The U.S. healthcare system is at a pivotal moment. Persistent workforce shortages, rising operational complexity, and mounting administrative burdens are driving providers and payers to seek transformative solutions. Enter agentic AI—the next generation of artificial intelligence that moves beyond traditional automation and generative AI, enabling autonomous execution of complex clinical and administrative workflows. This is not just a technological leap; it’s a strategic imperative for healthcare leaders aiming to future-proof their organizations and deliver better outcomes at scale.
To understand the significance of agentic AI, it’s important to distinguish it from previous waves of AI innovation. Generative AI, such as large language models, excels at creating new content—summarizing information, drafting documentation, or generating images—based on patterns learned from vast datasets. These tools have already found a home in healthcare, supporting tasks like transcribing clinical notes, summarizing patient histories, and enhancing patient communication. However, generative AI typically requires human oversight to act on its outputs.
Agentic AI, by contrast, is designed to act. These systems autonomously pursue complex goals, make decisions, and execute end-to-end processes across disparate systems. In healthcare, this means moving beyond generating a discharge summary or a prior authorization letter to actually submitting forms, updating records, coordinating with payers, and triggering follow-up actions—all without waiting for a human to push the next button. Agentic AI combines the creative and analytical power of generative models with decision engines, workflow orchestration, and deep integration into clinical and administrative systems.
Early pilots and deployments of agentic AI in the U.S. are already demonstrating measurable impact across several high-value workflows:
Traditionally, prior authorization is a labor-intensive process requiring staff to gather documentation, fill out forms, and communicate with payers. Agentic AI can automate the entire workflow: extracting relevant data from EHRs, validating medical necessity, auto-filling forms, submitting requests, and tracking responses. This not only reduces administrative burden but also accelerates patient access to care and speeds up claims resolution.
Discharge planning involves coordinating multiple stakeholders—physicians, nurses, social workers, and payers—to ensure safe transitions from hospital to home or another care setting. Agentic AI can orchestrate this process by identifying discharge criteria, scheduling follow-up appointments, arranging transportation, and ensuring that all necessary documentation is completed and communicated to the right parties.
Hospitals and clinics are burdened by repetitive administrative tasks, from scheduling and billing to claims management and compliance reporting. Agentic AI can autonomously manage these workflows, integrating with EHRs, billing systems, and payer platforms to reduce errors, speed up processing, and free up staff for higher-value work. Pilot programs have shown administrative cost reductions of up to 50%, faster patient onboarding, and improved satisfaction for both patients and providers.
Agentic AI can assist clinicians by summarizing patient histories, flagging potential risks, and coordinating care across departments. For example, an AI agent could monitor EHR data, identify patients at risk of readmission, and trigger follow-up actions automatically, supporting proactive, data-driven care.
The promise of agentic AI is immense, but so are the challenges—especially in the U.S. healthcare environment, where data privacy, interoperability, and regulatory compliance are paramount. Unlike generative AI, which can often be deployed as a standalone tool, agentic AI requires deep integration with a patchwork of legacy systems, EHRs, payer platforms, and regulatory databases. Key challenges include:
Despite their autonomy, agentic AI systems are not designed to operate in a vacuum. Human oversight remains essential—especially in high-stakes environments like healthcare. The "human-in-the-loop" model ensures that clinicians, administrators, and compliance officers can review, validate, and override AI-driven decisions when necessary. This collaborative approach balances efficiency with accountability, allowing organizations to harness the speed and scalability of AI while maintaining control over critical decisions.
As agentic AI takes on more responsibility in care delivery, regulatory scrutiny will intensify. Healthcare organizations must navigate a complex landscape of evolving guidelines, from digital health initiatives to emerging standards for AI transparency, explainability, and risk management. Ethical considerations—such as bias mitigation, patient consent, and liability in the event of errors—must be addressed proactively. Building robust governance frameworks, clear audit trails, and transparent decision-making processes will be essential to earning the trust of patients, providers, and regulators alike.
Healthcare organizations looking to unlock the value of agentic AI should take a pragmatic, phased approach:
With decades of digital transformation expertise and a proven track record in regulated, data-intensive environments, Publicis Sapient partners with U.S. healthcare organizations to design, build, and scale agentic AI solutions tailored to the sector’s unique needs. Our proprietary platforms accelerate system integration and workflow automation, while our human-centered approach ensures that technology augments—not replaces—human brilliance.
The future of healthcare is agentic. The time to prepare is now.
Ready to explore how agentic AI can transform your healthcare organization? Connect with Publicis Sapient to start your journey toward autonomous, efficient, and patient-centered care.