Artificial Intelligence. Two words that trigger everything from excited coffee chats with my mates to boardroom FOMO to outright apocalyptic fear. AI has dominated every conversation I’ve had for the last six months. Everyone wants a slice of it. It shows up in product roadmaps, vendor decks, job specs, and shareholder updates. But when I scratched beneath the surface, I found a messy pile of buzzwords and vague ambition.
I don’t know about you, but I like simple. So here is my attempt to make AI simple.
If you’re leading digital delivery, managing platforms, or just trying to figure out what AI means for your portfolio, this is your practical intro. There are buzzwords, but I explain them. There is no hype because I don’t have to impress anybody. It is just a clear look at how AI is structured, what matters now, and what’s coming.
AI Isn’t a Thing. It’s a Stack.
We tend to talk about AI like it’s a single capability: “We’re building an AI feature” or “We’re evaluating AI vendors.” But AI is more like a stack of techniques, use cases, and enabling paradigms. I see AI as comprising four layers: Foundational Techniques, Application Areas, Emerging Paradigms and Cross-Cutting Concerns.
Foundational Techniques
These are the underlying methods that let machines learn, adapt, and reason. They’re not shiny, but they are essential.
- Machine Learning (ML): The bread-and-butter of modern AI. ML trains systems to spot patterns and make decisions based on data, without needing someone to code every rule.
- Deep Learning (DL): A special kind of ML that uses multi-layered neural networks. Think of it as the heavy-lifter for tasks like image recognition or voice synthesis.
- Reinforcement Learning (RL): This one’s about trial and error. An agent takes actions, gets rewards or penalties, and learns how to improve its performance over time. Popular in robotics, game-playing AIs, and optimisation problems.
- Knowledge Representation & Reasoning (KRR): Before the neural network boom, this was AI’s backbone. It lets systems represent facts, rules, and logical relationships — still crucial for explainable systems and domain-specific reasoning.
Application Areas
This is where AI shows up in real products and services.
- Natural Language Processing (NLP): Anything to do with understanding or generating human language — from chatbots to translation tools to document summarisation.
- Computer Vision: Making sense of images and video. Used in everything from automated inspections to self-checkout systems.
- Robotics: When you embed intelligence into physical machines. Often overlooked in pure digital discussions, but hugely important in logistics, healthcare, and industrial settings.
Emerging Paradigms
LLMs and Generative AI might look like just new tools — and technically, they are. But their influence runs deeper. They’re reshaping how we build systems, design interfaces, and even organise teams. That’s why I call them “emerging paradigms”: they’re changing the game, not just the gear.
- Large Language Models (LLMs): Massive transformer-based models like GPT or Claude that are trained on huge datasets to generate human-like language. They’re not “intelligent” in the way people are, but they’re incredibly capable in the right context.
- Generative AI: This goes beyond language. These models create images, audio, even video. They’re opening up use cases in design, content creation, and simulation.
Cross-Cutting Concerns
Ignore these at your peril. As AI systems touch more business processes, these concerns aren’t optional.
- Explainability: Can you understand why the AI made a decision? Can your auditors? Can your customer?
- Fairness: Is the model biased? Are you testing for that? How are you mitigating unintended harm?
- Ethics: Just because you can deploy a model doesn’t mean you should. AI raises tricky questions about privacy, autonomy, accountability and sustainability.
One User Action, Four Layers – A Workflow
Let’s say I ask ChatGPT to create an image of me explaining the AI Stack on a whiteboard. That single request cuts through every layer of the AI stack. It goes something like this …
Step 1 – I enter a prompt:
I ask ChatGPT to “create an image of me explaining the AI Stack on a whiteboard.” This is the user action that kicks everything off.
Step 2 – Language is parsed (Application Area: NLP)
Natural Language Processing interprets my intent — identifying key concepts (me, whiteboard, AI Stack), the style (image), and the desired composition.
Step 3 – The model decides how to respond (Foundational Techniques)
Machine learning and deep learning models:
- Break down the prompt into tokens
- Infer likely visual elements
- Assemble internal representations of what the image might include
Step 4 – Image is generated (Emerging Paradigm: Generative AI)
A generative model (like a diffusion model) creates a new image from scratch — not by copying, but by synthesising a visual output that aligns with the prompt and training data.
Step 5 – Output is evaluated (Cross-Cutting Concerns)
The system checks the result for:
- Safety – Is there harmful or inappropriate content?
- Bias – Does the output reflect unintended stereotypes?
- Explainability – Could the system justify the result if asked?
- Accountability – Is the process logged, traceable, and reversible?
Step 6 – I see the image. I smile. Or (more often) I tweak the prompt and try again.
The loop repeats — and each iteration travels the same layered path.
All that complexity — abstracted behind a single line of text. That’s the magic and the risk of modern AI. It looks simple. It isn’t.
Final Word
AI is not unknowable. It’s layered, structured, and increasingly accessible — if you know where to look. Understanding the stack isn’t just academic. It’s what lets you spot the value, ask the right questions, and challenge the nonsense. And there is a lot of nonsense. You don’t need to be a data scientist. But you do need to be fluent enough to tell the difference between a good answer and hallucination. Because more and more, AI will be in your tools, your roadmap, and your team’s decisions.
It’s not magic. It’s not hype. It’s just tech — with a few more layers.
Image generated by ChatGPT