AI in Benefits 101: Why You Should be Cautious of Generative AI With Your Deductibles

Understanding why not all generative AI is built for benefits accuracy

As AI becomes a bigger part of the benefits ecosystem, many HR and benefits leaders are trying to understand not just how it works, but where it’s safe to apply. Generative AI, the technology behind ChatGPT, Copilot, and Gemini, is showing up in demos, proposals and vendor roadmaps at a rapid pace. And while it can be incredibly useful in the right context, its important to understand that not all generative AI is created equal, and there are things to consider when using AI to help answer benefits plan questions. 

This second article in our AI in Benefits 101 series focuses on the “risk” side of the equation: why sometimes, generative AI’s creative strengths make it unsuited for coverage questions, cost details, or anything tied to a member’s health and wallet. You’ll also learn what type of generative AI is designed for that job. 

Two Very Different Kinds of AI—and Only One Works for Benefits

Generative AI is the most familiar form of AI. It drafts, summarizes, rewrites and produces natural-sounding language. It’s excellent for communication-heavy tasks. But benefits guidance requires something fundamentally different, such as accuracy, context and the ability to reference employer-specific plan details. This is why you can't rely on just any generative AI model.

That’s where Guidance AI comes in. Guidance AI is a type of generative AI, but there is a key difference—it is specifically built for the company that's using it. This specialized type of AI is trained on curated, verified data: plan designs, networks, eligibility rules, point solutions and formularies. Its purpose is to return the correct answer.

Understanding the difference between more "creative" generative AI and the accurate, guidance AI is essential for any leader evaluating AI-powered solutions.

Why Generative AI Is the Wrong Tool for Coverage and Costs

Most generative AI models predict language based on patterns. They do not check facts, interpret plan intent, or understand nuances in network design. As a result, they can produce answers that sound confident, yet are completely incorrect.

That weakness is easy to overlook until an employee asks a question like:

• Is this provider in-network?
• What will this procedure cost?
• What’s my deductible balance?
• Is this procedure covered?

These moments demand precision, not creativity. A “close enough” answer can create confusion, unnecessary spend, or a breakdown in trust—all of which carry real consequences for employees and employers.

Popular generative AI tools aren't unreliable because they're early-stage; they are unreliable because they were never built for high-stakes factual questions.

The Data Privacy Problem: Public AI Models Can’t Touch PHI

Most generative AI tools are public, open models. Anything entered into them can influence future training. For benefits leaders, that creates an immediate compliance barrier. Protected health information (PHI), plan documents, eligibility files, and provider contracts cannot be safely shared with a public model. Doing so exposes organizations to HIPAA risks that no HR team can accept.

It is also worth noting that nearly every company now has its own approved AI tool or internal model. These internal systems are designed to operate within corporate privacy standards. HR and benefits leaders should check with their IT or data teams and make sure employees are using the sanctioned tools available to them. Internal models provide the added benefit of building context over time, safely storing prior content and improving as more information is added.

Why Guidance AI Is the Right Type of Generative AI for Benefits

Guidance AI is designed around truth. It pulls from your curated plan data, applies your coverage rules, and integrates with your ecosystem to surface the right option at the right moment. It knows when to escalate to a human specialist. And most importantly, it stays within the boundaries of verified information.

HealthJoy uses this specialized, truth-driven AI to power JOY, its virtual benefits assistant, ensuring every member recommendation is factual, compliant, and personal.

It’s the difference between an answer that “sounds right” and an answer that is right. For benefits leaders navigating a growing landscape of AI tools, clarity on this distinction is essential. 

Coming Next: How the Right AI Elevates HR

If Blog 1 introduced how generative AI works, Blog 2 makes the case for why you must be cautious of the generative AI you use for benefits accuracy. In Blog 3, we look at the opportunity: how the right kind of generative AI (Guidance AI) reduces administrative burden and elevates HR teams into the strategic roles they were hired for.