Key terms: AI, machine learning (ML), and generative AI

- [Instructor] Before we start building models or generating reports, we need to be clear on the foundational terms behind AI. These concepts are often used interchangeably, but each plays a distinct role in how we apply AI to financial reporting. Let's start at the top. Artificial intelligence, or AI, refers broadly to the ability of machines to perform tasks that would normally require human intelligence. Things like interpreting language, recognizing patterns, or making decisions. Within AI, one of the most impactful subfields for finance is machine learning, or ML. ML is a method of teaching machines to learn from data. Instead of hard-coding rules, like If This Then That, we feed historical data into an algorithm that identifies patterns and uses them to make predictions. In the context of finance, this could mean forecasting future cash flow based on historical trends, estimating the likelihood of a late payment, or flagging unusual spending patterns in real time. There are a few things to know about classical machine learning that are especially important in finance. Number one, it's deterministic. That means for a given input and a trained model, it will always give you the same output. This consistency is critical when we're dealing with financial data, where accuracy, traceability, and auditability are non-negotiable. Number two, it relies on structured data, the kind you typically have in your GL, ERP, or spreadsheets, time series data, numerical values, and well-defined columns. Now, let's move to generative AI, which has been getting a lot of attention thanks to tools like ChatGPT, Gemini, Claude, and others. Generative AI is a different class of model that doesn't just classify or predict, it creates. It can generate human-like text, write code, summarize reports, even simulate conversations. But here's the key distinction. Generative AI is probabilistic. It doesn't always give the same answer, even when you provide the same input. Instead, it uses probability to decide which word or phrase is most likely to come next. That flexibility is incredibly useful for narrative generation, but it also means that outputs can vary and sometimes contain hallucinations or subtle inaccuracies. And that matters in finance. When you're generating a board report or a variance analysis with generative AI, it's your job to validate the output. Generative AI is powerful for drafting, summarizing, and reframing, but it's not a calculator. You shouldn't rely on it for precise numerical computations or reconciliation directly. In this course, we're going to show you how you can use these tools to do math using their coding functionality. So to recap, AI is the umbrella term for intelligent automation. ML helps you make data-driven forecasts and detect patterns, and it's deterministic, consistent, and ideal for structured financial data. Generative AI helps you create content and narratives, but it's probabilistic, so it must be used carefully in high-accuracy environments. Understanding these differences helps you select the right tools for the right parts of your workflow and ensures you're getting the benefits of automation without compromising the integrity of your financial reporting.