There's no single right answer — but there is a useful way to think about it. Here's our view, plus the best resources for learning more at every level.
People disagree about what counts as AI because the term describes a spectrum of technologies, not a single thing. A useful way to think about it is to ask three questions about any system:
Does it learn? Can the system improve its performance based on data or experience, without being explicitly reprogrammed? If yes, you're in AI territory.
Does it infer? Can the system draw conclusions from incomplete information, recognise patterns, or make predictions? That's a strong signal of intelligence.
Does it adapt? Can the system handle novel situations it wasn't specifically designed for? The more it can, the more "intelligent" it is.
Using these lenses, we can map everyday technologies onto a spectrum:
GPS location, spreadsheet formulas, basic calculators. These follow precise instructions — no learning, no inference, no adaptation.
Route optimisation, traditional autocorrect, rules-based chatbots. Sophisticated — but following pre-defined logic rather than learning. Newer versions may use AI underneath.
Speech recognition, spam filters, recommendation engines, smart thermostats, autonomous rerouting, generative AI. These systems improve with data and handle novel situations.
The boundaries shift over time. Yesterday's AI breakthrough becomes today's "just software." And the same feature — like autocorrect — can be rebuilt from simple rules into a neural network without the user ever noticing the change.
The important thing isn't getting the definition "right." It's understanding that AI is a spectrum, that it's already woven into your daily life, and that where you draw the line shapes how you think about its implications for your work, your organisation, and society.
New to AI? These are the best starting points — clear, jargon-free, and designed for people who want to understand what AI is and what it means for them.
You understand the basics and want to go further — into strategy, economics, and the real-world implications of AI for organisations and society.
You're building with AI or shaping AI strategy, and you need deep, current, technically grounded material.