A Practical Guide to AI Implementation for Health Plans

Healthcare organizations are understandably slow to embrace Artificial Intelligence (AI), given the potential impact on members’ lives. According to research from the SpringStreet Exchange, most surveyed healthcare organizations haven’t advanced beyond pilot projects with AI. 

But when built on a strong foundation and implemented organization-wide, AI can be the tipping point for better operations and member experiences. McKinsey and the National Bureau of Economic Research estimate AI could save organizations 5-10% in healthcare spending. 

The stakes are high for health plans. These organizations don’t just process transactions — they shape access to care. Poorly designed or disconnected AI systems can cause real harm, leading to coverage delays, inaccurate billing, improper denials, and disruptions for members.

To support quality coverage and positive member experiences, payer organizations must build integrated data infrastructures with reliable, observable and auditable AI workflows. 

Building a system that’s ready for AI

Every decision about enrollment, payment, or eligibility impacts a person’s ability to get care. To support these processes responsibly, AI tools require comprehensive, consistent, and current data. But in healthcare, intelligence isn’t enough; predictability matters just as much.

For example, enrollment, eligibility, billing, and communication data are deeply connected; a change in one area affects all the others. If an eligibility change isn’t reflected across all platforms, the billing system cannot calculate the correct premium, potentially leading to incorrect billing. AI will magnify the problem by acting on the wrong data, such as sending inaccurate invoices or misapplying payments. As a result, the patient may end up overpaying or having their coverage cancelled for unintentional missed payments. 

To prevent those risks, health plans must first map where data lives and define the integration points that ensure it flows securely and consistently across systems.

Integrating data is only the first step. Health plan software also needs durable workflows to ensure AI handles the interconnected data correctly. Durable workflows are:

• Stateful: They remember what steps they’ve accomplished so far.
• Recoverable: They can restart from the point of failure.
• Observable: They give a clear view into the tool’s status and decisions. 

Without these characteristics, AI often stumbles over data gaps or contradictions, producing incorrect outputs. Durable workflows make sure every action is logged, recoverable and auditable so AI decisions can be verified.

Given the data privacy and security required in healthcare, health plans must also establish robust governance frameworks for effective oversight of data, software and AI. These policies will implement best practices like encryption, de-identification and data minimization to ensure secure data management and regulatory compliance. Health plans should also conduct regular audits of how their AI systems use data. 

With a secure and connected infrastructure in place, health plans can begin deploying AI responsibly and with confidence.

A measured approach to AI implementation

Health plans can’t just plug an AI tool into their system; implementation requires an incremental approach with a well-defined long-term vision. 

Start with process assessments to identify small wins. Automating enrollment document review, for instance, can deliver immediate impact: AI can ingest forms, validate data, and flag inconsistencies, speeding up approvals and reducing manual work.

Using insights from the operations evaluation, design a step-by-step plan to move from basic AI applications to more complex systems over time.Once a workflow is optimized, move to the next use case — such as billing reconciliation, eligibility verification, or notice fulfillment. Each successful step strengthens the foundation for more complex automation.

Ongoing evaluation is key. As AI learns, it can drift — sometimes introducing bias or error.  Organizations must regularly audit and recalibrate their tools, evaluating and enhancing the source and quality of data as well as the tool’s performance. Steady improvements to systems and processes will prevent drift and lead to improved quality over time. 

This incremental approach builds a strong foundation for more advanced applications, such as the growing push toward agentic AI. Agentic systems are made up of autonomous AI agents that interpret tasks, make decisions and take action. With thorough planning and disciplined implementation, health plans will produce a framework where every agent operates within guardrails, is supervised by structure, and is accountable to measurable outcomes. 

How does AI improve health plan operations?

AI isn’t replacing human decision-making; it’s accelerating processes that support it. For health plans, this means faster enrollment, fewer billing errors, and smoother eligibility management.

AI-powered tools  can execute tasks like:

• Validating enrollment form data
• Cross-checking eligibility against federal and state databases
• Normalizing data for CMS communicationGenerating premium invoices
• Applying payments to the correct account

This automation will reduce errors and delays to promote faster enrollment, smoother onboarding and improved payment management. 

AI also enhances engagement. According to McKinsey, 62% of healthcare leaders believe generative AI will improve consumer experience. Plans can use it to send timely reminders about preventive care, renewals, or payment deadlines — all tailored to each member’s needs.

For example, plans can identify members at high risk of churn and determine how to re-earn their trust. 

AI will be needed for the long-term success of Individual Coverage Health Reimbursement Arrangements (ICHRA), where employees get to choose their own plan rather than being limited to options offered by their employer. While originally viewed as a health coverage tool for small businesses, ICHRA adoption is up 34% among applicable large employers. However, only 14% of consumers surveyed by Softheon feel confident in selecting their own coverage, indicating the need for tailored guidance. 

Preparing for the next AI iteration

In the health insurance industry, a delayed enrollment isn’t just an error; it’s a barrier to members receiving critical care. 

To prevent those barriers, health plans must take a methodical, accountable approach to AI adoption. By building secure, integrated systems and durable workflows from the start, payers can automate responsibly today while preparing for the next generation of intelligence: predictive analytics, decision support, and agentic AI.

At Softheon, we help health plans operationalize AI through automation, integration, and accountability. We empower teams to deliver better, faster, and fairer access to care.