Podcast Summary: Balancing AI Innovation and Governance in Revenue Cycle Management

Podcast: Becker’s Healthcare Podcast
Host: Brian Zimmerman
Guest: Kanar Kokoi, Founder & CEO, Chirac Health
Date: October 13, 2025
Duration: ~20 minutes

Episode Overview

In this episode, Brian Zimmerman sits down with Kanar Kokoi, Founder and CEO of Chirac Health, to explore the responsible adoption of AI in revenue cycle management (RCM) within healthcare. The discussion centers on achieving the right pace of innovation, implementing effective governance, and maintaining a crucial "human in the loop" for successful AI integration. Together, they unpack risks, best practices, and the human factors that shape healthcare AI’s future, with a sharp focus on real-world impact and durability.

Key Discussion Points and Insights

1. Kanar Kokoi’s Background & Vision for AI in Healthcare

• [00:33] Kokoi has decades of experience blending operational and technical expertise in healthcare. His focus: bridging the gap between strategy and execution, and reducing provider burnout through innovative, sustainable systems.
  • Quote: “Innovation for me is not just about technology. It is about creating a system that is financially strong, operationally sound and supportive for the people who deliver the care.” – Kokoi [01:49]

2. Finding the Right Pace for AI Adoption in Revenue Cycle Management

• [03:11] Over 70% of health systems have implemented some form of AI in RCM, but Kokoi warns against both extremes: rushing can amplify risks; proceeding too slowly means falling behind payers that are already using AI to their advantage.
• Five pillars for thoughtful AI adoption:

  • Regulatory and Audit Environment: With increasing scrutiny and inconsistent enforcement, robust controls are essential to avoid scaling up errors.

  • Response Strategy: Combines tech innovation and policy advocacy.

  • Partner Selection: Balance between large (resources/scale) and small (agility/focus) vendors, but focus on their governance and accountability structures.

  • Testing and Transparency: Lack of standardized frameworks leaves organizations operating in silos—Kokoi highlights that there’s little data-sharing or meaningful benchmarking.

  • Human in the Loop: Essential for judgment, oversight, compliance, and managing unintended consequences.

  • Quote:
    “If you go too slow, I believe we risk being left behind.…the right pace isn’t really about speed for speed’s sake. It’s more about how thoughtful the adoption has been with guardrails and clear strategy.” – Kokoi [03:24]

  • Quote:
    “I do see many of AI technologies claiming that they'll go 100% tech proof and no human oversight. You know, this just cannot be really a function within revenue cycle.…the human guides and reviews and ensures the output is safe and it is accurate.” – Kokoi [07:08]

3. The Governance Gap: Why So Few Mature Structures?

• [08:18] Despite large-scale adoption, only 17% of organizations report mature AI governance. The lack of frameworks exposes providers to risk—what Kokoi calls the "AI bubble," where reliance on volatile vendors leaves organizations vulnerable.

• Operational resilience is often lacking—many organizations do not have processes for archiving, portability, or knowledge transfer if vendors exit the market.

  • Quote:
    “Strong governance framework is what really separates many organization that can scale AI safely from those that risk disruptions every time a vendor changes or disappears.” – Kokoi [10:35]

  • Advice:
    Balance agility (from small partners) and scale (from large partners), but always build robust governance first.

4. Training, Change Management, and the “Human in the Loop”

• [12:33] Lasting improvement depends on cultural change as much as on technology. Clinicians need assurance that AI is an aid, not a replacement. Administrators must ensure tools support compliance and quality.

• AI requires ongoing human feedback to address nuances of tone, context, and medical culture.

• Kokoi shares a case where an AI tool only improved after constant human review and iterative training.

  • Quote:
    “The machine has to be taught tone, it has to be taught context and culture.…the human in the loop is so important, because it needs that continuous feedback and iteration.” – Kokoi [13:17]

  • Memorable Moment:
    Kokoi cautions against claims that AI can fully replace a physician’s work:

    • “If a physician can be replaced by AI, then that is not a good physician…” – Kokoi [13:44]

  • Quote:
    “Implementing AI is a responsibility which requires constant validation. So…the AI adoption isn’t just about building the smarter model. It is really about embedding it into the right culture, teaching it context and continuously refining it to support the human decision.” – Kokoi [15:56]

5. Final Thoughts: AI as a Human-Driven Journey

• [17:56] Kokoi closes by emphasizing that AI in healthcare is fundamentally about people, process, and culture—not just new tools.

• Governance and human oversight are essential for sustainable value.

• Technology should amplify—not replace—human expertise.

  • Quote:
    “The AI can accelerate workflow, it can improve accuracy, it can reduce administrative burden, but it cannot replace judgment, it cannot replace empathy, it cannot replace context. My best solution…is really the best outcome comes when the technology amplifies human expert rather than replaces it.” – Kokoi [19:15]

Memorable Quotes & Moments with Timestamps

• “Innovation for me is not just about technology. It is about creating a system that is financially strong, operationally sound and supportive…” – Kokoi [01:49]
• “If you go too slow, I believe we risk being left behind.” – Kokoi [03:24]
• “Do you have the right structure in place to evaluate and monitor and hold those partners accountable?” – Kokoi [05:05]
• “I do see many of AI technologies claiming that they'll go 100% tech proof and no human oversight. You know, this just cannot be really a function within revenue cycle.” – Kokoi [07:08]
• “Strong governance framework is what really separates many organizations that can scale AI safely from those that risk disruptions every time a vendor changes or disappears.” – Kokoi [10:35]
• “If a physician can be replaced by AI, then that is not a good physician…patient care is just something that cannot be replaced by technology.” – Kokoi [13:44]
• “The machine has to be taught tone, it has to be taught context and culture.…the human in the loop is so important, because it needs that continuous feedback and iteration.” – Kokoi [13:17]
• “The AI can accelerate workflow… but it cannot replace judgment, it cannot replace empathy, it cannot replace context.” – Kokoi [19:15]

Key Takeaways

• Successful AI deployment in revenue cycle management demands a balanced, thoughtful pace—not just moving fast for its own sake.
• Mature governance and robust, adaptable structures are rare but crucial; most organizations lag in preparedness, leaving them at risk.
• Sustained human oversight is required at every stage—from AI training and model validation to daily workflows.
• Technology, at its best, augments human expertise; it should never fully replace human judgment, empathy, or context.
• Ongoing training and culture shift are necessary for clinicians and administrators to trust, embrace, and maximize AI tools.

Timestamps for Key Segments

• 00:33 — Kokoi’s professional background and vision
• 03:11 — The right pace for AI adoption and associated risks
• 08:18 — The governance gap: Why only 17% have mature structures
• 12:33 — The role of training, change management, and human oversight
• 17:56 — Final thoughts and lasting principles for AI in healthcare

This episode offers candid, actionable insight for leaders navigating the complex intersection of AI, governance, and people in healthcare revenue cycle management.