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Most healthcare leaders know what AI is. They have heard the promises, watched the demos, and seen it get integrated into tools and reports.
But now the question of where it is really making a difference is being asked in meetings.
Not in theory, but in the practical operational daily grind. In the midst of denials, missing charges, slow follow-ups, and mounting admin pressure.
That’s where AI is already reducing denial rework, accelerating collections, and lowering cost-to-collect in high-performing revenue cycle organizations. Some hospitals are already seeing the effects while others are just starting. But one thing is certain. AI in revenue cycle management is no longer just about automation. It’s about changing how work gets done.
Where AI Is Quietly Making a Difference
Most hospitals did not implement AI with the hope of seeing major changes the very next day. It began with small steps. A solution that pointed out problems in claims. A solution that reminded coders when it seemed like the documentation was not complete. Just enough to make the burden a little lighter.
That’s where the real difference is beginning to appear, not in the big changes, but in the little day-to-day stuff.
In the case of denials, some hospitals are now alerted when something doesn’t seem right about a claim. It could be a missing signature. Or an inconsistency in the paperwork. This gives teams a chance to spot errors that can be avoided before the claim is even submitted.
In fact, a 2025 industry survey found that 83% of healthcare organizations reported that using AI in claims helped reduce denials by at least 10% within six months, improving billing accuracy and speeding up submissions.
Then there’s underpayments.
When multiple contracts and codes are at stake, small discrepancies in payment may go unnoticed. AI is now able to alert when payments are lower than expected, not next week but right away.
Even patient collections are experiencing an uplift.
Some hospitals are now using AI to determine the best time and way to contact them, whether it is through text, call, or email, based on their behavior. This has helped increase their collections without adding more pressure.
And in addition to all that, AI is assisting organizations in moving from reacting to preventing. Fewer surprises, less rework, smoother month-end closes.
In hospitals that have leaned in, AI is becoming less of a “shiny project” and more of a “quiet partner.” One that helps teams stay ahead of what used to catch them off guard.
That’s the real shift.
It’s not about replacing people. It’s about giving them the space and support to work smarter, before the problems pile up.
When AI Starts Taking Initiative
No hospital brought in AI expecting it to fix everything overnight. It didn’t arrive with a big rollout or a grand announcement. It showed up quietly. A tool that helped review claims. A prompt that caught something missing in the notes. Small things meant to make the day a little easier.
And that’s where the change has actually started.
If you talk to most CFOs or revenue cycle leaders, they’ll tell you the same thing. What keeps them up at night isn’t one major failure. It’s the steady drip of small issues. Claims that have to be touched again and again. Payments that come in just a little short. Patient follow-ups that slip because there’s never enough time.
Those are the gaps that add up. And those are the gaps AI is beginning to help with.
Flagging what’s fixable before it becomes rework
Some hospitals are now receiving nudges when something in a claim does not seem right. Perhaps a discharge note is missing. Perhaps a code does not align with what would be expected for that type of procedure. These nudges not only prevent denials, they also decrease the work that follows.
Catching underpayments when they happen
When there are dozens of contracts being managed by teams, a variance that is missed may not be noticed. However, now, with the help of AI, these discrepancies are being brought to notice in real time, allowing teams to take action while the window of opportunity is still open and not after the month-end close.
Improving collections with a more thoughtful touch
Instead of sending the same message to all patients, AI is helping in varying the timing and tone of the message based on behavior. A text message reminder at 10am may be more effective for some patients than a phone call in the evening. Small changes, big impact.
Enabling teams to shift from reacting to anticipating
Through the identification of common problems in documentation, coverage, and timing, AI is encouraging teams to solve processes at the root level before the same problem re-emerges in next week’s claims.
A recent Deloitte report on healthcare found that generative AI and automation technologies can cut the time revenue-cycle staff spend on mundane tasks in half, freeing up teams for higher-value work and helping improve operational efficiency.
That’s not because AI is magical. It’s because it fits into the cracks that people were already trying to fill.
The hospitals that are seeing results aren’t the ones with the most tech, they’re the ones using it smartly to get ahead of everyday friction.
Starting Small, Moving Smart
You don’t have to make a huge transformation to begin implementing AI in your revenue cycle. The hospitals that are experiencing the most success didn’t roll out huge initiatives, they started small.
Here’s what that can look like:
Pick One Slice of The Process – Define a scope that is clear: one payer, one facility, or a set of CPT codes. The goal is not to show how effective AI is immediately, but to alleviate a pain your team is already experiencing every week. A clear scope allows you to observe cause and effect.
Understand What’s Happening Today – Before you embark on AI, take a moment to understand how things are done today. What is your denial rate? How frequently do underpayments get through? How many times does staff handle the same claim before it goes out? How much time is spent on reviewing or correcting what should have been correct the first time?
It’s not about creating a large dashboard. It’s about understanding where time is spent and where the friction is. Once you understand that, you’ll understand where AI can help, and not just add another layer.
Start With Tools That Support, Not Replace – The best start is not a complete overhaul. Give your team some support with quiet helpers: tools that look at what is going on, identify missing documents, highlight claims that typically get edited by payers, or point out issues that come up repeatedly. These tools do not change the way your team works. They make things a little smoother and faster.
When they are working properly, employees don’t even notice them; they just notice that fewer things are falling through the cracks.
Test, Then Compare – Perform a side-by-side analysis. Use AI support for one set of claims and the current process for another. After a few weeks, compare the results for rework, days to bill, and time per claim. This will provide leadership with a clear view without having to make assumptions about the results. If the difference is significant, it will show up in the data.
Scale What Works, With Oversight – As soon as you notice the positive impact, you can start expanding to other payers, departments, or facilities. People should be involved in the process as you scale. What works in one place may not work in another. AI should be a support layer, and human judgment should guide decisions and exceptions.
A recent McKinsey report found that hospitals using AI in their revenue cycle have seen operating costs drop by as much as 40%.
But those results didn’t come from one big leap. They came from solving the next obvious problem.
That’s really the heart of it. AI doesn’t need to be a sweeping change. It just needs to help with what is slowing you down right now. Fix that. Then use that win to take the next step. And the one after that.
This is how hospitals are making progress, with less pressure, less disruption, and better results.
Wrapping It Up
Hospitals teams that are really making progress in AI in the revenue cycle are thinking about the next problem, not all problems at once.
Begin with a question: Where is your team stuck now?
It’s not “What can AI do?” but “What takes our time, slows cash, or causes rework, and how can AI help?”
If that conversation sounds helpful, we’re here to help.
In 30 minutes, we will identify 2–3 workflows where AI can reduce denial rework and accelerate cash within 90 days.
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No sales pitch. No disruption. Just practical solutions to see where AI can alleviate some of the burden.
