AI
The shift toward workflow-native and agentic AI systems Understanding where early ROI is emerging helps clarify what kind of AI architecture is required to deliver it at scale. The dominant model of AI deployment in healthcare to date has been what might be described as a alongside-workflow. These are tools that receive input from existing systems, perform defined tasks, and return outputs for human review. This model has value, but it has reached its limits as a vehicle for enterprise-wide transformation. The limitation is not just technical, but structural. Systems
Concept image of AI-driven document management.
Sathiyan Kutty
Sathiyan Kutty is the chief AI officer at Emids, where he leads AI-driven innovation across healthcare payer, life sciences, and health tech markets. With over two decades of experience spanning analytics, AI, and technology-led growth, Seth has built a reputation as a sharp and pragmatic leader in the field. Beyond his corporate career, Seth is a repeat entrepreneur, having founded and scaled a data and AI services company that went on to achieve a profitable exit. Seth holds a Bachelor of Science in Electrical Engineering and Computer Science and a Master of Science in Industrial and Operations Engineering with a specialisation in Operations Research from the University of Michigan. This blend of hands-on experience and academic grounding shapes his approach to building scalable, outcome-oriented AI platforms that deliver lasting value.
applications, which carry the heaviest regulatory and integration burden, but from operational and back-office workflows. Patient intake, documentation classification, prior authorisation, and revenue cycle management are all areas where AI-driven automation is beginning to show consistent, measurable results. For payer organisations, this value is most visible in prior authorisation and claims operations, where AI is beginning to support decision-making rather than just task execution. Systems that can interpret policy rules, validate coverage, and identify discrepancies earlier in the process are helping reduce administrative effort while improving turnaround times. The impact extends beyond efficiency, influencing provider relationships and member experience in meaningful ways.
High-volume workflows On the provider side, similar gains are emerging across intake and revenue cycle workflows. AI is being used to verify eligibility in real time, structure unorganised documentation, and identify gaps before submission, shifting error detection upstream in the revenue cycle. This reduces downstream rework and improves the overall efficiency of financial operations. In many provider environments, even small improvements in first-pass acceptance rates can translate into meaningful financial impact, given the volume and value of claims processed. There is a structural reason for this. These workflows
are high-volume, largely rule-governed, and tied to clear performance metrics such as processing time, accuracy, and reimbursement outcomes. They are also sufficiently removed from direct clinical decision-making, allowing organisations to introduce improvements without the same level of regulatory friction. Revenue cycle management has become a proving
ground for healthcare AI. The combination of complex rules, transaction volume, and financial exposure creates an environment where even incremental improvements translate into measurable returns. For payers, this extends into payment integrity, where
AI systems are being used to detect anomalous billing patterns, identify duplicate claims, and prioritise audit efforts with greater precision. For providers, the impact is most visible in denial management and coding accuracy, where AI-enabled systems are helping improve first-pass acceptance rates and strengthen revenue predictability.
90 Health Estate Journal September 2026
that operate alongside workflows cannot coordinate across steps, manage dependencies, or adapt dynamically to exceptions. As the number of AI-enabled tasks increases, the operational burden of stitching these outputs together often shifts back to human teams, limiting the overall impact of automation. What is beginning to replace this model is a workflow- native approach, in which AI systems are designed to operate within operational processes rather than alongside them. These systems manage multi-step execution, handle exceptions, and adapt based on context, functioning as part of the operational infrastructure itself.
In payer environments, this enables a more cohesive
approach to prior authorisation, where AI systems can manage the lifecycle of a request from intake and validation to exception handling and compliance tracking. In provider settings, a similar shift is visible in access
and scheduling workflows, where systems coordinate across referral requirements, clinical appropriateness, and network constraints. These processes, which previously depended on manual coordination, are beginning to operate with greater consistency and speed. The emergence of agentic AI represents a broader
shift in how systems interact with operations. Instead of responding to individual prompts, they are capable of navigating complex processes with a degree of autonomy. This creates new opportunities for end-to-end optimisation, while also raising important questions around governance, accountability, and oversight.
The rise of forward-deployed context engineering Healthcare workflows are shaped by a complex, often undocumented mix of payer rules, regulatory requirements, legacy systems, and institutional practices. These are not variables that can be standardised easily or addressed through generic configurations. They require careful interpretation and translation into system behaviour. This has led to the emergence of forward-deployed
context engineering, teams embedded within healthcare organisations to translate operational complexity into production-ready AI systems. In payer environments, this is particularly important
where policy rules vary across plans, products, and geographies. Without this embedded understanding, systems risk producing outputs that are technically correct but operationally misaligned. In provider settings, workflows such as clinical documentation improvement and coding are shaped by specialty-specific practices and local variations that are rarely captured in structured form. Embedding this context ensures that systems reflect how work is performed, rather than how it is assumed to function. Organisations investing in this capability are finding
that it significantly improves the transition from pilot to scale by addressing the contextual gaps that often derail implementation.
AdobeStock / Suriyo
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