The push toward Agentic AI systems that can autonomously reason, plan, and execute tasks is clear. By 2028, these technologies will be making many work decisions. Agentic AI promises gains in efficiency, reduced processing times, and lower error rates across core health plan operations. Whether it’s claims, underwriting, premium billing or enrollment, Agentic AI can make you more productive.
However, a challenge remains: how do you connect a state-of-the-art AI system with decades-old core administration platforms, enrollment engines, and premium billing solutions that you never designed for this level of autonomous interaction?
For operations leadership, the fear of a costly, multi-year rip-and-replace project may be a bottleneck to AI adoption. The good news is that Agentic AI doesn’t demand a technological overhaul. It requires a strategic integration layer.
What Are the Benefits of Integrating Agentic AI with Core Administration Systems?
Instead of simply processing transactions, Agentic AI agents can predict future challenges—flagging a member at high risk of non-payment before the grace period ends or identifying a complex claim that requires human review before it gets stuck in a manual queue. This pre-emptive intervention reduces costly rework and manual interventions.
Ultimately, this leads to a reduction in administrative cost ratios and improves data accuracy. By automating multi-system data reconciliation and complex rule-based decisions, Agentic AI frees highly skilled human employees to focus exclusively on tasks that require critical thinking, judgment, and member empathy, maximizing human value.
However, a fundamental challenge remains: how do you connect a state-of-the-art AI system with decades-old core administration platforms, enrollment engines, and premium billing solutions that were never designed for this level of autonomous interaction?
How do I connect Agentic AI to Core Administration Systems?
Think of an Agentic AI system not as a replacement for your core system, but as a highly intelligent, goal-oriented employee. This “employee” needs access to data and the ability to trigger actions across disparate systems to complete a single task (e.g., process a complex claim or resolve a member dispute).
The goal is to leverage your existing data and processes while adding a new dimension of smart, goal-oriented automation. You can achieve this goal through an integration strategy focused on three key pathways:
API Connectors
- How it Works: The Agentic AI uses the system’s dedicated APIs to send and securely retrieve structured data. For example, the AI might pull a member’s current eligibility status from the enrollment system. Then, send a corresponding bill update to the premium billing solution.
- The Advantage: Direct API access is fast, reliable, and minimizes the risk of data translation errors. APIs ensure the AI is operating on clean, real-time information.
Middleware Translators
Where direct, documented APIs don’t exist, a specialized integration layer, or middleware, acts as the interpreter.
- How it Works: The AI communicates with the middleware, which is designed to translate the AI’s requests into the format required by the legacy system. The middleware manages the secure handoff of data between the new and old environments.
- The Advantage: Middleware enables Agentic AI to interact with systems that have limited integration points without rewriting the core system code. It is the necessary translator for complex, multi-step process orchestration.
The RPA Bridge
For the most deeply entrenched, legacy systems with minimal or non-existent APIs, Robotic Process Automation (RPA) tools serve as the agent’s eyes and hands.
- How it Works: The Agentic AI directs an RPA bot to physically navigate the legacy system’s user interface (UI)—logging in, clicking buttons, extracting data from screens, and inputting new information—just like a human employee would.
- The Advantage: RPA is the short-term, low-code solution for systems that are functionally robust but integration-poor. It allows the AI to start delivering value immediately by automating repetitive, screen-scraping data movements.
What Do I Need to Know About Data Readiness?
Regardless of the pathway chosen, Agentic AI thrives only on high-quality, accessible data. Integration is not only about connecting systems. It’s also about cleaning and structuring the data so the AI can reason accurately.
- Before Integration: Operations teams must work with IT and Data Science to structure existing data. Unclean, siloed, or poorly defined data will lead to AI “hallucinations” or poor decision-making, undermining the entire investment.
- The Governance Check: Strong AI governance—including clear policies for security, privacy (like HIPAA adherence), and bias detection—must be in place before the agent is given autonomous access to sensitive data across multiple core systems.
By embracing a layered integration strategy using APIs, middleware, and RPA, health plans can successfully deploy Agentic AI today. This approach protects existing technology investments while ensuring the organization can capitalize on the next wave of automation, improving the efficiency and productivity of your organization.
What are Two Strategies for Success?
Successfully integrating Agentic AI requires more than just technical connectivity. It also demands organizational and strategic alignment. Operations leaders should consider two crucial, non-technical strategies:
Organizational Alignment and Sponsorship
The most successful AI initiatives are never purely IT projects. Cross-functional projects sponsored by leadership from the C-suite (COO, CFO) typically perform best. This sponsorship ensures that resources are allocated and that teams are motivated to change their processes. Consider establishing a dedicated AI Steering Committee composed of members from Operations, Finance, IT, and Compliance. This committee ensures you prioritize use cases based on business value and consistently apply ethical guardrails, preventing the silos that often hinder complex integration projects.
The Pilot-and-Scale Methodology
Avoid the temptation to implement a massive, enterprise-wide AI system all at once. Instead, adopt a pilot-and-scale methodology. Select a single, high-volume, repetitive process with clearly quantifiable metrics—such as triaging inbound member inquiries or automating a specific step in the claims review—and define success metrics before deployment. Once the pilot demonstrates a clear Return on Investment (ROI), use that success story to secure funding and buy-in for the next phase.
Certifi’s health insurance premium billing and payment solutions help healthcare payers improve member satisfaction while reducing administrative costs.
