An autonomous future of intelligent healthcare | Viewpoint

In healthcare, every operational decision carries real-world consequences for patients, clinicians, and entire systems of care. It is this reality that makes the industry uniquely sensitive to the next phase of Artificial Intelligence (AI) adoption, as this technology begins to influence not just analysis and recommendations, but actions and outcomes.

AI has already established itself as a major transformative force in the interconnected world of healthcare. The next phase of adoption promises to be more purposeful, with greater precision in medicine, speedier and deeper research, and more compassionate patient care.

It is not surprising, therefore, that almost all stakeholders – from care providers, clinicians and medtechs to pharma companies, public health agencies and insurers – are set to fuel an AI market projected to reach $491 billion by 2032, with generative AI alone expected to account for $22 billion by 2027.

This momentum comes from AI’s ability to retool every aspect of the healthcare system. With effective deployment, care providers will see improved clinical outcomes in diagnostics, patient monitoring and care. Clinicians will gain time to focus on deeper patient relationships. Pharmaceutical firms will accelerate drug discovery and development. Payers’ efforts will be enhanced by predictive models targeting preventive health management. Medtech will unlock efficiencies across supply chain and product design. In the public health domain, AI will emerge as a strong ally in disease surveillance and effective resource allocation.

The pace and scale of change in this ecosystem makes one fact clear: the era of isolated AI experimentation is over. Meaningful AI integration is now constrained not just by medical data complexity, but by the need for seamless interoperability with clinical workflows, evolving regulatory mandates, and deep integration with medical devices. Equally critical is the need for healthcare applications with context-aware reasoning, to ensure patient safety. The industry has reached a tipping point where these capabilities are no longer optional – they are needed now.

Agentic AI provides a practical path to make all this possible – simultaneously, reliably, autonomously, and at scale.

Autonomy with a purpose

Agentic AI’s relevance to healthcare is rooted in purpose. The industry has already applied traditional AI across areas such as medical image analysis for timely intervention and the use of support and advisory chatbots. 

It is now time to shift from reactive and task-based automation to networked systems that can reason, plan, decide, and adapt across complex and multi-step processes. When executed autonomously and within the right guardrails, this capability will fill an essential requirement for a life-critical industry that is strapped for resources and skilled professionals.

Agentic AI elevates healthcare from proficiency in analytics and automation to intelligent agency. A specialist multi-agent ecosystem can simultaneously survey the landscape, assess risks and requirements, make informed decisions across the healthcare value cycle, act on them and dynamically course-correct as conditions change, all on its own. Crucially, these agents learn and evolve through ongoing interaction.

The result is a huge positive impact across patient care, clinical excellence, administrative efficiency, and financial performance. Agentic AI not only makes decisions and creates workflows to eliminate bottlenecks caused by bandwidth constraints, it amplifies human capabilities to design future-forward strategies.

Creating reimagined health economics with AI

Every task across the healthcare revenue lifecycle carries both a financial cost and a clinical consequence. Traditional cost optimization levers in healthcare have largely reached their limit. Incremental automation can no longer keep pace with rising utilization, tightening reimbursement, and growing administrative complexity. What is emerging instead is a structural shift in health economics driven by AI-powered autonomy.

Nowhere is this shift more visible or consequential than in Utilization Management (UM). Like Revenue Cycle Management (RCM), UM sits at the intersection of clinical workflows, financial performance, and operational throughput. It directly influences cash flow, cost-to-collect, clinician productivity, and the overall stability of provider ecosystems. Historically, however, UM has been constrained by manual reviews, rigid rules engines, and fragmented handoffs that slow decisioning and introduce avoidable friction across the care continuum.

Agentic AI changes this model by applying the same execution-oriented intelligence now transforming RCM to UM workflows.

In AI-enabled UM environments, intelligent agents ingest unstructured clinical documentation directly from electronic health records, synthesize it against current payer medical policies, and autonomously generate, submit, and track authorization requests across systems. Rather than waiting for denials to surface downstream, these agents continuously monitor authorization pathways, predict where delays or adverse outcomes are likely, and trigger corrective actions in real time. This includes initiating peer-to-peer reviews, preparing escalation documentation, and activating alternative clinical pathways before care or revenue is disrupted.

The economic impact mirrors what leading organizations are already seeing in AI-driven RCM. Early adopters report 20 to 35 percent efficiency gains, 27 percent fewer denials, 36 percent higher workforce productivity, and 40 to 60 percent reductions in administrative burden. In UM specifically, these gains translate into faster decision cycles, improved payment accuracy, and fewer downstream appeals.

For provider ecosystems, AI-powered UM is emerging as a source of market differentiation, not just operational efficiency.

Over time, these systems create a continuous learning loop across the revenue and utilization lifecycle. The result is a unified operating model where standard workflows are handled autonomously, while humans focus on exceptions, governance, clinical judgment, and patient empathy. This balance improves throughput, strengthens financial resilience, and enhances clinician and patient experience.

The emergence of autonomous health organizations may come earlier than we think. Humans and digital agents will be soon working as colleagues – the human lending oversight to their AI colleagues who will, autonomously, analyze data in real time, make decisions and act on them, create personalized patient experiences, and achieve dynamic compliance. For Agentic AI to be the pillar of such a future, today’s healthcare leaders must truly lead the agentic AI charge, right here and now.

Dennis Thomas is the business unit head, healthcare & life sciences, WNS, part of Capgemini