Equipping 10,000 service representatives with intelligent search
For another leading healthcare organization, the internal complexity of member support had become a barrier to efficiency. With approximately 10,000 customer service representatives (CSRs) managing over 22 million annual inquiries, primarily regarding complex benefits and coverage, reliance on manual searches across fragmented repositories drove up average handle time (AHT) and compromised both the consistency and quality of the member experience. We developed a Google-like cognitive search experience designed specifically for agent enablement.
- Unified content layer: Structured and unstructured content was brought together and paired with member context to generate accurate, personalized search results for each inquiry.
- Semantic intelligence: Azure Cognitive Search, semantic search, AI/ML models, and a real-time feedback loop enabled continuous refinement and improved result relevance over time.
- Intuitive design: A familiar, search-engine-style interface aligned closely to existing agent workflows, driving adoption while helping teams respond with greater confidence and consistency.
The result was a smarter support environment where agents could surface relevant answers quickly, improve the quality of member interactions, and reduce inefficiencies tied to manual searching. The organization projected a 6–8% reduction in AHT, $10 million in estimated gross savings over three years, alongside improved agent productivity, higher first-call resolution, and fewer repeat callbacks.
Optimizing payer operations with a family of AI agents
For a leading healthcare payer, process fragmentation across triage, case management, and compliance was driving up cost to serve. Modern healthcare environments had reached a level of complexity where traditional automation was no longer sufficient. Without real-time, context-aware decision support, agents remained burdened by disjointed tools, slowing resolutions and hindering scalable growth across operational functions. We designed an agentic AI-powered family of agents built on a platform-led architecture for payer operations.
- Contextual intelligence:ai-powered intent recognition combined with task-based agents sharing context retrieval via cloud-native services and Redshift enabled faster, more accurate routing and resolution.
- Parallel processing: Asynchronous execution reduced latency across multi-step workflows, with human-in-the-loop checkpoints embedded at key decision points to maintain oversight.
- Unified orchestration: Process-based agents operated across triage, case management, and compliance with full context continuity, supported by PEGA-based business process management and customer-facing interfaces.
This created a connected operating model where specialized agents coordinated across tasks instead of operating in isolation. The organization achieved improved user experience and sentiment, a ~20% year-over-year reduction in cost to serve, improved process metrics across case management, triage, and compliance, and increased operational efficiency by automating repetitive, decision-heavy processes.
What else is covered in the PDF?
The full eBook includes three additional transformation stories. An optical character recognition (OCR)-led mobile solution that cut form completion time by 80% and reduced user drop-off by up to 60% for a leading healthcare player. A capture AI deployment that automated 1,800 daily Medicaid pre-authorization requests, accelerating access to care while improving service-level agreement adherence. And a personalized digital concierge that helped unlock the 70% of over-the-counter benefits, approximately $5 billion annually, that remain unutilized, driving a 25–35% increase in spend utilization for seniors.
