As someone who’s led AI transformation from the front lines of clinical operations to enterprise strategy, I’ve learned that the true promise of AI for healthcare isn’t in point solutions—it’s in re-architecting how entire systems function. The current hype around scribes and chatbots misses the deeper opportunity: agentic AI, when paired with modern data platforms, can rewire workflows, close care gaps, and bring intelligent orchestration to the health system’s most fragile pressure points.
Pressure Points: Financial Stress, Clinician Burnout, and Data Fragmentation
Health systems today are under tremendous pressure. Margins are shrinking labor costs alone consume over half of many hospitals’ operating revenue, and supply costs are volatile1. Clinician burnout has escalated in parallel, driven in large part by administrative burden—documentation, EHR inefficiencies, follow-ups, and portal messages2. Meanwhile, much of the data that could enable improvements remains siloed, outdated, or inaccessible in real time. Without clean, well-governed, interoperable data, even the best AI models fall flat.
What Agentic AI Brings to the Table
Agentic AI goes beyond reactive tools: instead of waiting for input, it observes, decides, acts, and adapts. In practice, that means systems that close loops in real time—redistributing staff during acute staffing shortages, accelerating discharges when beds are available, or auto-routing critical patient follow-ups without waiting for manual handoffs.
For example, some systems use AI to predict demand and optimize patient throughput, achieving reductions in “avoidable days” and shortening hospital stays—both helping financial performance and reducing strain on caregivers3. Agentic AI also helps reduce the hidden costs of clinician turnover by alleviating the administrative burden. It doesn’t just offer marginal improvements—it shifts the baseline.
Modern Data Platforms: The Foundation for Change
Agentic AI is only as powerful as the data infrastructure supporting it. Modern data platforms enable real-time interoperability, secure and auditable data access, and efficient workflows across disparate systems (EHRs, scheduling, billing, patient portals). These platforms provide the connective tissue that allows agentic agents to sense bottlenecks, act with confidence, and learn continuously.
They also support critical governance: bias mitigation, transparency, ethical and regulatory compliance. Without good data hygiene, even the most advanced AI can amplify inequities or fail to deliver expected value4.
System-Level Benefits: What Success Looks Like
When health systems treat AI as infrastructure and not as an add-on, the gains are far reaching:
- For patients: fewer delays, more proactive care, fewer gaps in follow-up.
- For clinicians: less burnout, more time doing what human clinicians do best: caring, diagnosing, human connection.
- For the hospital or health system: stronger financial resilience, better throughput, improved margins, readiness for value-based care.
In many cases, improvements in throughput and operational efficiency contribute to financial performance—whether reducing days lost to bottlenecks or transforming revenue cycle processes5.
In my work advising both health systems and AI companies, I’ve seen how agentic AI can shift the conversation from “which tools to buy” to “how to redesign care delivery.” The systems that will succeed here are those that treat AI not as an optional add-on, but as essential infrastructure—and the leaders who understand that are already pulling ahead.
If you lead a health system, ask two questions: Are our data platforms ready to support closed-loop, real-time decision making and how can agentic AI be embedded into operations—not bolted on? Because the real promise of AI isn’t in automating tasks. It’s in building adaptive systems that respond in real time, continuously heal themselves, and align care delivery with both human needs and financial sustainability.
