AI Adoption in Hospital RCM Is Surging. Operations Are the Real Bottleneck.
A new Fierce Healthcare article highlights a shift that many hospital finance leaders are already feeling:
interest in AI for revenue cycle management is no longer theoretical. It’s mainstream.
According to a recent HFMA Pulse Survey conducted with AKASA, 80% of health systems are now exploring, piloting, or implementing generative AI for RCM, up sharply from just two years ago. Roughly 40% are already live or in pilot mode.
What’s slowing things down isn’t fear of AI. It’s operations.
Where AI adoption is actually happening
The strongest use cases today are not flashy. They’re practical.
Finance leaders point to:
Clinical documentation accuracy
Coding completeness
Pre-bill claim review
Mid-cycle automation
These are the parts of the revenue cycle where small errors quietly turn into large revenue losses.
The survey found:
89% of leaders say missed or inaccurate codes impact revenue
51% say the impact is significant
On average, leaders estimate 8.5% of total revenue is at risk due to documentation and coding gaps
That number matters. For a $1B health system, that’s roughly $80M in exposure, before denials, before bad debt, before payer disputes.
Why progress is uneven
Adoption is not happening evenly across the industry.
Large systems are moving faster. Midsize systems, particularly those between $500M and $1B in net patient revenue, are more cautious.
The barriers are consistent:
Cost and budget pressure
Limited IT capacity
Integration complexity
Security and privacy concerns
Difficulty proving ROI early
One executive quoted in the survey summed it up well:
“AI isn’t the barrier. Resources are.”
This is an important distinction. Hospitals are not resisting AI. They’re trying to absorb it while managing workforce shortages, payer pressure, and regulatory change at the same time.
This matches what I forecast in RCM 2030
In RCM 2030, I argued that automation would not fail because of technology. It would stall because organizations underestimate the operational lift required to make it work.
AI doesn’t replace broken workflows.
It exposes them.
What this survey reinforces is that AI adoption is most successful when:
It integrates into existing systems with minimal disruption
Vendors do the heavy lifting, not hospital IT teams
Staff are supported, trained, and given time to adapt
Use cases focus on accuracy and prevention, not just speed
The health systems seeing early returns are not chasing “AI everywhere.”
They are targeting specific points of leakage and fixing those first.
What this means for finance and RCM leaders
The takeaway from this article isn’t that every hospital needs more AI tomorrow.
It’s that AI is becoming a standard tool in revenue integrity, and the real strategic question is how to deploy it without overwhelming already-stretched teams.
The next phase of RCM maturity isn’t about whether AI works.
It’s about whether operations are ready to support it.
Hospitals that treat AI as an IT project will struggle.
Hospitals that treat it as an operational redesign tool will move faster and see cleaner results.
This isn’t a race.
But it is a direction of travel.
And the organizations that prepare their workflows, workforce, and expectations now will have a much easier time catching the revenue that’s already at risk.
