Hi, over the last six months, during more than a dozen healthcare conferences, I’ve noticed something interesting: people are using the term "Artificial Intelligence (AI)" and "Generative AI (GenAI)" interchangeably. And while this post isn’t meant to be a deep dive into AI, LLMs (large language models), or the technical nuances, it’s worth pointing out that these are not the same thing. My goal here is simple—to get us all thinking about how we can be more specific when discussing AI, so our conversations are more structured and productive.

Here’s an analogy: imagine someone with a cold. They feel a sneeze coming on, and they ask for a Kleenex—but what they’re really asking for is a tissue. Kleenex is just one brand, not the entire category. That’s how AI is often used in conversation—like a catch-all term for all things tech, when what we really mean, in many cases, is Generative AI.

AI vs. Generative AI: What’s the Difference?

At its core, Artificial Intelligence refers to any machine or system that can perform tasks that normally require human intelligence—like decision-making, pattern recognition, or problem-solving. Examples of AI in action include:

• AI in healthcare: Automated systems that analyze medical images for diagnostics.
• AI in finance: Algorithms that predict market trends or manage risk.

Generative AI, however, is a subset of AI that goes beyond recognizing patterns or making decisions. It creates new content—whether that’s text, images, music, or even code. The key difference? It’s not just reacting; it’s innovating. Common tools include:

• ChatGPT: Generates human-like responses in conversations and content.
• Copilot for Microsoft 365: Assists with drafting emails, presentations, and reports by generating content within your workspace.

Why Being Specific Matters

When we blur the lines between AI and Generative AI, we risk oversimplifying discussions and missing out on the nuances that make these technologies valuable in different ways. Many businesses are integrating Generative AI tools without even realizing that’s what they are. Understanding this distinction helps us:

• Set clearer expectations for what each technology can do.
• Tailor conversations around AI to be more precise and solution-focused.
• Foster better collaboration by knowing when we’re talking about data analysis versus content creation.

At the end of the day, specificity matters. Using “AI” as a blanket term, while convenient, can be like asking for a Kleenex when you just need a tissue. The tools, outcomes, and capabilities are different, and so should be our discussions. By being more intentional with how we use terms like AI and GenAI, we can have more structured, tailored conversations that drive innovation and clarity in our industries.

What do you think? Let’s open the floor to a more specific, nuanced discussion on AI!