What Is AI? A Plain English Explanation for Finance Professionals

You keep hearing about it. Your CFO mentioned it in the last all-hands. Your ERP vendor just emailed about it. But what actually is AI, and how does it work?

Let’s cut through the noise. AI stands for Artificial Intelligence, but that phrase has been stretched to cover everything from a basic Excel formula to systems that can write code, pass professional exams, and hold a conversation indistinguishable from a human. Not all AI is the same thing, and understanding the difference matters if you want to use it properly.

Here’s the version that actually makes sense for someone working in finance.

The old AI vs the new AI

For decades, AI meant rules-based systems. A program that followed instructions: if X happens, do Y. Your email spam filter, your bank’s fraud detection, the autocomplete in your search bar. These are all “AI” in the broad sense, but they’re just sophisticated if/then logic written by programmers.

What changed everything was a different approach called machine learning. Instead of writing rules, you feed the system enormous amounts of data and let it figure out the patterns itself. Show it millions of emails labelled “spam” or “not spam” and it learns to tell them apart on its own. Show it millions of images labelled “cat” or “dog” and it learns to see the difference.

That was impressive. But it still wasn’t what we have now.

Large Language Models: the thing everyone’s talking about

The AI that’s reshaping finance right now is a specific type called a Large Language Model, or LLM. ChatGPT, Claude, Gemini, Copilot — these are all LLMs.

Here’s how they work at a high level. Researchers took an enormous amount of text — billions of web pages, books, articles, code, research papers — and trained a system to predict what word comes next in any given sequence. That sounds simple. The results are anything but.

When you train on enough data, the system doesn’t just learn to predict words. It builds an internal model of the world. It learns grammar, logic, reasoning, facts, how different topics relate to each other, how to structure an argument, how to explain a concept at different levels of complexity. All of it emerges from that one task: predict the next word.

The result is a system that can hold a conversation, write code, analyse data, summarise documents, answer questions — and do all of it in the context of whatever you’ve told it.

Why does it sometimes get things wrong?

This is important for finance people specifically. LLMs don’t “know” things the way a database does. They generate responses based on patterns learned during training. That means they can sometimes produce plausible-sounding but incorrect information — this is called hallucination.

For tasks like creative writing or summarising a document you’ve already given it, hallucination is rarely a problem. For tasks like pulling specific financial figures or legal citations from memory, you need to verify the output.

The rule of thumb: use AI to do the thinking, the drafting, the structuring. Use your own expertise to verify the facts. That combination is almost unbeatable.

What this means for your day-to-day work

The mental model that clicks for most finance professionals is this: think of it as the most knowledgeable colleague you’ve ever had, who works at your pace, never gets tired, never judges you for asking a basic question, and can turn around a first draft of almost anything in seconds.

It won’t always be right. It needs clear instructions. It works best when you treat it like a conversation, not a search engine. But once you get used to working with it, you’ll wonder how you managed before.

That’s it. That’s what AI is. The rest is just detail.