- Ambient documentation that listens to clinical encounters and drafts notes, giving time back to clinicians and reducing burnout.
- AI-supported coding and pre-coding in which humans remain the final decision-makers, but review and validate suggestions at speed rather than coding from scratch.
- Denials and appeals automation that removes repetitive, low-value steps so staff can focus on resolving complex cases and high-impact issues.
Many executives are proactively redefining roles rather than trimming headcounts. Staff are being upskilled into new positions such as documentation quality specialists, AI workflow reviewers and payer strategy analysts that didn’t exist just a few years ago.
“At least from our perspective, we’re very focused on the human element. And so, our strategy is more along augmented AI, how do we help our people perform better? Not necessarily autonomous AI where we’re replacing people.”
Patrick O’Shaughnessy, CEO, Catholic Health
Clarity of intent matters. Where leaders communicate that AI is there to reduce friction, improve job satisfaction and elevate human judgment, they see faster adoption, better engagement and fewer internal roadblocks. A human-first posture is becoming a competitive advantage in both retention and change management.
Rewiring payer–provider relationships
No conversation about the healthcare revenue cycle can ignore payer dynamics. Leaders described current payer–provider relationships as too often adversarial, inefficient and costly for both sides. But there are signs of a cautious shift. Some organizations are moving from combative postures to more data-driven, performance-based collaboration. Instead of wrestling claim by claim, they are working with payers to address underlying process issues and identify more areas where cooperation and collaboration are mutually beneficial.
“I think in Michigan, we tend to have a better relationship between the health insurance community and the provider community. We have a formal relationship, for example, with Blue Cross Blue Shield of Michigan. That allows us to sit around a formally sanctioned table with the health insurer and their team and at least have an open conversation about the issues that we need to address together.”
Brian Peters, CEO, Michigan Health and Hospital Association
Executives highlighted several emerging practices to improve payer relationships:
- Joint performance dashboards that track clean claim rates, denial patterns, turnaround times and payment accuracy in near real time
- Regular operational reviews where both sides examine the data, agree on root causes and commit to specific process changes
- Automation at the payer–provider interface, such as tools that assemble high-quality, data-driven appeal letters in minutes or pre-bill claim audits that minimize denials, DRG downgrades and underpayments.
The long-term vision described by many leaders is a continuous accountability model with fewer surprises, more transparency and shared responsibility for fixing systemic issues.
Bridging incumbents and insurgents to unlock value
Finally, executives appear to be rethinking the industry’s incumbents versus insurgents narrative. Large health systems and established vendors hold scale, trust and data. New entrants and AI-native startups offer speed, creativity and fresh technology solutions.
Rather than perpetuating the status quo, forward-looking leaders are leaning into a more nuanced approach that emphasizes collaboration over competition. On one side, incumbents are trying to act like insurgents within their own walls, building internal innovation hubs and being more agile in how they pilot and scale new capabilities. On the other side, insurgent partners are being asked to move toward deeper, more integrated relationships with providers that respect compliance, security and operational realities.
“I think incumbents will have an advantage for a while, partly because the data already exists inside. The data to train AI for the organization is already inside our firewalls, inside our environments. And so, I think we and other incumbents have an advantage for a time if we lean into that.”
Dan Liljenquist, Chief Strategy Officer, Intermountain Health
Data sits at the center of this dynamic. Most health systems rightfully view their data as a unique strategic asset and are committed to protecting it. At the same time, they recognize the risk of being left behind if they do not harness that data in partnership with technology innovators to transform operations. The emerging model is less about best-of-breed point solutions that invite vendor churn and more about long-term, values-aligned partnerships that co-create solutions and share risk and reward.
Remaking the fabric of the revenue cycle
Taken together, these top organizational priorities signal more than a series of incremental improvements. Revenue cycle leaders are transforming the operational fabric of healthcare around technology, data and collaboration. They are moving from labor-first to tech-first models to address structural workforce and margin challenges. Building robust governance to ensure that AI is safe, reliable and aligned with their mission. Deliberately keeping AI human-first to maintain trust and engagement. Using data and automation to reshape payer–provider relationships and bridging the strengths of incumbents and insurgents to unlock AI’s full potential.
For revenue cycle executives, the path forward is demanding, but the direction is clear. The future will belong to organizations that can combine bold technology adoption, disciplined governance and deep human-centered leadership into a cohesive strategy.
