In very short, "AI" is short for "artificial intelligence." Per the OED, AI is defined as "the capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this." When it comes to AI in marketing, that definition is a bit broad. For our purposes, it's best to say that AI is the ability of virtual machines to deliver human-like work product in response to repetitive tasks. This definition of AI in marketing encompasses varying levels of complexity. These can be broken down broadly into traditional AI, machine learning and deep learning. Each of these is a subset of the prior, like so: "AI" in the broadest sense is the basic ability for a machine to follow directions and deliver work product. Examples of AI include round-robin routing of website leads, data enrichment for CRM records, and automated email follow-ups. Basically, any "workflow" associated with marketing automation can and should be considered AI. Within AI is machine learning. In broad strokes, this refers to a machine's ability to identify patterns in data it's given, and execute tasks accordingly. Whereas "traditional" AI requires explicit instructions (e.g., send an email to the address specified), machine learning allows for some degree of autonomy (e.g., analyze this traffic data, and predict the next three months' results). Excel's FORECAST capabilities are a good example of machine learning for marketing. Finally, there's deep learning – a subset of machine learning. The line between the two disciplines is blurry, but the upshot is that deep learning typically relies on "neural networks" – sets of algorithms designed to replicate the human brain. This allows for more free-form inputs (e.g., data lakes rather than spreadsheets), and more complex or free-form analysis. In essence, a deep learning model should be able to "train" itself, rather than simply answering a question based on what it's given.