Key takeaways
- Generative AI creates and summarizes content, while agentic AI coordinates tasks and workflows
- Both technologies can work together to improve healthcare operational efficiency
- Healthcare use cases include clinical documentation, patient scheduling, and revenue cycle management
- ROI depends on measurable time savings, reduced costs, and improved workflow outcomes
- Human oversight, data privacy, and clear governance are essential for responsible AI adoption
What is the difference between agentic AI and generative AI?
Generative AI works by generating content through pattern recognition using available data. This kind of AI can help healthcare professionals in summarizing consultations, drafting discharge instructions, organizing clinical notes, and even explaining complicated policies. However, the generated output requires suitable review, especially when it comes to patient care.
Understanding how different AI systems work is essential for healthcare leaders, exploring the distinctions between agentic AI and traditional AI agents to make informed implementation decisions.
On the other hand, Agentic AI revolves around task completion. In other words, it is capable of developing action sequences, searching for authorized records, recognizing what is missing, updating processes, and routing the case to the right team.
Capability | Generative AI | Agentic AI |
Main function | Content creation or modification | Coordination of activities toward an objective |
Typical output | Summary, draft, response | Completed activities, progress, anomaly |
Human’s role | Review of output | Setting limitations, approving activities, escalation |
Value for the business | Efficient knowledge work | Smoothing of workflow and transfer process |
Risk | Incorrect or made-up output | Mistakes and unwanted or harmful activities |
Importantly, agentic AI and generative AI are not mutually exclusive. An agentic system can use generative AI to interpret information and prepare content, while coordinating actions through connected tools and workflows.
How does agentic AI work in healthcare?
Consider a patient waiting for insurance approval for a procedure. The organization will need to gather the records, check coverage, review the documentation requirements, submit the request, and follow up with the insurance company. The absence of one record could affect the approval process and create more work.
Here, an agentic workflow can help by pulling permissible records, flagging documents that are missing, drafting the email, and sending it to an employee for approval. An approved system can then proceed with making a submission and tracking its status.
It would be helpful here to distinguish between agentic AI vs. AI agents. Agentic AI refers to the process of attaining goals through coordination of actions, while AI agents are systems responsible for taking those actions. Human oversight remains essential for incomplete records, unusual insurance requirements, and decisions requiring clinical judgment.
Where does generative AI create value today?
Healthcare organizations can start with activities that are time-consuming and allow a human to review the results before use. The following use cases include:
- Clinical documentation: Compose visit summaries and sort out notes for review by clinicians.
- Patient communications: Generate appointment reminders, discharge instructions, and patient-oriented explanations.
- Knowledge management: Produce internal policy summaries and assist employees with finding information.
- Call centers: Summarize calls and generate responses to frequently asked questions.
- Revenue cycle management: Gather document information and compose claim appeals.
Organizations should evaluate accuracy, adoption, and time savings before expanding these use cases.
Where can agentic AI in healthcare improve the bottom line?
Agentic AI can add value to workflows that require coordination across systems, follow-ups, and exception handling.
- Patient access and scheduling: Agents could coordinate eligibility, follow up on referrals that have not been completed, and identify cases that need human intervention. Better process coordination might minimize delay and enhance the patient experience.
- Revenue cycle management: Agents could coordinate denial resolution by collecting supporting documents, organizing cases with upcoming appeal deadlines, and coordinating specialist involvement. Such efforts might reduce administrative processing delays and prevent unnecessary revenue leakage.
- Care coordination: After a patient has been discharged from the hospital, agents could track any outstanding appointments, instructions, and communications.
- IT operations: Agents could prioritize and categorize support tickets, collect information needed for diagnosis, and execute permitted actions.
Adoption is on the rise. According to an update from McKinsey’s April 2026 survey, 50% of the healthcare organizations in the US have adopted generative AI technology. In addition, 19% have adopted agentic AI, and 51% are exploring agentic AI proof of concepts. Nevertheless, adoption doesn’t automatically translate to profitability.
Why healthcare needs both technologies
Consider a denied insurance claim to see how the two technologies work together. Generative AI can produce a description of the rejection and the corresponding appeal letter. The agent can collect all the necessary documents, prepare a case, get it approved, and control deadlines.
Each element has its own place and function, which makes the entire process a lot easier to control and to trace. It also removes the need to use complex artificial intelligence systems for doing things that regular software could do easily.
How should executives measure return on investment?
A successful pilot project has to show improvement beyond just fast responses or great demonstration skills. Baseline measures should be set up beforehand and then compared to the results achieved later on by the executives.
Good examples of such measures are:
- Financial performance: cost per case, overtime, avoidable costs, and claim recovery.
- Operational performance: processing time, backlog, manual intervention, and first-pass resolution.
- Experience and quality: error rate, rework, escalations, patients' feedback, and employees' feedback.
The executives have to separate time saved from money saved. ROI becomes measurable when organizations can process more cases with existing resources, reduce overtime, avoid unnecessary costs, or demonstrate improvements in service quality.
What risks must healthcare leaders govern?
There needs to be control over any AI system that processes patient data or takes actions. The organization will have to determine access to data, actions allowed, approvals required, and escalations.
A 2025 paper published in npj Digital Medicine highlights the importance of governance frameworks for managing AI-related risks in healthcare, including privacy, bias, safety, and accountability. Healthcare organizations should establish appropriate access controls, audit trails, security safeguards, and ongoing monitoring for AI systems that process patient information.
Clinical decisions involving diagnosis and treatment must remain subject to appropriate oversight by qualified healthcare professionals. Approval points, audit logs, transaction thresholds, and a way to pause the process can prevent mistakes from escalating. There should be an owner of every deployment who will investigate mistakes and assess its performance.
How can healthcare organizations start responsibly?
Start with the basics: something tangible and measurable, not an attempt at automating the entire healthcare department. Identify the existing workflow, learn what dependencies exist between the systems, and identify when human approval is needed.
Prove this through a pilot project using a draft or recommendation. Evaluate the safety issues, employee involvement, processing time, financial gains, and then move forward with expanding the scope of activities of the system. Clinicians, finance, compliance, security, and technology departments must be involved in making that decision. Use the pilot results to determine whether the workflow is ready for wider deployment, with expansion guided by demonstrated safety, operational value, and financial performance.
The final takeaway
The real value of AI comes from effective implementation, with clear accountability, appropriate safeguards, and measurable outcomes helping healthcare organizations maximize their investments. Contact TO THE NEW to explore how we can help your organization implement AI solutions that streamline healthcare workflows and drive measurable business outcomes.
Read further
1. McKinsey (50% of US healthcare organizations have adopted generative AI)
2. npj Digital Medicine (Key AI governance risks in healthcare: privacy, bias, safety, and accountability)