Every revenue cycle leader is asking the same question: where does AI actually fit? To find out, RevSpring recently surveyed CIOs and IT leaders at Epic organizations about where they’re ready to let AI take on real work, what’s holding them back, and what they’re prioritizing next.
The findings point to one clear theme: this is a governance-first moment, and the biggest near-term opportunity sits at the front end of the patient financial journey.
Patient communication and insurance/authorization workflows showed the strongest readiness for AI, each cited by 9 of 11 Epic CIOs and IT leaders surveyed. Financial workflows followed (5 of 11), and only one leader said they weren’t ready for any AI autonomy.
Security, privacy, and compliance are the biggest barriers, followed by EHR integration complexity. In RevSpring’s survey, leaders also identified bidirectional EHR integration and closed-loop confirmation as critical capabilities for AI vendors.
AI governance has matured quickly. Seven of the 11 leaders surveyed already have formal, enterprise-wide governance programs in place. Transparency, explainability, and human oversight ranked as the most important safeguards, ahead of compliance and security. Looking forward, governance and oversight remain top priorities, reflecting a clear preference for AI that behaves predictably rather than “drifting” as it learns.
Where does agentic AI create advantage first? Freeing staff capacity by reducing repetitive work (5 of 11). But that advantage depends on trustworthy inputs: patient identity/demographic accuracy and consent/communication-preference data were flagged as the biggest limiters today.
The same constraint surfaces on the care navigation side of the house. If an AI agent gives the wrong provider, the wrong network status, or a dead-end appointment, the consumer loses trust and the organization suffers the impact. Provider data changes constantly, nearly 1 in 3 physicians changes affiliations or locations every year. An agent that answers without a trusted data set will send patients to retired providers, wrong networks, and even outdated locations.
The front end is where adoption is most ready to happen. And because integration and governance are the gating factors, the intelligence has to live inside the systems staff already use, with humans in control of the decision.
Taken together, these findings point to a practical model for applying AI at the front end of the revenue cycle: governed intelligence embedded directly into existing workflows. Pre-service scoring offers one example of what that can look like in practice.
AI in the revenue cycle doesn’t win by being clever. It wins by arriving at the right moment, inside the system of record, and then getting out of the way; a design constraint, not a feature list. Preservice scoring that integrates into Epic leaves Epic as the system of record and writes discrete scores, notes, and composed documents back into the chart. The round trip is the point: insight is in the chart before staff need it.
The more useful starting point isn’t the model, it’s the decision a registrar is about to make. Four signals are enough to change it: how likely a patient is to pay, whether they are likely to qualify for financial assistance, whether they are likely Medicaid-eligible, and what monthly amount they can realistically afford. Notably, none of that requires credit data or another health system’s patient behavior. A distinction that can matter significantly to compliance reviewers
The gap between a predictive score and an operational one is whether it terminates in an action. Each signal should map to a next-best step inside the Epic workflow; prompting staff at registration, presenting personalized options, triggering the right outreach. Account indicators that spotlight guarantor status preservice make that concrete: route to financial assistance, verify an address, or optimize a payment plan before the balance ever ages.
Return to the two barriers leaders named: security and compliance, then integration complexity. Both are architectural problems, which means they are solved before the first score is generated, or not at all. A bidirectional interface that hydrates the patient record in real time addresses them directly; so does daily batch scoring by file exchange, which scores upcoming accounts without staff involvement.
This isn’t a hypothetical trade-off. It played out publicly this week: OpenAI announced that healthcare organizations can now connect Epic environments to ChatGPT for Healthcare, letting clinicians pull chart data into a chat workspace. Access runs read-only, with nothing writing back to the record. It’s a useful real-world marker of the exact fork in the road our CIOs are describing: a tool can read the chart, or it can close the loop back into it, and those are different architectures with different value.
The organizations moving fastest on AI are not the ones with the most ambitious models. They are the ones that settled integration and governance first, then pointed proven intelligence at a narrow, high-value decision: who can pay, who needs help, and how best to reach them. That is a less exciting story than autonomous AI; and a more durable one.
That principle holds across the full patient journey, not just the revenue cycle. RevSpring’s MCP does not replace a health system’s existing AI agent, it is complementary, grounding their agent with better data: clinical taxonomy, provider directory depth, real-time insurance and eligibility, and cost data.