Someone Needs to Do the Ugly Work

If you’re a healthcare leader, you’ve been hearing about AI transformation for years. You might even have invested in promising solutions before. Maybe it was robotic process automation for claims processing, “AI-powered” scheduling systems, or “AI chatbots” for patient inquiries. The results? Often disappointing.

That's because what most organizations call "AI" is really just sophisticated automation: smart enough to follow rules faster than humans, but not intelligent enough to think ahead or adapt to anything unexpected.

AI agents are different. Fundamentally different. With agentic AI adoption expected to increase more than 30-fold by 2028, understanding that difference will determine whether your organization rides this wave of healthcare innovation or gets left behind by those who figured it out first.

The Automation Ceiling

Traditional automation in healthcare has a predictable journey: initial excitement about efficiency gains. Pilot programs that show promise. Maybe even a full deployment that works well for standard cases. Then the challenges begin.

A patient presents symptoms that don't match your automated triage protocols. Your scheduling system can't handle the complexity of coordinating multiple specialists for a complex case. Every time something unexpected happens—which is basically every day in healthcare—these systems stop working and escalate to human staff.

‍We call it the automation ceiling, and every healthcare organization is bound to hit it.

Systems that should reduce workload end up creating more exceptions for staff to handle. Tech that works beautifully in demos struggles with the messy realities of actual patient coordination. Meanwhile, admin costs still consume over 40% of hospital expenses. Automation alone doesn’t cut it.

What Makes Agents Actually Intelligent

AI agents don't just follow rules; they’re designed to pursue and complete goals. When faced with an obstacle, they don't stop and wait for instructions. They figure out alternative approaches, learn from past interactions, adapt their strategies, and keep working toward the desired outcome.

Think about how your best care managers work: when a patient needs a referral but the preferred specialist isn't available, they don't just enter "specialist unavailable" in the system. They check alternative specialists, review the patient's insurance, consider travel distance, and maybe even call the original specialist's office to see if there's any flexibility. They use judgment, creativity, and persistence to solve problems.

That's what AI agents do, but at machine speed and scale. The result? Processing times drop from weeks to hours, approval rates improve, and your staff focuses on cases that actually require human expertise instead of playing phone tag with insurance companies.

The Multi-Agent System Team

Here's where it gets interesting: The most advanced healthcare systems aren't just deploying “one AI”. They're orchestrating multiple specialized agents that work together like a virtual multidisciplinary team.

The best-performing multi-agent teams can be trained to cover any specific workflows, even highly complex ones. Let’s take one specific example: a diabetic patient is discharged after a cardiac event. Our AI Care Managers don’t wait for someone to remember to call in a few days.

One agent initiates contact within hours to conduct a structured assessment using evidence-based protocols. Another agent analyzes the patient response, identifies symptoms requiring immediate attention, and schedules a cardiology follow-up, while a third agent sends medication reminders and dietary guidelines.

AI Care Managers team learns continuously: if patients with similar profiles respond better to text vs. phone outreach, or certain medication combinations require more frequent monitoring, the agents adapt accordingly.

This same multi-agent approach can be applied modularly to any other patient journey workflow, or even other areas of healthcare operations. Revenue cycle agents coordinate with clinical documentation agents to prevent billing delays before they happen. Patient flow agents work with bed management agents to optimize throughput in real-time.

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The Building Partner You Actually Need

Here's what every healthcare executive should understand: implementing agentic AI isn't like buying one more sophisticated software. If you want a partner to help you make a real, successful AI transition, focus on these three priorities:

• Healthcare experience: These systems require a deep understanding of clinical workflows, regulatory requirements, and the subtle interdependencies that make healthcare unique. That’s why domain expertise integration is proving to be crucial for healthcare applications.

• Collaborative Development: Unlike traditional software implementation, AI agents require side-by-side development. Co-building ensures agents understand your specific workflows, patient populations, and operational constraints before making autonomous decisions.

• Composable Architecture: The most effective implementations use interconnected but independent agents that can be deployed, modified, and scaled separately. This allows organizations to start with specific use cases and expand based on demonstrated results.

Lead or Follow

Healthcare orgs are already coordinating complex clinical workflows autonomously. RCM is being reimagined around intelligent agent orchestration. Patient care pathways are being optimized in real-time by systems that never sleep.

The next phase of healthcare operations won't just be automated; it will be intelligently orchestrated by systems that never stop learning, adapting, and improving.

The question is: will your organization lead this transformation, or will you be racing to catch up with organizations who have already figured it out?