When we met with our Customer Advisory Board (CAB) this quarter, one theme came through clearly: AI is now a permanent part of the benefits conversation, and HR leaders are expected to make sense of it in real time.
Everyone at the table had used tools like ChatGPT or Microsoft Copilot. Most were fielding pitches from vendors claiming “AI-powered navigation.” And nearly all were trying to assess which tools were useful, which were risky, and which were simply hype. Across experience levels, the ask was the same:
“Give us a practical way to understand all of this so we can evaluate AI with confidence.”
This mini-series on AI in Benefits exists because of that request. We’re beginning with the foundation: generative AI. This is the type of AI most people encounter first. Over the coming weeks, we’ll build on this foundation to help benefits leaders evaluate where AI truly fits in their strategy.
If you’ve experimented with ChatGPT, Copilot, or Gemini, you’ve already interacted with generative AI. Under the hood, it’s powered by Large Language Models (LLMs), but the core idea is straightforward: Generative AI creates text by predicting the most likely words to come next.
It feels intelligent because its responses are smooth and conversational, but it’s important to understand what’s happening behind the scenes. It isn’t pulling verified facts. It isn’t reasoning through benefit rules. It isn’t “thinking.” It’s generating language based on patterns it has learned from public sentences.
And because it doesn’t know your organization, your plan design, or your network, it can’t inherently tailor answers to the realities of your unique benefits program.
This distinction, between generating language and understanding truth, becomes critical in benefits strategy.
Despite its limitations, generative AI has quickly become a useful tool for HR teams. Many CAB members shared how it’s already helping them communicate more clearly, especially when explaining complex concepts to employees. It can simplify long documents, offer alternatives to dense language, and help craft messages more efficiently.
For communication-heavy work, generative AI can reduce workload and improve clarity. It’s fast, flexible and adaptable, which matters in environments where HR teams are stretched thin.
Generative AI’s biggest strength, producing natural, confident language, is also its biggest weakness. Because it predicts patterns rather than verifying facts, it can generate answers that are incorrect but highly convincing.
This is manageable in creative tasks. It becomes risky the moment an employee asks a question that requires factual accuracy, such as checking network status, estimating a cost of care, or confirming coverage. These decisions affect a member’s health and finances, and accuracy is non-negotiable.
The benefits industry is being flooded with tools claiming AI capabilities. Employees are using AI in their personal lives and often, at work. Leadership teams are asking how AI can reduce workload. And vendors are racing to add “AI-powered” labels to their solutions.
Understanding generative AI—what it is, what it’s good at, and where it fails—helps you navigate this moment with clarity. It gives you the foundation to:
It also sets the stage for the next step: understanding Guidance AI, the curated, plan-specific intelligence designed to deliver accurate, trustworthy answers in healthcare. In Part 2 of this series, we’ll explain why the AI you see in the news is the wrong tool for benefits accuracy, and what to look for instead.