TL;DR More than 40% of agentic AI projects are projected to be cancelled by 2027, with escalating costs, unclear business value, and inadequate risk controls identified as the primary causes (Gartner, June 2025). For healthcare payers, that risk is amplified by regulatory complexity, clinical workflow variability, and the compliance stakes of decisions that directly affect member care. Agentic AI promises a path to operational transformation, but most deployments fall short because AI agents cannot act effectively without a granular understanding of how a health plan actually operates.
This is a data problem. And process intelligence is how payers are solving it.
Healthcare payer operations have moved through distinct waves of automation. Robotic Process Automation in the 2010s handled high-volume, rule-based tasks like eligibility checks and data entry. Task agents followed, handling predefined workflows. Service agents took on member inquiries across phone, portal, and chat.
Now, process agents represent the next frontier. Unlike their predecessors, process agents are designed to orchestrate complex, multi-step healthcare workflows autonomously. They can manage claims adjudication, care coordination, and utilization management with a level of judgment that previous automation tools could not approach.
The critical difference between process agents and earlier automation lies in what powers them. Task and service agents run on Large Language Models (LLMs). Process agents run on Large Action Models (LAMs), which go beyond text generation to incorporate contextual understanding of clinical pathways, decision-making based on medical necessity criteria, and continuous learning from real-world outcomes.
The shift matters because healthcare workflows are not linear. Claims examiners do not follow a single path. Clinical reviewers make judgment calls based on subtle case nuances. Care coordinators adapt to patient complexity in real time. LLMs were never designed to replicate this. LAMs, trained on first-party operational data, can.
AI agents deployed into healthcare operations without a grounding in how work actually happens consistently fail at exception points, producing incorrect outputs or escalating cases that experienced staff would handle in seconds. The gap is in the operational context the model was never given.
Traditional process mining tools read event logs from individual applications and capture approximately 15-20% of actual work activity. The system-switching, workarounds, informal coordination, and manual steps that account for the majority of knowledge worker time never appear in any log. An AI agent trained on that partial picture is working with a fundamentally incomplete map of the territory.
One healthcare payer discovered through process intelligence that claims examiners were spending 40% of their time switching between systems to complete a single verification step. The documented process said nothing about this. When an AI agent was deployed using behavioral data from process intelligence, it replicated those navigation patterns, consolidated the system checks, and flagged only the cases requiring human review. Processing time dropped by half with improved accuracy.
That is the pattern that defines successful healthcare AI deployments. The AI does not replace the examiner's expertise. It replicates it at scale, in less time, with consistent compliance.
Process intelligence works by continuously observing how work is performed directly at the desktop level. Unlike traditional process mining, which reads event logs from individual applications, process intelligence captures the complete behavioral picture: how staff navigate between systems, where they pause, what decisions they make, how they handle exceptions.
This generates what Skan AI calls a Digital Twin of Operations, a high-fidelity model of how a health plan actually functions, built from direct observation rather than documentation or interviews. It reflects the real workflows of claims examiners, clinical reviewers, care coordinators, and member services staff as they perform their daily work across every application they touch, including legacy systems and manual workarounds that standard tools never see.
The data the Digital Twin captures includes:
This data becomes the training foundation for LAMs that are specific to the organization. Not generic healthcare workflows. The plan's actual medical policies, benefit designs, and operational procedures.
The chasm between what LLMs can do and what LAMs can do is crossed only with high-fidelity, first-party enterprise data. Off-the-shelf models trained on public healthcare data cannot replicate how clinical reviewers assess medical necessity at a specific health plan. They cannot learn plan-specific adjudication rules or the informal workarounds member services teams use to resolve complex inquiries.
Healthcare payers that build proprietary first-party datasets through direct observation of operations develop AI capabilities that are structurally difficult for competitors to replicate. Enterprises currently building an observational data layer are establishing the context advantage that agentic AI will require at scale. Organizations that wait will be building that foundation later, from a further behind position, with a narrower window to close the gap.
Three barriers consistently prevent payers from capturing this advantage:
Data silos and legacy systems. Legacy claims platforms, separate utilization management tools, and disconnected care management systems create fragmented visibility. Process intelligence identifies where these silos create operational waste and where integration would yield the highest return.
Skills gaps and talent constraints. Deploying agentic AI in healthcare requires expertise in data engineering, AI development, medical management, claims adjudication, and regulatory compliance. Process intelligence helps payers understand exactly what roles and capabilities they need to build or acquire.
HIPAA compliance and responsible AI. Collecting comprehensive first-party data at the desktop level operates within a privacy-by-design framework. Skan AI anonymizes and masks individual user details and PII during capture, ensuring compliance with HIPAA, state privacy regulations, and CMS requirements while providing governance data to prevent algorithmic bias in coverage decisions and maintain human oversight for complex clinical determinations.
McKinsey estimates that healthcare payers could achieve between $150 million and $300 million in administrative cost savings for every $10 billion in revenue by fully integrating AI and automation into operations (McKinsey, 2025). Production deployments in the sector show what that trajectory looks like in practice: one Fortune 50 healthcare payer identified $13M+ in annual savings across more than 3 million calls by using process intelligence to standardize operations and optimize workforce capacity.
Process intelligence identifies AI opportunities by measuring where administrative burden is heaviest, where errors cluster, and where experienced staff spend time on work that should not require human judgment. The highest-ROI use cases in healthcare payer operations follow a consistent pattern across organizations.
Claims processing. Claims examiners at most payers navigate multiple systems to complete a single adjudication. Process intelligence reveals the exact navigation patterns, identifies the redundant steps, and provides the behavioral data to train AI agents on the most efficient paths. One payer halved processing time while improving accuracy. Skan AI customers typically discover that 40% of claims work happens in applications their existing tools cannot see.
Prior authorization. Prior authorization workflows are dense with informal coordination and compensating workarounds. Clinical reviewers gather data from multiple sources before making a single decision. Process intelligence maps which data points are consulted for each decision type, enabling AI agents to pre-populate that information and reduce review time from hours to minutes while maintaining clinical appropriateness standards.
Care coordination. Care coordinators routinely perform administrative tasks, including scheduling, reminders, and care plan updates, that consume time better spent on complex case management. Process intelligence identifies which coordination tasks follow predictable patterns and can be safely automated, freeing care managers to focus on high-risk members with complex chronic conditions.
Member services. Member inquiry handling varies significantly across representatives based on experience and escalation habits. Process intelligence captures the patterns of top performers, identifying how they resolve common inquiries efficiently and when they escalate. AI agents trained on this data handle routine inquiries at scale while routing complex cases to the appropriate human expertise.
For a deeper look at use cases across healthcare, banking, and insurance, see 9 key use cases for agentic AI in healthcare.
|
Stage |
Objective |
Typical Systems / Applications |
|
Set Goals |
Adjudicate claims accurately: optimize for speed, accuracy, compliance, and appropriate payment |
Claims systems, medical policy databases, compliance dashboards |
|
Map Context |
Understand constraints: benefit design, medical policies, regulatory requirements, contract terms |
Benefit administration systems, policy management tools, regulatory databases |
|
Sensing Inputs |
Collect data: claim details, medical records, provider contracts, member eligibility, clinical documentation |
Claims management, document imaging, provider portals, eligibility systems |
|
Decision & Reasoning |
Validate coding, assess medical necessity, apply benefit rules, identify coordination of benefits |
Claims rules engines, medical necessity criteria, AI platforms, fraud detection tools |
|
Take Actions |
Execute: approve, deny, pend for review, adjust payment, request additional information |
Claims processing systems, payment systems, workflow tools, correspondence systems |
|
Validate Outcomes |
Measure: accuracy rates, processing time, appeal rates, compliance metrics, member satisfaction |
Quality monitoring dashboards, audit tools, reporting systems |
Successful agentic AI adoption in healthcare follows a phased progression. Organizations that skip phases to deploy autonomous agents before establishing process intelligence foundations consistently face costly failures. Gartner's projection that over 40% of agentic AI projects will be cancelled by 2027 reflects exactly this pattern: agents launched without operational context fail at exception points, erode confidence in the initiative, and are subsequently deprioritized or cancelled.
Phase 1: Process Discovery. Deploy process intelligence to observe and map how work actually happens across claims, prior authorization, member services, and care coordination. Establish baseline metrics for workforce utilization, application usage, system-switching frequency, and average time to complete end-to-end transactions.
Phase 2: Process Optimization. Use the baseline to identify inefficiencies, eliminate redundant steps, and standardize workflows based on best-practice patterns observed in the data. One healthcare payer reduced process variability by 40% and identified $10M in cost savings through this phase alone.
Phase 3: Intelligent Automation. Deploy task and service agents against optimized workflows. AI trained on optimized processes delivers significantly better outcomes than AI trained on processes that include waste and workarounds.
Phase 4: Agentic Automation. Deploy process agents powered by LAMs trained on the organization's first-party operational data. At this stage, AI agents manage complete claims adjudication workflows, prior authorization decisions, and care coordination activities autonomously within defined compliance parameters, with human oversight maintained for complex and exception cases.
The strategic roadmap requires five parallel commitments running through all phases: strategic business alignment, end-to-end process optimization, change management and upskilling, ethical and responsible AI governance, and a regulatory compliance framework built around HIPAA, CMS requirements, and medical necessity standards.
Skan AI's process intelligence platform is purpose-built for this progression. By monitoring healthcare operations directly at the desktop level across claims examiners, clinical reviewers, care coordinators, and member services staff, Skan AI generates the high-fidelity behavioral data that LAMs require to operate effectively in complex payer environments. This is what Skan AI calls Agentic Process Intelligence: the direct observation of how work actually happens, converted into the operational ground truth that AI agents need to act reliably and compliantly.
The platform creates a Digital Twin of the health plan's operations, covering process flow, screen interactions, task sequences, application navigation, communications, errors, and timing data. This becomes the training dataset for Skan's Large Action Model (S.L.A.M.), which learns the plan's specific medical policies, benefit designs, and operational procedures rather than generic healthcare workflow templates.
The result is AI that makes claims adjudication decisions with the nuanced judgment of an experienced examiner, navigates prior authorization workflows with the contextual awareness of an expert clinical reviewer, and coordinates care activities with the efficiency of a skilled care manager, all within HIPAA compliance and regulatory requirements.
Enterprises that have established an observational data foundation before agentic AI deployment report compounding ROI across agent iterations. Each optimization cycle improves the training data. Each deployed AI agent generates outcomes data that further refines the model. Organizations starting later are structurally slower to close that gap.
Healthcare payers that have deployed process intelligence as the foundation of their agentic AI strategy are identifying $10M to $28M in annual savings and 30-40% cycle time reductions across claims, member services, and prior authorization operations.
See how Skan AI build that foundation for health plans like yours.
Healthcare payers deploying agentic AI on top of incomplete process data consistently encounter the same failure modes: exception-handling gaps, compliance blind spots, and agents that perform well in demos but stall in production. The organizations closing the gap are those that built the observational foundation first.
Skan AI deploys within weeks and typically surfaces the first high-impact process insight before the baseline observation period ends. Whether your priority is claims cycle time, prior authorization throughput, member services efficiency, or readiness for autonomous process agents, the starting point is the same: see how work actually happens.
Request a discovery call with the Skan AI healthcare team to see how leading health plans are using process intelligence to build the data foundation their agentic AI investments require.