The 3-Phase Roadmap Healthcare Payers Use to Reduce Administrative Costs
Healthcare payers can realistically unlock $150M to $300M in administrative cost savings for every $10B in revenue by integrating AI and automation into their operations, according to McKinsey (2025). Most organizations are nowhere near that threshold, and the barrier is not a lack of technology. It is a lack of visibility into how work actually happens before technology decisions are made.
The organizations achieving durable results follow the same foundational sequence: see how work is performed, optimize what needs to change, then digitize what has been stabilized. This 3-phase process intelligence roadmap outlines how each phase works, what it delivers, and why the sequence matters. The full deployment analysis is covered in our Claims, Costs, and Clarity ebook.
Why is healthcare administrative waste so difficult to solve?
The core problem is a visibility gap, not a technology gap. Healthcare payer operations span dozens of applications, multiple teams, and highly variable workflows across claims, prior authorizations, member services, and utilization management. With medical loss ratios (MLRs) climbing and administrative costs under sustained scrutiny, payers need a methodology that produces durable results, not point-in-time fixes.
Standard analytics tools and system logs capture what IT systems record. They cannot see how staff actually navigate between applications, handle exceptions, or compensate for broken processes. Those invisible behaviors are where most administrative cost accumulates.
Applying automation or technology investment to this incomplete picture produces a predictable outcome: the wrong version of the workflow runs faster, at greater scale, with the same underlying problems intact.
What does work observation and process visibility give healthcare payers that analytics cannot?
Process visibility can help uncover how work happens across payer systems. Observing work across every application used, every manual step taken, and every workaround that experienced processors have built around broken workflows over time reveals the operational reality of an organization’s operations.
Having an accurate, real-time view of how work happens (versus how it was designed to happen) is a key first-step to holistic transformation and AI maturity.
For example, unlike event-log-based tools that surface 15-20% of actual work activity, Skan AI's observation-first approach captures 100% of desktop-level work across every application in the payer environment. This view becomes the foundation for the platform’s Context Graph of Work: a continuously updated model of operations. In this context graph, payer workflows can be mapped supports to show every improvement, automation decision, and technology investment with observed operational evidence of their outcomes rather than assumptions.
Here's how observation-based process intelligence compares to event-log tools across seven operational dimensions:
| Dimension | Observation-First | Event-log tools |
|---|---|---|
| Process visibility | 100% of desktop-level work across all applications | 15-20% via backend system logs |
| Time to first insight | Initial findings within a few weeks | 3-6 months implementation before data flows |
| Exception path coverage | Full capture of workarounds and manual compensations | Blind to workarounds not recorded in system logs |
| Data source | Desktop-level observation of actual work performed | Event logs generated by system transactions |
| Coverage scope | All applications in use, regardless of system type | Applications with event logging enabled only |
| Deployment model | No IT projects, infrastructure changes, or integrations | Requires IT project setup before deployment |
Phase 1: How do payers build an accurate operational baseline?
An accurate baseline tells operations leaders exactly where administrative time is going, at a level of precision that standard reporting cannot provide. This is what Skan AI means by the shift from guesswork to certainty: every subsequent decision is grounded in observed operational reality rather than assumed workflows. Phase 1 answers the question every VP of Operations has but rarely has reliable data to answer: where are my teams actually spending their time?
Process intelligence is deployed across targeted roles including claims processors, case reviewers, and member service agents. It continuously observes work performed across claims, enrollment, prior authorizations, and member service functions without disrupting existing operations or requiring IT integration. Initial findings arrive within a few weeks.
Key metrics captured in Phase 1:
• Workforce utilization rates across teams and roles
• Application usage patterns and system-switching frequency
• Average time to complete end-to-end claims processing tasks
• Volume of manual interventions required per transaction
The baseline also creates precise benchmarks for measuring every subsequent phase with confidence. It is the layer that makes optimization evidence-based rather than directional.
Phase 2: How do payers turn baseline data into targeted optimization?
Phase 2 narrows focus to the roles with maximum operational impact: claims adjudication, prior authorization reviews, and provider data management. Detailed process telemetry identifies the specific bottlenecks, non-value-added steps, and workflow divergences that standard improvement efforts miss.
Improvements are implemented and validated across multiple structured improvement cycles. This gives operations leaders a rigorous, evidence-based method for refining workflows, reducing costs, and improving service-level performance with measurable precision rather than directional change.
Key activities in Phase 2:
• Standardizing workflows based on top-performer behaviors identified in the baseline phase
• Eliminating rework and duplication across claims and member service operations
• Optimizing case routing to match work complexity with appropriate resources
• Conducting variant analysis to understand the root causes of process divergence
Prior authorization workflows benefit most from this phase. These processes are dense with informal coordination, compensating workarounds, and exception-handling patterns that standard audits never surface. Seeing them clearly is what enables targeted interventions that produce measurable throughput improvements across the prior auth cycle.
Payers using this approach consistently achieve cycle time reductions of 30-40% in targeted workflows (Skan AI, 2026).
How does phase 3 lock in the gains through technology?
Phase 3 ensures that technology investments enhance optimized workflows rather than automate broken ones. This is the sequencing mistake that most payer transformation programs make: committing to automation or new platforms before the team has an accurate picture of what the optimized workflow should look like.
Phase 3 focuses on three capabilities:
• Automation identification: Mapping high-yield automation opportunities in routine, rule-based activities across claims and enrollment based on actual usage data from Phases 1 and 2, not vendor-led assumptions
• Digital adoption: Tracking utilization of new tools across teams and locations to confirm adoption is consistent and measurable, not assumed
• Application rationalization: Simplifying the technology stack based on actual system usage patterns, eliminating tools that add friction, and reallocating investment toward capabilities that generate measurable returns
As enterprises scale agentic AI programs, over 40% of those projects will be canceled by 2027 due to absence of operational context (Gartner, 2025). The operational ground truth generated across Phases 1 and 2 is the data layer that agent operating procedures draw from. The agentic AI for healthcare payers overview covers the full readiness framework.
What outcomes do healthcare payers achieve with this approach?
The phased approach produces compounding results across people, process, and technology simultaneously. One major US health insurer working with Skan AI identified $28M in annual savings across 26,000 frontline agents by standardizing operations and optimizing workforce capacity (Skan AI, 2026). Cycle time reductions of 30-40% and productivity improvements of 20-35% in targeted roles are consistent with payer deployments covered in the Claims, Costs, and Clarity eBook.
The pattern holds across organization sizes and payer types. Organizations that build an accurate operational baseline before investing in automation achieve durable, measurable results. Those that sequence technology ahead of visibility tend to automate the wrong workflows and encounter the same performance issues at greater scale, with higher sunk costs.
This operational visibility also delivers compliance benefits that extend beyond cost reduction. The CMS Prior Authorization Final Rule requires health plans to make decisions faster and with greater transparency, creating direct compliance pressure on the workflows where process telemetry delivers its fastest returns.
Continuous process telemetry creates a verifiable record of how claims and prior authorization decisions are made across every team and location. For payers managing revenue cycle operations at scale, this visibility directly reduces denial rates and appeals cycle times.
Which healthcare payer organizations are best positioned for this roadmap?
Healthcare payer organizations achieve the strongest results from this roadmap when three conditions are present:
• Operations span multiple systems, applications, or geographic teams with limited visibility into workflow variation
• Improvement goals exceed what existing BI or reporting tools can support with sufficient precision
• Leadership is willing to establish an accurate current-state view before committing to automation or new platform investment
Health plans, managed care organizations, and regional payers running claims adjudication, prior authorization, utilization management, or member services at scale are consistent high performers. The full deployment criteria and phase-by-phase KPI benchmarks are in the Claims, Costs, and Clarity ebook. Skan AI for healthcare payers covers vertical-specific deployment approaches.
Frequently Asked Questions
The three phases are baseline, optimize, and digitize. Phase 1 establishes an accurate view of how work is performed across claims, prior authorizations, and member services. Phase 2 uses that baseline to target and validate specific operational improvements. Phase 3 ensures technology investments are applied to optimized workflows rather than broken ones, maximizing automation ROI across the payer operation.
Automation amplifies the process it runs on. If the underlying workflow contains variation, rework, and workarounds, automating it accelerates those problems rather than eliminating them. A baseline built from actual desktop-level observation ensures that automation targets the right version of a workflow, not the designed version that diverges significantly from how claims and prior auth work actually happens.
Phase 1 baseline observation typically delivers initial findings within a few weeks and does not require IT integration or backend system access. Phase 2 optimization runs across multiple structured improvement cycles validated with precision. Phase 3 digitization follows once optimized workflows are stable and automation candidates are prioritized based on observed operational data from the first two phases.
No. Skan AI's observation-first approach captures desktop-level activity across all payer applications without backend integrations, API connections, or IT projects. Deployment begins quickly and does not require extended implementation timelines or changes to existing payer systems, which is why healthcare payer operations see initial findings in weeks, not months.
Claims adjudication, prior authorization, member services, utilization management, and enrollment are the highest-impact areas. Prior authorization workflows are particularly dense with informal coordination and compensating workarounds that standard audits do not surface. Continuous process telemetry gives operations leaders visibility into those patterns for the first time, enabling targeted interventions that produce measurable throughput and cycle time improvements.
Continuous process telemetry creates a verifiable record of how work is performed across all regulated workflows. It identifies deviations from documented procedures in real time, supports timely compliance with Centers of Medicare and Medicaid Services (CMS) prior authorization mandates, and gives compliance teams an auditable view of how decisions are made across teams and locations without requiring manual audit activity or additional staff time.
