Every conversation about AI in the revenue cycle eventually turns to automation.
I think we’re asking the wrong question.
The greatest opportunity isn’t simply helping providers process claims more efficiently. It’s helping organizations realize value from technology investments faster by reducing operational friction before revenue cycle workflows are ever fully operational.
In my work with provider organizations, I’ve seen firsthand that some of the biggest barriers to financial performance don’t start with claims workflows. They begin much earlier, during implementation, onboarding, enrollment, workflow adoption, and even major system transitions. Those operational processes can determine whether teams see value quickly or spend months trying to gain traction. That’s why I believe the bigger opportunity for AI is to help organizations see results from their revenue cycle investments sooner.
The hidden revenue cycle nobody talks about
Healthcare leaders invest in technology to solve problems, improve performance, and create measurable outcomes. Yet the period between purchasing a solution and realizing value is often longer and more complicated than expected.
That period is a revenue cycle in its own right. Before the first claim is ever submitted through a new system, organizations must implement the software, configure workflows, enroll providers, train staff, and drive adoption. Every delay extends the time between investment and financial return, yet this operational journey rarely receives the same level of attention as the claims processes that the new technology is meant to support.
Anyone who has been involved in a major implementation understands this reality. Workflows must first be adjusted for the technology, and even when the solution performs exactly as intended, organizations can struggle to realize value if adoption is slow or operational challenges create friction along the way.
We’ve seen provider teams spend months evaluating solutions designed to improve financial performance, only to encounter delays during implementation or workflow adoption. The challenge isn’t deploying technology. It’s orchestrating people, workflows, and systems so technology can deliver the outcomes it was purchased to achieve.
Why provider expectations are changing
Recent industry-wide disruptions have reminded healthcare organizations how quickly operational disruptions can become financial disruptions. Large-scale data and workflow interruptions exposed just how dependent teams are on connected systems, coordinated processes, and reliable access to information.
As a result, many healthcare leaders are evaluating technology investments differently than they did even a few years ago. Functionality still matters, but resilience, continuity, implementation risk, and speed to value now carry equal weight in the decision. Leaders want to understand how quickly teams can become productive, how easily workflows can be adopted, and how effectively organizations can maintain momentum during periods of change. They want confidence that investments will not only perform as promised, but that they can be implemented and scaled in a way that supports meaningful outcomes.
This shift is also changing how organizations think about AI. The first generation of AI focused primarily on automating individual tasks, making existing processes faster or less labor-intensive. The next generation is different. It has the potential to create truly connected workflows across the revenue journey, which is a fundamental shift. Revenue cycle performance is rarely determined by one task. Rather, it’s determined by how effectively dozens of interconnected decisions come together across implementation, patient access, authorization, claims, reimbursement, and ongoing optimization.
Reducing friction across the revenue journey
AI can absolutely help automate revenue cycle workflows, and many organizations are already seeing that value, but automation alone doesn’t fully address the challenges that have the greatest impact on financial performance.
Organizations often struggle because the path from technology investment to operational value is longer and more complex than it should be. The goal isn’t simply to automate existing work; it’s to eliminate the work that never needed to exist in the first place. That’s where AI has the potential to create a different kind of impact by helping organizations remove the challenges that slow progress in the first place. From onboarding delays and enrollment challenges to disconnected workflows, AI can reduce the small points of friction that add up over time to create larger consequences.
This perspective is increasingly reflected in how revenue cycle leaders talk about AI. In our own research, leaders expressed a desire to explore AI’s potential across interconnected workflows, rather than within a single revenue cycle process or task.
From reactive to proactive
A significant portion of work for many revenue cycle teams still happens after a problem is identified. When something goes wrong – a denial, a delayed payment, a workflow issue affecting performance – staff then shift into problem-solving mode to understand it, address it, and limit the damage.
Yet, by the time some problems become visible, organizations are already feeling the impact. Greater visibility gives teams an opportunity to respond earlier and avoid larger downstream consequences.
Of course, technology doesn’t replace the expertise required to manage these complex processes. Healthcare teams still depend on the experience and judgment of the people doing this work every day. AI’s greatest value is giving people the visibility and context to make better decisions sooner.
Data still determines success
Connected workflows require connected data. Organizations often ask whether they’re ready for AI. A better question is whether their data is ready. AI cannot orchestrate work across disconnected systems if each step sees only part of the picture. An organization’s AI strategy cannot exist separately from its data strategy – the two are intertwined.
Yet, like any technology, AI is limited by the information it has access to. When teams lack visibility across clinical, operational, financial, and payer data, it becomes much harder to generate meaningful conclusions. The strongest results come from organizations that take a broader view of the revenue journey and recognize that connected data supports better decisions and a clearer picture of performance.
Rethinking how we measure success
As AI adoption expands, how teams measure success is changing. Productivity gains and labor savings only tell part of the story. The more meaningful question is whether AI is helping providers realize value faster. Is it reducing implementation challenges? Accelerating onboarding? Helping teams adopt new ways of working more effectively? Is it creating measurable improvements in financial performance faster?
Healthcare has spent decades trying to make the revenue cycle more efficient. AI gives us the opportunity to rethink how the work gets done altogether. The organizations that realize the greatest value won’t simply automate more tasks. They’ll remove operational friction, connect workflows, and shorten the distance between investment and outcome. That’s how AI moves beyond efficiency and becomes a catalyst for stronger financial performance, more resilient operations, and ultimately, better care.
