The Data Front

A simple breakdown to help you understand the AI you’re using

Part 3 in the AI Enablement Series

In my last article, I compared five of the most practical AI chat tools you might use in your day-to-day work. But before we go any further, I want to take a step back.

Not to get technical, but to explain – in plain English – what these tools actually are. Because in my experience with data enablement, whether you’re using dashboards or automated workflows, a bit of clarity on what’s going on behind the scenes always builds confidence.

This article unpacks the key terms and concepts that sit underneath the AI tools many of us are starting to explore. No hype. No data science required.

Just useful language to help you understand what you’re using — and how to make better choices with it.

What is a Natural Language Model?

A natural language model is trained to understand and respond in everyday human language. It’s what makes AI tools feel conversational — even when you’re asking something complex.

If you’ve ever typed a request like “summarise this meeting” or “draft a one-pager in plain language”, and received a helpful response in full sentences, that’s a natural language model at work.

These models are the foundation of most generative AI tools today — from ChatGPT and Claude, to Gemini and Copilot.

What is Generative AI?

Generative AI is a type of AI that creates content. That could be text, images, audio, video — anything that didn’t exist until you asked for it.

In workplace tools, it usually means language. You give it a prompt — and it generates something: a paragraph, a checklist, a summary, a message.

These tools aren’t pulling from a library of pre-written responses. They generate new content based on patterns they’ve learned during training. And the more recent the model, the more fluent and useful the output tends to be.

What is a GPT or a Gem?

You’ll often hear people talk about GPTs or Gems as if they’re standalone tools — but they’re actually custom chatbots built on top of large AI models.

• GPTs are created using OpenAI’s ChatGPT platform.
• Gems are created inside Google’s Gemini tool.

They’re tailored to specific roles or tasks. Think:

• A “New Joiner Assistant” GPT trained on your onboarding documents
• A “Tone Checker” Gem that rewrites updates for internal comms
• A “Policy Helper” that can explain benefits or travel rules based on your organisation’s docs

They don’t remember across chats. But they can be built to respond in a consistent voice, with the same framing, based on the documents or instructions you give them.

This is one of the simplest ways to make AI useful for enablement — and I’ll share some examples of how to do it in a future article.

What is an AI Agent?

While GPTs and Gems are good at responding to prompts, agents are designed to complete tasks.

They’re built to follow instructions, chain actions together, and use other tools to get things done.

An AI agent might:

• Read a document
• Summarise the key points
• Create an email draft
• Send it to a colleague
• Add a follow-up to your calendar

Agents are still emerging — but they’re becoming more relevant for workflow automation and operational use. Think of them as goal-oriented versions of chat tools, built with more autonomy.

I’ll dive into agents in more detail in a later article in this series.

What’s the difference between public and enterprise AI?

If you’re using a free tool like ChatGPT or Claude, your input might be used to train the model further — unless you’ve disabled that setting or you’re on a business plan. These tools are built for open access, not data sensitivity.

Enterprise AI tools — like Microsoft Copilot or Gemini for Workspace — are different. They’re built to operate inside your organisation’s security framework.

This means:

• Your input isn’t used to train the model
• Your data stays inside your organisation’s firewall
• Nothing is shared between users or teams
• The AI doesn’t learn across chats or accumulate knowledge over time

Even though it may feel like it’s remembering or getting smarter, each conversation starts fresh — within the boundaries set by your IT and InfoSec teams.

So if your organisation has rolled out Gemini or Copilot, you can use those tools more confidently for internal content, summaries, and support — without worrying about where the data goes. It’s still worth checking your company’s guidance, but the tools themselves are built for security.

---

Understanding makes you more effective

Just like with data tools, you don’t need to know how to build AI to use it well. But understanding the basics of what’s behind the interface can help you:

• Choose the right tool for the job
• Use it more effectively
• Build trust in how and when to apply it

This is part of how I approach data enablement too — by making the tools more human, the use cases more grounded, and the learning curve less steep.

Next in the series, I’ll unpack what AI agents can actually do, go through prompt writing skills and how AI might start to show up in your everyday work.