Agentic AI -- autonomous systems capable of reasoning, planning and acting -- is often hailed as the next frontier in healthcare. In theory, these systems could eliminate billions in administrative waste by automating complex workflows like prior authorizations, claim denials and documentation management. But in practice, like the 95% of AI pilots across industries that fail, most AI deployments in healthcare stall before they scale.
The problem isn't the models. It's the complexity of the healthcare system.
Despite rapid advances in large language models (LLMs), orchestration frameworks and retrieval-augmented generation (RAG), agentic AI has yet to make a meaningful dent in healthcare's administrative burden. The reasons are systemic: fragmented data, brittle workflows, regulatory complexity and workforce resistance. And unless these challenges are addressed holistically, agentic AI risks becoming just another promising technology that never reaches the people who need it most.
One of the most common missteps in healthcare AI is building intelligent and responsive agents to solve isolated problems -- a prior authorization agent here, a denial resolution bot there. While these tools may show promise in controlled pilots, they rarely scale due to healthcare administration's web of dependencies.
A denial, for example, may stem from a documentation error during intake, a mismatch in eligibility verification or a missing modifier in coding. Solving the denial in isolation doesn't fix the upstream issues -- and may even reinforce them.
"Agentic AI requires a systems-level mindset," says Andrew Ray, Ensemble's Chief Innovation Officer. "You can't just drop agents into silos and expect transformation. These workflows are nonlinear. The friction is cumulative. You have to design for the whole."
1. Data fragmentation
Healthcare data is abundant but often unusable. It's scattered across EHRs, billing systems and payer portals -- each with its own schema, standards and quirks. For agentic AI to function, it needs access to normalized, structured and context-rich data.
"LLMs can't reason effectively without clean inputs," says Wael Salloum, Ensemble's Chief AI Officer. "You need semantic alignment across systems -- not just access."
2. Lack of context in AI development
Many startups enter healthcare with strong technical chops but limited understanding of operational realities. They build agents that work in theory but fail in practice -- unable to navigate payer-specific rules, compliance constraints or the nuances of clinical documentation.
Without deep partnerships with healthcare operators, these tools often remain stuck in pilot mode.
3. Workforce hesitancy
Even when the tech is sound, adoption can stall. Health systems face cultural and logistical hurdles in deploying new tools. Staff are often skeptical, undertrained or overwhelmed -- and in a domain where mistakes can have serious consequences, trust is everything.
As an end-to-end revenue cycle management (RCM) partner, Ensemble manages hospitals and health systems at every stage of their revenue cycle, from initial patient intake through complete payment collection.
To move from pilot to scale, three things must work in concert: domain expertise, robust data and top AI scientists to drive differentiation. This means that at every step from strategy to measurement, technology and talent must be in lockstep.
1. Domain expertise in partnership
Our AI scientists work hand-in-hand with revenue cycle experts throughout every stage of innovation. By collaborating directly with in-house RCM specialists, clinical ontologists, and data labeling teams, Ensemble designs highly specific use cases that reflect the complexity of healthcare regulations. As payer rules evolve and revenue cycle processes grow more intricate, we embed end users early in the development process. Their real-time feedback helps us identify friction points quickly and accelerate iteration -- ensuring that our solutions are not only intelligent, but practical and scalable in real-world environments.
This collaboration among AI scientists, healthcare professionals and end users fosters a high level of contextual awareness while escalating to human judgment where appropriate. This enables the system to replicate experienced operator decision-making while maintaining the speed, scalability and consistency of AI -- always under human oversight.
2. Datasets that are robust, harmonized and ever-expanding
Ensemble has access to one of the most robust datasets in healthcare through our role as a strategic revenue cycle partner to hundreds of hospitals nationwide. With decades of experience aggregating, cleansing and harmonizing data, the team's efforts have resulted in the curation of:
All of this data is mapped to industry-leading outcomes. This foundation powers EIQ, our proprietary end-to-end intelligence engine, with structured, context-rich data pipelines that span the more than 600 steps of revenue cycle operations and power our agentic AI applications.
In the past six months alone, Ensemble has partnered with notable systems including Jupiter Medical Center, Methodist Le Bonheur and Firelands Health. Each additional client increases the robustness of the dataset that powers our agentic AI applications, allowing for holistic benefit across the hundreds of hospitals we serve. This means more opportunities for identifying patterns and greater chances to escalate a response if opportunities are noted.
3. AI scientists working at the top of their game
Ensemble's incubator model for research and development brings together highly qualified AI professionals commonly found only in big tech. Our team consists of scientists with PhD and MS degrees from leading AI/NLP institutions such as Columbia and Carnegie Mellon, with extensive experience acquired at FAANG companies and AI startups. At Ensemble, these experts work in a mission-driven environment while being able to conduct research into areas like LLMs, reinforcement learning and neuro-symbolic AI.
In addition to unparalleled computational resources and advanced infrastructure, our scientists have access to substantial amounts of private and sensitive healthcare data not typically available in other sectors. This exclusive environment enables rigorous evaluation and innovation that make transformative contributions to AI research -- all while driving meaningful impact in healthcare and improving the experience of real patients.
Unlike traditional automation, agentic AI systems are designed to operate across dynamic, multi-step workflows. They rely on multi-agent orchestration where specialized agents are coordinated through a central planner and must retrieve relevant documents or data to ground outputs. They must retain context across long-running workflows and adapting to changing rules or exceptions.
Critically, agentic AI networks should also feature a human-in-the-loop design, one where agents escalate ambiguous cases, learn from feedback and can improve over time.
Each of these capabilities are powerful -- but they also demand a level of infrastructure, integration and governance that most health systems aren't yet equipped to provide. Because Ensemble maintains a consistent focus on these three pillars -- domain expertise, robust data and top AI scientists -- our clients are able to focus their attention on clinical excellence while still staying ahead in RCM innovation and keeping up with payers.
Using this model, our agentic AI approach is already delivering tangible results across hundreds of hospitals and health systems. We have built, deployed and scaled AI to help our clients:
This strategic alignment reinforces the power of a true end-to-end revenue cycle partnership that is grounded in measurable outcomes, highlights the value of a comprehensive operational support and is executed by a team who understands the complexities of talent, technology, and transformation. Whether bridging gaps or alleviating non-clinical burdens, the right partnership is not just helpful; it is essential. Selecting the right one is a critical step in any organization's growth strategy, enabling both parties to move forward with greater speed, confidence, and impact.