Executive Summary

As we enter 2026, Hospital and Health System leadership faces a strategic mandate to move beyond the Electronic Health Record (EHR) as a mere "system of record" toward a System of Intelligence. While 88% of health systems are now using AI, only 18% have a fully formed enterprise strategy, leading to a proliferation of "Shadow AI" that introduces risk without scaled value. To bridge this gap, organizations must adopt a framework for Agentic AI systems capable of not just assisting humans, but independently planning and executing multi-step workflows across clinical and administrative domains.

This executive brief outlines the six core capabilities required for scalable New Age EHR adoption, anchored in the Copilot Studio 2026 vision. We transition from individual AI assistance (e.g., meeting summaries) to organizational workflow optimization where agents own repeatable processes from end-to-end. By institutionalizing the FURM (Fair, Useful, Reliable, Measurable) framework and leveraging the Model Context Protocol (MCP) for cross-system interoperability, health systems can transform business intent into operational reality. Evidence shows that organizations moving from experimental pilots to scaled execution achieve a median ROI of 3.2:1, reclaiming thousands of clinician hours and significantly reducing medication distribution errors. The following sections provide a roadmap for leadership to establish governance that enables innovation while ensuring PII/PHI compliance and sustained business performance.

1. Introduction

The Evolutionary Leap: From Individual Assistance to Agentic Optimization

The healthcare landscape in 2026 is defined by a shift from AI as a "tool" to AI as a "coworker" or "sidekick". In the previous era of digital health, AI was manually triggered and siloed, primarily focused on helping individuals do work faster such as drafting an email or summarizing a patient encounter. Today, the "Copilot Studio 2026" vision demands a shift toward organizational workflow optimization, where AI agents autonomously manage the "last-mile" work that has historically strained healthcare resources. This evolution is critical as health systems face rising labor costs and a "value-creation imperative" where revenue growth is now the primary driver of shareholder value.

Leading organizations are redesigning their operating models to move beyond "pilot purgatory". They are establishing Agent Factories—centralized groups that coordinate AI efforts across departments to ensure that every deployment is grounded in business intent and clinical relevance. By embedding AI into the organizational fabric rather than treating it as an add-on, hospitals can free up 25-40% of enterprise capacity, allowing human staff to focus on high-judgment tasks and direct patient connection.

2. Capability 1: Turning Business Intent into Agentic Solutions

Historically, building an automated solution required a lengthy translation of business needs into technical specifications, a process that often resulted in "translation gaps" and stalled adoption. In 2026, the interface for agent creation has become natural language. Subject matter experts including nursing leads, operations managers, and finance officers can now describe a desired outcome (e.g., "Automate the prior authorization process for all orthopedic surgeries") and an orchestrator agent can plan the execution. This capability broadens who can build, allowing innovation to bubble up from the front lines while IT maintains oversight in a governed environment.

By utilizing low-code platforms like Copilot Studio, organizations can turn curiosity into capability. These agents interpret intent, context, and goals through underlying medical foundation models rather than custom-built code. This "democratization of automation" ensures that the most acute operational pain points those felt by frontline teams are addressed with "first-time-right" solutions. For leadership, the result is faster creation times and a more agile organization that can pivot its digital health strategy in real-time as market conditions or regulatory requirements change.

3. Capability 2: Owning the End-to-End Workflow

For a New Age EHR to be effective, it must transition from a passive database to an active participant in patient care. Capability 2 involves deploying agents that can own repeatable processes from end-to-end, automatically advancing work without waiting for manual handoffs. For example, a Workflows Agent can trigger when a patient is admitted, autonomously gather required documentation from multiple sources, validate it against payer requirements, and generate a supporting clinical rationale for insurance. This eliminates the "queues" where clinical care often stalls, ensuring that patients receive therapy faster.

In the clinical realm, Ambient AI has moved beyond note-taking to become a multi-persona orchestrator. These systems listen to patient-clinician conversations and convert them into structured clinical notes in real-time across 14+ languages, while simultaneously identifying care gaps and suggesting "next-best actions". At Kaiser Permanente, this technology reclaimed over 15,000 hours of physician time in a single year. By automating these administrative burdens, health systems can reclaim the "joy of medicine," reducing clinician burnout by an average of 21% within three months.

4. Capability 3: Multi-Agent Systems and Organizational Coordination

Meaningful business outcomes rarely happen within a single system or team. Capability 3 focuses on the coordination of multi-agent systems that specialization, delegate, and collaborate toward shared goals. Instead of one "master agent," organizations now compose "teams" of agents that mirror human departments: one agent monitors vital signs, another gathers laboratory data, and a third coordinates the clinical team's response based on internal protocols. This collaborative intelligence allows for "structural reasoning" that moves beyond simple pattern recognition into complex problem solving.

This coordination is powered by the Agent2Agent (A2A) protocol, allowing disparate systems to communicate behind the scenes to keep complex work cohesive. In surgical settings, for instance, multimodal AI can coordinate instrument recognition, real-time vital monitoring, and post-operative risk prediction, notifying the relevant stakeholders only when human judgment is required. This "Human-in-the-Loop" architecture ensures that while AI handles the high-volume orchestration, professional accountability and clinical judgment remain the final authority.

5. Capability 4: Governance Guardrails (The FURM Framework)

As AI moves from experimental pilots to core infrastructure, "Shadow AI" the unauthorized use of tools by clinicians must be replaced by formal governance frameworks. Currently, 80% of health systems lack the mature programs necessary to manage these investments. Successful New Age EHR adoption requires an "AI Formulary" of approved, validated tools and a multidisciplinary steering committee that includes C-suite sponsors, data scientists, and equity officers. This structure ensures that innovation does not outpace control, protecting patient safety and community trust.

The gold standard for this transition is the FURM framework, which mandates that every AI model be:

• Fair: Mitigating demographic biases in training data.
  
• Useful: Fitting into clinical workflows rather than adding steps.
  
• Reliable: Demonstrating validated performance over time through local validation.
  
• Measurable: Having clear KPIs, such as "minutes saved" or "visits avoided".
  

By institutionalizing these principles, leadership creates a "compliance-first" environment where AI acts as a reliable partner in care delivery.

6. Capability 5: Interoperability and the PII/PHI-Compliant Data Plane

A fundamental barrier to AI adoption is that 97% of biological and health data is currently fragmented and inaccessible. Capability 5 involves building an Intelligent Data Management Cloud (IDMC) that serves as the canonical data plane for the organization, utilizing the FHIR (Fast Healthcare Interoperability Resources) standard for real-time movement of clinical and administrative data. By establishing a "Patient 360" view, resolving identity issues like duplicate records, and normalizing data across disparate systems, health systems create a "golden layer" of data that AI can trust.

Interoperability must also extend to Confidential AI and data privacy. By 2026, over 70% of PHI is expected to live outside traditional EMR systems, scattered across collaboration platforms like Microsoft Teams and analytics tools like Power BI. Utilizing tools like Microsoft Purview allows for the automatic discovery and classification of PHI wherever it lives, applying protection controls consistently across platforms. This horizontal governance ensures that agentic workflows remain compliant with HIPAA and GDPR standards, turning data privacy from a technical constraint into a strategic advantage.

7. Capability 6: Scaling for Sustained Value and ROI

The final capability for scalable EHR adoption is a shift from possibility to performance. Organizations must move from counting how many agents they build to how many people rely on them to get work done. This requires lifecycle management continuously monitoring model performance, auditing for bias, and identifying "subgroup drift" in real-time. Organizations that establish a Center of Excellence (COE) to triage needs and maintain a unified AI strategy can scale their deployments without fracturing across departments.

Financially, enterprise AI projects are proving highly sustainable, with top performers achieving a median ROI of 3.2:1 within an 18-month payback period. To sustain this, leadership must treat AI as a workforce staffing strategy, reclaiming clinical time to offset nursing vacancies and physician burnout. By co-publishing ROI, quality, and equity metrics to a board-level dashboard each quarter, health systems can prove the progress of their digital transformation and secure the investment needed for the next generation of patient-centered care.

References

Angus, Derek C., et al. "AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence." JAMA 334, no. 18 (October 13, 2025): 1650–1664.
Callahan, A., D. McElfresh, J. M. Banda, et al. "Standing on FURM Ground: A Framework for Evaluating Fair, Useful, and Reliable AI Models in Health Care Systems." NEJM Catalyst 5, no. 10 (2024).
Chopra, Nitasha. "6 core capabilities to scale agent adoption in 2026." Microsoft Copilot Blog, January 26, 2026.
Devarakonda, Sriram. "Looking back, leaping forward: Healthcare AI predictions for 2026." Cardamom Health Insights, January 20, 2026.
Elechi, Ubalaeze, et al. "Artificial Intelligence in Healthcare: A Narrative Review of Recent Clinical Applications, Implementation Strategies, and Challenges." Journal of Healthcare Leadership 17 (December 30, 2025): 863–876.
Exelegent. "Data Governance in Healthcare: How SMEs Protect PHI in 2026." Exelegent Security Blogs, 2026.
Informatica. "Informatica Announces Fall 2025 Release with Latest Innovations to Intelligent Data Management Cloud." Informatica Newsroom, October 29, 2025.
John Snow Labs. "Generative AI in Healthcare: Use Cases, Benefits, and Challenges." John Snow Labs Knowledge Hub, November 28, 2025.
LumenData. "Informatica Implementation for Healthcare Data Management: A 2026 Guide for Leaders on IDMC, AI-Native Analytics, and Interoperability." LumenData Blogs, 2026.
McKinsey & Company. "Reimagining Life Science Enterprises with Agentic AI." McKinsey Life Sciences Practice, November 2025.
McKinsey Global Institute. "Agents, Robots, and Us: Skill Partnerships in the Age of AI." November 25, 2025.
MIT Sloan School of Management. "Transforming Healthcare with AI." MIT Jameel Clinic, 2025.
Patel, Neha, Shubham Singhal, and Ankit Jain. "What to expect in US healthcare in 2026 and beyond." McKinsey & Company, January 12, 2026.
Schmidt, Karsten. "Strategically Rethinking HCP Engagement in 2026 & Beyond." LinkedIn, January 5, 2026.
Stanford Institute for Human-Centered AI (HAI). "Artificial Intelligence Index Report 2025." Stanford University, April 2025.