Where AI, RCM, and the Realities of Care Collide: What Healthcare Leaders Are Saying

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Author Andrew Finck is a sales manager at CSG Forte.

Over the past few months, I’ve found myself in conference halls, boardrooms, and hallway conversations with healthcare executives who are all circling the same set of questions:

  • How do we deliver better care and better patient outcomes?
  • What role should AI play?
  • How do we improve services amid increasing financial pressures?

It’s been an energizing first quarter of 2026; by attending various industry conferences, I’ve learned that the healthcare industry outlook can change almost daily. For example, McKinsey’s January 2026 outlook says the industry is facing “successive waves of challenging trends,” with financial pressure across multiple segments, including payers affected by higher utilization, regulatory actions, and coverage shifts. Similarly, PwC reports that healthcare executives are entering 2026 in a period of “heightened strategic uncertainty,” driven by rapid technology change, coverage volatility, and recent changes to federal policy priorities.

And as someone who’s spent nearly two decades embedded within healthcare, payments, and revenue cycle management (RCM), I’m struck by how quickly the conversation is shifting—and by how much clarity we still need.

AI is (almost) everywhere, but it’s not everything

The dominant theme this year is unsurprising: AI.

Leaders across industries are imagining a world where technology does what humans don’t have the time or capacity to do. AI accelerates documentation, eases administrative burdens, improves claim accuracy, and creates a smoother patient experience from intake to payment to follow up.

In every industry-related conversation, I hear similar excitement: AI could finally free caregivers to be caregivers again.

It could strengthen the electronic trail clinicians rely on, provide structured documentation that supports coding, and help patients feel seen, heard, and understood. By allowing AI to smooth administrative tasks, providers can take more time with their patients to really understand their main and underlying issues. In fact, AI has the potential to transform RCM from a reactive back-office function into a real-time intelligence engine that drives financial stability.

But I also hear leaders expressing caution—and it’s warranted. For example, many states still operate under two-party consent rules for recordings, limiting providers’ ability to capture information from clinical encounters. Leaders aren’t just asking, “Can AI do this?” They’re asking, “How do we deploy AI responsibly, legally, and sustainably?”

And that’s the right question. We can’t be enamored with technology for technology’s sake. Implementation matters. Workflows matter. Ethics and compliance matter. Unfortunately, AI is not yet capable of keeping accurate, comprehensive medical notes, and providers would benefit greatly from recording patient encounters to ensure accuracy. This is especially true for patients who have dementia, Alzheimer’s, are elderly or have non-age-related memory issues, and newly diagnosed patients with cancer and other complicated issues. Recording patient visits provides caregivers and loved ones with needed information so they can help more effectively.

I also regularly hear healthcare leaders express a desire to act boldly without losing sight of operational challenges created by today’s regulatory environment. I believe patients should be allowed to record their medical visits if they want to, especially so they can review the information later and share it with caregivers. Research cited by the National Institutes of Health suggests recordings can improve patients’ understanding, recall, satisfaction, and treatment adherence, and they are often shared with family members or caregivers. There is also some evidence that better communication and transparency can help reduce malpractice risk.

Under the hood: billing, coding, and the claims reality

While AI steals the headlines, the operational realities of healthcare administration remain stubbornly unchanged.

The truth is, most host hospitals still run on the same core systems they’ve used for years. These outdated platforms lack modern capabilities and can’t easily be upgraded. That means smaller practices are often left struggling to bill and collect payments effectively. As the capability divide widens and hospital systems are unable to keep up with the latest technology, many have to outsource those solutions just to keep costs low.

Executives consistently bring up the same pain points:

  • Inconsistent coding quality
  • Claims getting kicked back for avoidable errors
  • Lag times between treatment, submission, approval, and actual cash hitting the account
  • Staff burnout from chasing denials
  • A constant push-pull between wanting innovation and simply trying to stay afloat

And, these days, they’re adding new ones. Recently, executives I speak with have begun reporting an insidious and shocking issue: Some payers are denying valid claims if they are submitted without proof that the patient paid their co-pay in full on the date of service.

AI can help, but only if the underlying infrastructure is ready for it. And, unfortunately, standardization remains a major barrier. Ensuring systems “code it right the first time” and send aligned, accurate claims is still at the top of leadership’s priority list.

This isn’t glamorous work. But it’s work that determines whether smaller practices survive, and whether larger systems unlock liquidity for modernization.

The question no one wants to ask out loud

Across the conversations I’m having, a quiet theme keeps emerging:

How do cash-strained practices invest in AI-powered transformation when they’re already fighting to get paid on time from both payers and patients?

A difficult—but necessary—tension exists between stability and innovation. Smaller practices in particular feel squeezed. They need stability. They need predictable cash flow.

They also want automation. They want efficiency. They want to future-proof their operations. It’s at that intersection the tension is felt most acutely: by practices operating on razor-thin margins that need predictable cash flow but find themselves facing enormous uncertainty around reimbursement.

That’s where payments and RCM modernization become essential.

When practices streamline payment processes, accelerate reimbursements, and unlock predictable cash flow, they free up the operational and financial bandwidth required to adopt the next generation of tools. AI becomes the second leap, not the first.

For many organizations, the path toward AI starts with something far less futuristic: fixing the payment experience today so they can afford the innovation of tomorrow.

Why these conversations matter to me

My background in healthcare consulting taught me to see our industry not just as a collection of systems and processes, but as an ecosystem of real people working under immense pressure. Today, I’m still in rooms with physicians, hospital operators, RCM leaders, and technologists, although I’m viewing their situations through a different lens.

I’m listening for the pain points. The bottlenecks. The opportunities that unlock not only financial performance, but the human experience of care delivery.

And these conversations have reinforced something I’ve believed for years:

Technology can absolutely ease clinicians’ burden and accelerate care, but only if we address the financial and operational realities that stand in the way.

That’s where payments can be a quiet hero.

That’s where small workflow improvements create a disproportionate impact.
That’s where the future is built. Not with one giant leap, but with many intentional, well-informed steps.

Even a few days’ reduction in A/R from payers or patients can make a real difference, and there are hard numbers to prove the operational friction delays create: The American Hospital Association, for example, estimates hospitals spent $43 billion in 2025 trying to collect payment insurers owed for care already delivered, including nearly $18 billion spent overturning claims denials alone. What’s more, the average hospital employed about 64 administrative and billing staff dedicated to these functions, about 6.5% of total hospital employment.

These aren’t just back-office metrics. They’re a reminder that when payment and revenue-cycle processes work better, providers gain breathing room, clinicians face less friction, and organizations are better positioned to move care forward—especially as physician practices and other providers contend with rising costs and slower, shrinking reimbursement.

Looking ahead: let’s keep this conversation going

I’ll be spending the rest of this year doing what I love most: sitting with leaders across the healthcare spectrum, asking the hard questions, and exploring what sustainable transformation actually looks like.

If we do this right, five years from now, we won’t just talk about AI in healthcare.
We’ll be actually operating a system that:

  • Pays providers faster
  • Supports better clinical decisions
  • Reduces administrative waste
  • Improves patient trust and patient care
  • And gives clinicians the space to do what matters most

I’m eager to continue this dialogue—not from a place of hype, but from a place of possibility rooted in reality.

If you’re leading through these challenges right now, I’d love to hear what’s on your mind. The best solutions in healthcare have never come from technology alone; they’ve come from honest conversations and open dialogue. They’ve originated and blossomed via forward-looking conversations among people who care about what comes next.

I care, and I know you do, too. Let’s keep talking.