From generative to agentic AI in healthcare

Healthcare is entering a new era where AI doesn’t just assist—it orchestrates.  

From predictive analytics to generative models and now agentic intelligence, each stage has redefined how providers deliver care and manage operations. The latest shift—to agentic AI—promises to transform care delivery and administrative functions by acting as an orchestrator across systems, workflows, and data sources. 

The shift from predictive to generative AI 

Early AI applications in healthcare focused on predictive analytics, with a focus on identifying high-risk patients, forecasting disease progression, and optimizing resource allocation. These tools were valuable but largely complex to build and maintain, requiring human intervention to act on insights. 

Generative AI introduced a new paradigm. Large language models (LLMs) allow organizations to work more quickly, helping providers by generating clinical documentation, summarizing patient histories, and performing prior authorization requests. These use cases reduce administrative burden and improve efficiency, but generative AI has remained task-specific—it creates content but can’t coordinate complex workflows across systems. Generative AI alone cannot fully complete most tasks. 

The rise of agentic AI 

Agentic AI represents the next evolution: autonomous, goal-driven systems that can orchestrate end-to-end workflows across multiple platforms. Unlike generative AI, which creates content, agentic AI executes and manages tasks across systems—acting as a true orchestrator rather than a passive coordinator—connecting EHRs, scheduling tools, payer portals, and patient engagement platforms to deliver seamless experiences for providers and patients. 

Agentic AI connects disparate resources and can unilaterally complete tasks, moving beyond simple responses to proactively managing entire workflows from start to finish. For healthcare providers and staff, this means less time navigating disparate systems and more time focusing on patient care. 

How agentic AI creates a frictionless provider experience 

Today’s healthcare environment is fragmented—multiple platforms, disjointed processes, and conflicting communications. Agentic AI reduces friction by completing multi-step processes that traditionally require manual intervention. Here are specific examples: 

1. Prior authorization management 

• Current state: Providers spend hours gathering documentation, submitting forms, and following up with payers. 
• Agentic AI impact: Accelerated approvals and less back and forth. An AI agent can accurately determine if prior authorization is required for a specific procedure, retrieve necessary clinical data from the EHR, populate payer-specific forms, submit requests, and monitor status. If additional documentation is needed, the agent can alert providers proactively.  

2. Care coordination across settings 

• Current State: Discharge planning and follow-up often requires care coordinators to reach out to patients multiple times via email or phone and manually schedule appointments. 
• Agentic AI Impact: Streamlined continuity of care and reduced administrative costs. An agent can automatically schedule follow-up appointments, arranges home health services, update the patient portal, and send timely patient reminders—all without provider intervention. 

3. Clinical documentation and coding

• Current state: Providers dictate notes, which are later reviewed and coded manually. 
• Agentic AI impact: Enhanced compliance and a faster revenue cycle. An AI agent can listen during the encounter, generate structured documentation, apply accurate coding, and submits claims—all in real time. The coding team’s role shifts to reviewing, making edits, and refining coding on complex claims. Humans remain essential, but agentic AI elevates their roles—shifting focus from routine tasks to higher-value decision-making

Positive impact on the patient experience

When providers are freed from administrative burdens, patients benefit from more attentive, personalized care. Agentic AI also enhances patient engagement directly: 

• Faster access to care: Automated scheduling and prior authorization reduce delays. 
• Improved communication: AI agents send timely updates, reminders, and educational materials. 
• Consistency across touchpoints: When data is shared and technology is standardized, patients experience a unified journey, regardless of care setting. 

Consider the use of AI to monitor wearable patient sensors. An agent that is continuously monitoring sensors can detect early signs of sepsis or heart failure, automatically alert clinicians, and even recommend changes to treatment plans—possibly before the patient or provider even notices symptoms.  

For access-related tasks, a multi-agent “navigator,” can streamline the entire appointment lifecycle, verifying patient identity, checking availability, rescheduling, and updating records directly in the EHR—without any manual input. From the patient’s perspective, this means fewer missed appointments, a quicker scheduling process, and a personalized care journey in which AI helps them complete tasks specific to their needs, rather than following a template. 

What’s next 

Despite these benefits, several hurdles remain. Nearly 50% of healthcare executives say they face cybersecurity, data privacy, and financial barriers to AI implementation, according to a recent Guidehouse survey. Over 40% cited concerns about data quality, standardization, availability, or governance.  

Data access and integration quality present major roadblocks—many systems struggle with fragmented EHRs and inconsistent information exchange.  This is a daily occurrence and continues to be the biggest limiter in moving smoothly into agentic AI.  Regulatory and ethical concerns also loom large, as autonomous decision-making must be transparent, auditable, and aligned with clinical governance. Deployment costs are significant—from $50K for specialized agents to over $1M for enterprise-wide systems. Clinicians also worry that excessive automation could erode patient trust or critical thinking. 

The journey from predictive analytics to generative AI and now to agentic intelligence marks a pivotal moment in healthcare innovation. Agentic AI is not just another technological upgrade—it is a paradigm shift that brings us closer to a future where healthcare is proactive, personalized, and frictionless. Organizations seeking to leverage the full potential of AI should consider these key best practices: 

• Establish governance: Align use cases with strategic priorities and ensure transparency.  
• Develop a roadmap: Draft a plan of the capabilities you’d like your organization to develop and update every 6-12 months to keep pace with rapid change.  
• Build partnerships: Collaborate with technology providers to build scalable, sustainable solutions.  
• Consider creating an AI Center of Excellence (CoE):  A dedicated, cross-functional team focused on AI success can help your organization define vision, talent, security, standards, and KPIs.  

This evolution in AI marks a pivotal moment in healthcare innovation. Agentic AI is not just another upgrade—it’s a paradigm shift that transforms fragmented processes into a unified, intelligent ecosystem. By acting as an orchestrator, it empowers providers, enhances patient experiences, and lays the foundation for scalable, value-based care.