For most people, healthcare feels complicated everywhere except the exam room. We trust our doctors, we get the care we need, and then the bills arrive, often filled with codes and explanations that don’t explain much at all. Behind the scenes, the billing process is even more complex: dozens of payer portals, constantly shifting rules, and thousands of small administrative decisions that determine whether a claim gets paid quickly, slowly, or not at all.
I learned this early. Growing up, I spent summers helping my parents, who are both healthcare providers, with billing for their small clinic. I watched them battle denials, decode payer language, and spend hours on hold trying to understand why a claim that “should have been covered” wasn’t. What frustrates me is that we’ve normalized this as ‘just how billing works,’ even though it clearly doesn’t have to.
Today, that pressure has only grown. Denials have risen for three years in a row, while administrative requirements have been tightening. And providers spent more than $25.7 billion fighting claim denials last year, even though 70% of those denials were eventually overturned. The system isn’t broken because denials are unwinnable. It’s broken because the people trying to fix them are overwhelmed.
The question now facing the healthcare industry — and the one AI is finally starting to answer — is simple: What if we could give billers and providers the same kind of intelligent tools that payers have used for years?
AI Is closing the automation gap between providers and payers
Insurance companies have spent the last decade quietly building sophisticated automated systems: instant eligibility checks, auto-denials, document scanning, and rules engines that flag even the smallest mismatch.
Providers, meanwhile, are still expected to navigate all of that manually.
That imbalance has created a gap: a widening divide between what payers automate and what providers must do by hand. It’s not sustainable. And it’s why AI is beginning to transform the revenue cycle in a very real way.
We’re seeing providers use AI to:
- Understand and resolve denials faster – Instead of digging through PDFs, billers can now get instant explanations of denial codes, coverage rules, and required documentation, reducing hours of research to seconds.
- Prevent errors before claims go out the door – AI can analyze compatibility between CPT and ICD codes, check for missing modifiers, identify prior authorization needs, and compare submissions to payer policies.
- Automate repetitive follow-up work – The average practice logs into 5–20 payer portals just to track claim status. AI can now monitor these steps automatically, flag issues early, and help teams prioritize which denials to fight first.
- Generate payer-ready appeal letters – Since 70% of appeals succeed, speed and consistency matter. AI can now draft structured, compliant letters in minutes, helping teams recover more revenue with less effort.
These aren’t hypothetical. This is happening right now among practices using AI-driven tools. Providers are seeing shorter A/R cycles, fewer denials, and faster cash flow, driven by the simple fact that they finally have systems that can help them keep up.
The human impact: AI isn’t replacing billers, it’s elevating them
There’s a misconception that AI is about replacing people. In reality, the organizations succeeding with AI are the ones using it to empower their teams, not shrink them.
RCM and billing work is highly skilled, but much of the day-to-day is repetitive: checking statuses, tracking rules, writing appeal letters. These tasks drain time without adding value.
AI flips that dynamic. By handling repetitive, rules-based tasks, AI gives billers time back to do the strategic work that actually moves revenue:
- reviewing complex cases
- analyzing trends
- improving documentation
- advising providers on how to prevent denials in the first place
In other words, the best AI doesn’t eliminate billers; it turns them into “superbillers,” capable of doing more with less burnout.
Why this matters for patients
Billing frustration isn’t just a provider problem; it’s a patient problem too.
Every delayed claim, every error, every confusing denial ultimately affects the person receiving care. Enter AI, helping to reduce that friction:
- Faster resolution of issues – A denial that once took weeks can be overturned in days with the right tools.
- Less surprise bills – Clear answers about coverage upfront means less downstream surprises for patients.
- More financial transparency – AI can provide eligibility requirements, coverage limits, and patient responsibilities before care is delivered.
- Less provider burnout – When billing teams aren’t drowning in admin work, they can stop chasing payments and focus on patients.
The result is a smoother experience for both providers and patients.
Photo: claudenakagawa, Getty Images
Roshan Patel is the Founder & CEO of Arrow, the AI Operating System for modern revenue cycle teams, helping reduce denials and accelerate collections from one platform. Built for billers, loved by CFOs, Arrow enables healthcare organizations to unify their entire revenue cycle — empowering teams, data, and AI to work together to keep every claim moving, reduce rework, and deliver predictable revenue.
Roshan founded Arrow after witnessing firsthand the financial and operational strain delayed payments and denials place on healthcare providers. His focus is building foundational infrastructure that allows revenue teams to operate with clarity, speed, and confidence as healthcare billing grows more complex.
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