Building an enterprise AI architecture for healthcare and benefits
In conversations with HR and benefits leaders across industries, we often hear a common question: "Oh, so it’s like ChatGPT?" It's a fair question. The explosion of simple AI applications has created the impression that enterprise AI might be equally straightforward to implement - just add an API call to an LLM and you're done.
Our experience at Avante tells a different story. The path to meaningful AI transformation requires far more than connecting to an API. It demands sophisticated data pipelines that connect siloed benefits administration systems, security frameworks that protect sensitive employee health information, and workflow orchestration that coordinates actions across HR teams, finance departments, and benefits consultants. A simple LLM interface can't reconcile inconsistent data formats between your third-party administrators (TPAs) and the proprietary analyses from your benefits broker. It can't maintain HIPAA compliance while sharing appropriate information with external partners. These challenges form the true foundation of enterprise AI - the hidden infrastructure that separates trivial demos from transformative business solutions.
The Real Challenges in Enterprise Benefits Management
1. Integration Across Organizational Boundaries Is Non-Negotiable
Large employers operate within a complex ecosystem of internal teams and external partners. HR teams work with their benefits consultants and brokers to navigate complex landscapes across benefits administration platforms, third-party administrators, wellness programs, and pharmacy benefit managers.
For AI to deliver real value in benefits management, it must seamlessly integrate not just internal systems but also the tools and workflows that connect employers with their consulting partners. This requires sophisticated connectors and data transformation pipelines that respect organizational boundaries while enabling fluid information flow across the entire benefits ecosystem.
2. Real Work Happens Across Teams and Partners, Not in Chat Windows
Perhaps the most important insight from our work in benefits: true value comes from addressing the fragmented nature of benefits management that spans internal teams and external partners.
Challenge: Establishing a Shared Language While Preserving Specialized Needs
Benefits ecosystems require a common vocabulary that bridges organizational boundaries. Each stakeholder - from HR teams to benefits consultants to finance departments - operates with their own terminology, priorities, and frameworks. Effective AI must translate between these specialized languages while preserving the nuanced requirements of each group.
For example, when discussing pharmacy benefits, HR may focus on employee satisfaction metrics, finance examines cost containment measures, and benefits consultants analyze clinical outcomes. Without a unified semantic layer that respects these different perspectives, communication breaks down into a game of telephone where critical details get lost in translation.
Challenge: Workflow Automation Beyond Simple Q&A
Consider the reality of how benefits work actually happens:
A benefits manager needs to determine if a specialty drug program is delivering ROI. Today, this process typically involves:
The benefits team requesting program performance data from their broker or consultant
The consultant manually collecting data from the pharmacy benefit manager portal
The consultant creates a custom analysis in their proprietary format and sends it via email
The benefits team then needs to reconcile this analysis with their internal finance data
Multiple follow-up emails clarify methodologies and resolve data discrepancies
After weeks of back-and-forth, they finally have enough aligned information to make a decision
This distributed process creates multiple points of friction, information loss, and delays. The benefits team, despite having knowledgeable consultants, still finds themselves caught in administrative coordination rather than strategic planning.
The real opportunity isn't in answering isolated questions faster - it's in creating a connected ecosystem where internal teams and external partners can collaborate seamlessly. AI's potential extends far beyond providing faster answers to include orchestrating complex workflows, maintaining context across interactions, and proactively identifying process bottlenecks.
Properly designed benefits AI doesn't replace consultants and brokers - it amplifies their expertise by eliminating the fragmented data collection and reconciliation that consumes everyone's time, while ensuring all stakeholders can communicate in their preferred terms and frameworks.
Building a Platform That Supports the Benefits Ecosystem
These challenges require a platform architecture that addresses the specific needs of the modern benefits environment. Rather than starting with AI capabilities and trying to retrofit them into existing operations, our platform begins with the reality of existing collaborative workflows, then enhances them with purpose-built AI components.
The Agentic Architecture: Specialized Intelligence Supporting Multiple Stakeholders
Our platform transforms benefits operations through specialized components. Some examples from our constellation of agents:
Data integration agents connect and normalize information from TPAs, carriers, and point solutions
Document processing agents extract relevant information from benefits guides, plan documents, and policy information
Collaboration agents facilitate secure information sharing between employers and their brokers/consultants
Employee engagement agents provide personalized guidance through email, Slack, Teams, and CRMs like ServiceNow, Zendesk, and Zoho Desk
Analytics agents identify trends and insights in plan performance and utilization
Orchestration agents coordinate the overall process flow and manage exceptions across organizations
Each agent focuses on what it does best, creating a system that respects the specialized roles of both internal teams and external partners while eliminating the friction that typically exists between them.
Bridging Structured and Unstructured Data Across Organizations
One of the most technically challenging aspects of benefits AI involves connecting unstructured human inquiries with highly structured benefits data - especially when that data spans multiple organizations with different terminologies and data models.
Employees ask questions in natural language ("What's my deductible for therapy?"), HR teams need specific plan details from their consultants, and finance teams require structured reports in their preferred format. Meanwhile, brokers and consultants have their own analytics platforms and reporting methodologies.
This multi-layered disconnect requires a semantic layer that maintains mappings between natural language concepts, technical implementations across benefits systems, and the proprietary frameworks used by consulting partners.
For example, when a benefits leader asks a seemingly straightforward question like "How many of our employees are getting preventative care?", our platform must navigate a complex web of terminology and data structures:
In the employer's HRIS system, employees might be categorized by department codes, employment status flags, and benefit eligibility markers (e.g., "FT-EE-ELIG")
In the medical claims database, preventative care could be represented by hundreds of different CPT codes (e.g., 99381-99387 for preventative exams, 77067 for mammograms, 45378 for colonoscopies)
In the carrier's reporting portal, these might be grouped under a proprietary category called "Wellness Visits" or "Preventative Services"
The benefits consultant's analytics platform might use another proprietary classification system that groups these services into "Primary Prevention" and "Secondary Prevention" categories
The pharmacy benefit manager might track preventative medications using entirely different coding systems and categorizations (like GPI drug classes)
Without a sophisticated semantic layer, answering this simple question would require manual extraction and reconciliation of data from each system, followed by complex transformations to align the different coding systems. Our platform handles this complexity through a combination of domain-specific knowledge bases, relationship maps between terminology systems, and AI-powered classification that identifies equivalent concepts across different data models.
The result is that stakeholders can interact with the system using their natural terminology, while the platform handles the complex translation to and from the structured data formats used across the benefits ecosystem.
Beyond the Wrapper: How Our Platform Transforms Benefits Management
Benefits management represents a complex challenge precisely because it spans organizational boundaries and extends across the entire benefits lifecycle. Avante's platform addresses these challenges through:
Secure connections to both internal systems and partner-managed platforms
A unified data model that normalizes information while preserving its lineage
Communication capabilities that integrate with collaboration tools used across organizations
Customizable workflows that respect different roles while facilitating seamless handoffs
As we continue to develop our platform, several principles guide our approach:
1. Enhance Partnerships, Don't Disintermediate Them
Rather than attempting to replace brokers and consultants, our platform amplifies their value by eliminating administrative burden and providing richer analytical foundations. This allows trusted advisors to shift their focus from data gathering to strategic guidance.
2. Support the Entire Benefits Lifecycle
Benefits management isn't a collection of discrete tasks - it's a continuous cycle of planning, implementation, monitoring, and refinement. Our platform supports this entire lifecycle, providing continuity across annual renewal periods and creating institutional memory that persists even as team members change. This comprehensive approach ensures that insights from one phase inform decisions in the next, creating a virtuous cycle of continuous improvement.
3. Build for Multi-Stakeholder Alignment
Perhaps the greatest opportunity in benefits AI is creating alignment across previously disconnected stakeholders - HR, finance, employees, brokers, consultants, and vendors. Our platform is designed to facilitate this alignment by providing a common foundation of information while respecting the unique perspectives and needs of each participant.
Conclusion
The "GPT wrapper" myth misses what truly matters in enterprise AI. Building effective benefits AI requires more than connecting to an LLM - it demands robust infrastructure that connects disparate systems, bridges organizational boundaries, and orchestrates complex workflows across teams and partners.
This technical foundation enables something far more valuable than faster Q&A. It frees HR teams from administrative busywork, gives consultants better tools to deliver strategic guidance, and provides employees with personalized support when navigating their benefits.
Our vision isn’t just about building better benefits, it’s about building better workplaces. When employees can easily access the care they need and HR teams can focus on people rather than paperwork, organizations can create environments where people genuinely thrive. That's the true promise of enterprise benefits AI.
