Advertise on podcast: RCM Consultants - Medical Billing Marketing, RCM Automation, Data, and AI.

Everyone is telling you to put AI into your billing operation, but what happens when it makes a bad decision and nobody notices until the damage is done? For many billing company owners, that risk is the real obstacle to automation. In this episode, Sean McSweeney challenges the hype around AI agents and so-called agentic AI. He explains why even the best models struggle with the multi-stage, rule-heavy reality of revenue cycle work, and why deterministic RCM workflows paired with machine learning are often the safer path to automation you can trust. What You'll Learn: Why a simple car wash question stumps the most advanced AI models, and what it means for your billing processes What is really behind most AI failures in RCM, and why more AI will not fix it How deterministic RCM workflows differ from AI agents, and why they are easier to audit and control Where large language models still belong in your operation Key takeaway: AI is a powerful tool, not a magic wand, and deterministic RCM workflows built on exhaustive documentation are the foundation that makes it work.

You're adding AI to your billing workflows as fast as you can, but when did you last stop to think about superintelligent AI risk beyond your own back office? Sean McSweeney steps outside the usual RCM playbook to discuss the book If Anyone Builds It, Everyone Dies by Eliezer Yudkowsky and Nate Soares. He explains why he finds its argument so unsettling, why he rarely speaks this bluntly about the subject, and why he thinks business owners who use AI every day should understand superintelligent AI risk firsthand. What You'll Learn: Why does Sean think superintelligent AI risk deserves the same attention as nuclear proliferation?What did the so-called Godfather of AI do that signals how seriously insiders take the danger?Why do greed and competition between companies and countries make the problem so hard to solve?What can you do, even as a non-expert, to be part of the conversation?Pick up the book, form your own view on superintelligent AI risk, and share this episode with someone who needs to hear it.

A prospect fills out your form on Tuesday morning, and the earliest slot on your calendar is Wednesday afternoon. By then, another company may already have them. If you're trying to grow your billing company, your medical billing lead response time may be quietly costing you deals you never knew you were competing for. This episode challenges the habit of making prospects wait, even when same-day calls are operationally hard. Sean explains why lost deals are so hard to measure, and shares the industry numbers that make the case for speed. The conversation also covers friction, gatekeepers, and what your lead response time says to buyers. What You'll Learn: How much does a same-day call really change your close rate compared with next-day?Why do so many prospects talk to your competitor before ever showing up to your call?Do you need to close on the first call to win the deal?How can you cut friction without losing the information you need to qualify leads?Fast, live access is one of the cheapest ways to improve results from the same marketing budget, so tighten your medical billing lead response time before you spend another dollar on leads.

Your prospect just heard a pitch from a startup with hundreds of millions in funding and a promise of zero humans. Should a medical billing AI pitch like that keep you up at night? In this final part of our series on competing with AI-native companies, we look at two heavily funded examples, one in revenue cycle management and one in claims administration. We challenge the hype, question the funding logic, and reveal what these companies' own pivots say about the model. What You'll Learn: Why did well-funded teams with elite investors struggle to make AI-native billing work?What does a "hybrid" model really mean when you look closely?Why does venture funding push startups toward the riskiest approach?How can a smaller company win deals against a competitor that can buy business?The takeaway: you don't need to fear medical billing AI startups, because a modular, iterative technology strategy built on strong operations is the stronger long-term play.

Your competitors are pitching "AI-native" platforms, and you're wondering whether your piecemeal approach to RCM automation can possibly keep up. Should you rip everything out and rebuild around one giant AI system? In part two of this series, Sean McSweeney challenges the idea that AI-native is automatically better. He explains why a modular approach to RCM automation can be lower risk, more flexible, and easier to improve as technology changes. What You'll Learn: Why a single AI-native platform can make implementation harder, riskier, and more expensiveWhether going modular means giving up unified, centralized dataWhat AI's blind spots mean for how much of your workflow you should hand overWhat a fully autonomous, self-correcting AI system would actually requireStart automating the pieces where AI works today, and expand your RCM automation iteratively instead of betting everything on a single platform.

Private equity keeps funding well-capitalized startups that promise a fully automated future, and you are left wondering whether your medical billing company can survive against an AI native RCM competitor with nine figures in the bank. Sean McSweeney challenges the fear and hype around AI native RCM platforms. He separates the investor pitch from operational reality and draws on his own early mistakes entering medical billing to explain why a company built from scratch may not be the unstoppable force it appears to be. What You'll Learn: What does AI native actually mean, and is it really all AI?Why do tech-first teams so often underestimate the complexity of billing and claims processing?How does building for yourself differ from selling one platform to everyone?What advantage might an existing operation have that a startup cannot buy?Competition from AI native RCM companies is real, but this episode makes the case that you do not need to panic about being wiped out overnight.

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