Case Studies

Engagements where AI moved from concept to measurable impact — across Medicare growth, cost management, member experience, and enterprise intelligence.

Health Insurance · Medicare Advantage
Classical AI Generative AI

AI-Driven Capital Allocation Engine for a National Medicare Insurer

A national Medicare Advantage insurer was optimizing its marketing spend, broker relationship management capacity, and member incentive programs independently — with no unified framework to answer: where does the next dollar generate the most value?

The Challenge

Each commercial lever — direct marketing spend, broker outreach capacity, and retention incentives — was optimized in isolation. This siloed approach produced inconsistent economic assumptions, obscured diminishing returns, and made enterprise-level capital reallocation decisions nearly impossible to defend to finance leadership. In a margin-sensitive Medicare market, the inability to mathematically answer "should the next incremental dollar go to media spend or broker capacity?" represented a direct competitive disadvantage.

Our Approach

Structural Benchmarking (DEA)

Applied Data Envelopment Analysis to benchmark each distribution unit's efficiency relative to best-practice peers — producing a scored efficiency frontier across all comparable units.

Marginal Response Modeling

Built elasticity models estimating incremental plan applications per additional unit of spend or capacity — including saturation thresholds and confidence intervals per market segment.

Standardized Economic Translation

Introduced a single, finance-approved LTV methodology to translate all operational metrics into a common economic currency — enabling valid cross-lever ROI comparison for the first time.

GenAI Decision Layer

Integrated generative AI to automate data documentation, explain efficiency gaps in plain language ("why is this distribution unit below the frontier?"), and surface decision-ready recommendations for planning teams.

Deliverables

  • Five-layer Capital Allocation Engine architecture — from raw data foundation to constrained enterprise portfolio optimization
  • Standardized Allocation Decision Template with per-segment efficiency scores, marginal ROI rankings, absorptive capacity, and audit-ready recommendations
  • Governance framework with model versioning, drift detection, and agentic alert monitoring
  • 22-week phased implementation roadmap: Mobilize → Build → Controlled Reallocation → Institutionalize

Outcomes

10–15% of total spend identified for reallocation within 6–9 months
3.2× marginal ROI identified in highest-opportunity market segments
1 engine replacing siloed models — one economic standard across all levers

"For the first time, we could sit in a planning meeting and mathematically rank whether additional media spend or additional broker capacity generated more incremental value. That's not a reporting upgrade — that's a fundamentally different way to run the business."

— VP of Growth Strategy, National Medicare Advantage Plan
Health Insurance · Member Experience
Generative AI

GenAI-Powered Member Services & Medicare Enrollment Assistant

A leading regional health plan sought to transform its Medicare shopping and member services experience using generative AI — deploying empathetic, always-on AI assistants that reduce friction across benefit navigation, claims support, and plan selection.

The Challenge

The client's member services operation was call-center dependent, creating friction for members navigating plan selection and benefit questions during the critical Annual Enrollment Period. Leadership wanted to explore GenAI's potential to reduce call volume, improve first-contact resolution, and build trust with an older, less digitally native membership — while managing the innovation risk that comes with deploying AI in a regulated, high-stakes healthcare context.

Our Approach

Internal-First Pilot Strategy

Began with low-risk, internal deployments: an AI chat assistant for the sales team and an AI "listen-in" co-pilot offering live prompts during member calls — before any member-facing exposure.

Medicare Shopping as Proof Point

Used the Annual Enrollment Period as the first external touchpoint — higher intent, lower risk — with A/B testing to quantify impact on conversion and satisfaction before scaling to ongoing service cases.

Workflow-Integrated Platform Build

Designed the AI assistant on an accelerated platform approach — integrating with existing CRM and telephony workflows to minimize IT dependencies and compress time-to-value from months to weeks.

AI Governance & Trust Framework

Established guardrails for compliance, empathy design, and escalation pathways — ensuring AI interactions balanced innovation with member trust and regulatory requirements.

Deliverables

  • Prototype AI Assistant for internal sales team — live within 6–8 weeks of engagement start
  • Scaled rollout roadmap across member and prospect touchpoints with phased exposure gates
  • AI Governance and Trust Framework covering compliance, escalation, and empathy design standards
  • Early insights package on user trust, engagement quality, and call avoidance metrics from pilot

Outcomes

30–50% faster time-to-value vs. traditional development cycles
↑ CSAT measurable improvement in member satisfaction scores in test groups
6–8 wks from engagement kick-off to working internal prototype

"Starting with an internal pilot wasn't just the safe choice — it turned out to be the smart one. Our sales team became advocates before we ever touched a member interaction, and that built the organizational confidence to move fast on the external rollout."

— Chief Member Experience Officer, Regional Health Plan
Health Insurance · Medical Economics
Classical AI Generative AI

AI-Powered Cost Intelligence Platform for Health Plan Medical Economics

Healthcare cost analytics remained stubbornly manual, slow, and reactive for a mid-size health plan whose actuarial team was losing the race against accelerating spend trends. We designed and prototyped an AI-native cost intelligence system to change that.

The Challenge

The client's medical economics function relied on actuarial analysts manually building reports from disparate claims data — a process too slow to catch emerging cost trends before they became budget variances. With 90% of healthcare data created in the past five years, traditional methods were structurally inadequate. The plan was losing millions annually to fraud, waste, and billing inefficiencies that faster detection would have caught.

Our Approach

Real-Time Cost Intelligence Engine

Built a classical ML layer processing claims data continuously — flagging anomalies, projecting trend trajectories, and scoring risk by service category, provider, and member cohort in real time.

Conversational AI for Actuarial Teams

Designed a natural language interface — "Alexa for the CFO" — allowing actuaries and medical economics analysts to query spend trends, drill into anomalies, and generate narrative summaries without writing SQL.

Agentic Fraud & Waste Detection

Deployed autonomous AI agents running continuous pattern recognition across billing data — identifying upcoding, unbundling, and duplicate claims with NLP-assisted case prioritization for the SIU team.

Prescriptive Analytics Layer

Extended beyond detection to recommendation — surfacing actionable interventions for care management, contract renegotiation opportunities, and predictive cost scenario modeling for budget planning.

Deliverables

  • End-to-end cost intelligence platform architecture targeting CFOs, Chief Actuaries, and Medical Economics teams
  • AI chatbot assistant with claims data integration for natural language cost interrogation
  • Automated fraud and waste detection pipeline with investigator-ready case summaries
  • Three-tiered SaaS product design enabling deployment for plans of all sizes

Projected Impact (Modeled)

$5–10M annual recovery potential from fraud, waste, and abuse detection
40% reduction in prior authorization processing time
20–30% improvement in actuarial team productivity through AI-assisted analysis

"Our actuaries were spending the majority of their time building reports rather than interpreting them. The conversational AI layer changed that — within weeks, they were asking questions of the data in plain English and getting answers that used to take two days to produce."

— Chief Actuary, Regional Health Insurer
Health Insurance · Payment Integrity
Generative AI Classical AI

AI-Driven Payment Unbundling Detection & Recovery Automation

A health plan's payment integrity team was losing millions annually to billing unbundling — the practice of submitting separate claims for procedures that should be billed together at a lower composite rate. Manual audit processes caught only a fraction of cases, and investigator bandwidth was the bottleneck.

The Challenge

Payment unbundling is structurally difficult to detect at scale: the patterns are subtle, procedurally specific, and distributed across millions of claims. Existing rule-based detection flagged high false-positive rates that burned investigator time on low-yield cases. Leadership needed a solution that could triage accurately, prioritize by recovery potential, and generate compliance documentation automatically — without increasing headcount.

Our Approach

NLP Pattern Recognition on Claims

Applied natural language processing to unstructured claims narratives and procedure code combinations — identifying bundling opportunities that rule-based systems systematically missed.

ML-Based Anomaly Scoring

Trained classification models on historical adjudicated cases — scoring incoming claims by unbundling likelihood, expected recovery amount, and confidence interval to prioritize investigator queues.

GenAI Case Summarization

Deployed generative AI to produce structured case summaries — complete with evidence, applicable billing guidelines, and recommended corrective action — reducing investigator prep time per case by over 60%.

Provider Collaboration Workflow

Built a structured feedback loop integrating SIU resolutions back into model retraining — improving detection precision over time and enabling payer-provider dispute workflows with full audit trails.

Deliverables

  • End-to-end payment unbundling detection pipeline integrating claims data, NLP, and ML scoring
  • AI-generated case summary templates reducing per-case investigation time
  • Continuous model improvement loop with SIU feedback integration
  • Compliance documentation automation aligned with payer-provider legal standards

Outcomes

3–5% of total claims flagged — reduced overpayments previously missed
$4–8M in annual recovery potential identified at pilot scale
↓ 50% reduction in false-positive rate vs. prior rule-based detection

"The model didn't just find more cases — it found the right cases. And the AI-generated summaries meant our investigators were spending time on judgment, not on documentation. That's the leverage we needed."

— VP of Payment Integrity, National Health Plan
Health Insurance · Strategic Advisory
Generative AI

GenAI Use-Case Prioritization & ROI Framework for a Health Plan Executive Team

A health plan's executive leadership needed to move from "AI is interesting" to a board-ready investment strategy — with a defensible framework for prioritizing which GenAI use cases to fund first, and a credible financial model for expected returns.

The Challenge

The client had dozens of potential AI use cases in discussion — spanning prior authorization automation, fraud detection, member chatbots, clinical documentation, and appeals management — but no structured method for prioritization. Business units were lobbying for their own use cases independently, creating competing proposals without common ROI language. The CFO needed a single framework that could objectively compare opportunities and sequence investments by strategic value and implementation feasibility.

Our Approach

Use-Case Taxonomy & Mapping

Structured nine priority GenAI domains across cost management, clinical operations, and member experience — with standardized assessment dimensions: GenAI role, ROI sources, financial metrics, implementation complexity, and risk profile.

Financial Impact Modeling

Built per-use-case financial models with estimated annual savings ranges, time-to-value horizons, and confidence ratings — grounded in industry benchmark data and pilot results from comparable plan deployments.

Implementation Sequencing

Designed a phased rollout sequence — prioritizing high-ROI, lower-complexity use cases first to generate early wins and fund subsequent phases — with governance gates between phases.

Board-Ready Investment Narrative

Packaged findings into a C-suite and board presentation format — translating technical capability assessments into strategic investment decisions with risk-adjusted return profiles.

Deliverables

  • Comprehensive GenAI use-case assessment across 9 priority domains with standardized scoring
  • Financial impact models for each use case with low/mid/high scenario ranges
  • Phased implementation roadmap with budget allocation recommendations
  • Executive presentation package suitable for board-level investment approval

Outcomes

9 domains assessed with defensible ROI models and sequenced investment cases
$30–50M in aggregate annual savings potential identified across the full portfolio
1 framework adopted enterprise-wide as the standard for AI investment decisions

"We walked into that board meeting with a clear answer to 'where do we start and why?' — and a financial model that held up to scrutiny. That level of rigor is what separates a GenAI strategy from a GenAI wish list."

— Chief Digital Officer, Regional Health Plan

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