Agentic AI in the Real World
AI in Action: Real Deployments
Most organizations fail to realize value from AI—not because of model limitations, but because of breakdowns in workflow integration, trust, and operational execution.
My work focuses on building agent-driven systems that operate within real enterprise constraints, including:
- Multi-step clinical and operational decision workflows
- Human-in-the-loop escalation and validation
- Integration with EHRs (Epic), communication systems, and enterprise data platforms
- Continuous feedback loops to improve performance and adoption
Example Capabilities
- AI agents that support care coordination and reduce readmissions
- Ambient and assistive AI embedded directly into clinician workflows
- Decision-support systems that operationalize shared decision-making at scale
- Cross-system orchestration (communication, scheduling, documentation, analytics)
The goal is not AI experimentation, it is AI that works in production and delivers measurable outcomes.
Enterprise AI and Workflow Transformation
Example 1
Example 1
Example 1
Deployment of AI-enabled clinical and operational workflows across a large multi-hospital system in Texas led to over $8.5M in cost savings the following year.
Example 2
Example 1
Example 1
Integration of ambient AI, decision-support tools, and communication platforms directly into provider workflows saved 1500 physician hours per month.
Example 3
Example 1
Example 3
Implementation of autonomous inpatient bed control application resulted in extra availability of around 4 beds per night on average, at the same time reduced costs by $750K per year.
Connecting AI to Enterprise
The primary barrier to AI adoption is not model performance, it is execution.
Key challenges we address:
- Fragmented workflows that prevent AI integration
- Lack of trust and explainability in high-stakes environments
- Poor alignment between AI outputs and operational decision-making
- Failure to connect AI initiatives to financial and operational outcomes
Our approach:
- Identify high-friction workflows
- Embed AI agents directly into those workflows
- Align outputs with real decision points
- Measure impact in cost, utilization, and efficiency
This is where AI transitions from capability → enterprise value.
