I Was Selling AI Before Most People Knew What It Was
A decade ago I was selling predictive and prescriptive analytics solutions to enterprise clients. Tools like SPSS Modeler — IBM’s data science platform for predicting future outcomes — and CPLEX, the optimisation engine we talked about in Article 6, which solved complex scheduling and logistics problems for supply chain and warehouse operations.
Back then AI wasn't a word that appeared in everyday conversation. It lived in university research departments, specialist software vendors, and the back offices of large corporations with data science teams. It was powerful, it was real, and almost nobody outside of those environments knew it existed.
Fast forward to two years ago. ChatGPT arrived and suddenly everyone was talking about AI.
My initial reaction? Skepticism. I had spent years working with AI tools that were precise, deterministic, and built for specific problems. ChatGPT gave confident answers that were sometimes completely wrong. The hallucinations — the technical term for when AI models generate plausible sounding but entirely false information — bothered me. I knew enough about how these systems worked to be cautious.
Then something changed my mind.
I was preparing for a conference demo and needed to test how an AI assistant would handle tough questions from a live audience. I spent an hour asking it difficult questions, critiquing its answers, pushing back on things it got wrong. And in that session I saw something I hadn't expected — not perfection, but genuine usefulness. The ability to think through a problem with you, draft something in seconds, and improve it based on your feedback.
Shortly after that I started using it for small things. Polishing emails. Sharpening how I communicated complex ideas. Then one day I pasted my Terraform code — the infrastructure code I'd built through trial and error and a lot of googling — into Claude and asked it to review it.
What came back stopped me in my tracks. It critiqued my code the way a senior platform engineer would. It spotted patterns I'd missed, suggested improvements I wouldn't have thought of, and explained why — clearly, patiently, without making me feel like a beginner.
That was the moment I truly understood the power of modern AI.
But First — What Actually is AI?
Artificial Intelligence is the ability of a computer system to perform tasks that would normally require human intelligence.
That sounds abstract so let's make it concrete. Human intelligence involves things like recognising patterns, making predictions, understanding language, solving problems, and learning from experience. AI systems are built to do those same things — not by thinking the way humans think, but by processing enormous amounts of data and finding patterns within it.
There are different types of AI and understanding the difference between them helps everything else make sense. The best way to explain them is through an example most people use every single day — maps and navigation.
Four Types of AI — Explained With Maps
Descriptive Analytics — What Happened?
This is the most basic form. It looks at historical data and tells you what occurred.
On Google Maps this is your journey history — every route you've taken, how long it took, where you stopped. Pure description of past events. No intelligence applied yet, just organised data.
In business this is your monthly sales report, your website traffic dashboard, your bank statement. It tells you what happened but doesn't tell you why or what to do next.
Predictive Analytics — What Will Happen?
This is where it starts getting interesting. Predictive AI looks at historical patterns and uses them to forecast future outcomes.
On Google Maps this is the traffic prediction — “your journey will take 45 minutes, but if you leave in 30 minutes it will only take 28.” It's analysed millions of journeys on that route at that time of day and is predicting what'll happen based on patterns it's learned.
This is the type of AI I was selling with SPSS Modeler a decade ago — predicting customer churn, forecasting demand, identifying which patients were most likely to need hospital readmission. Powerful, specific, and already well established long before ChatGPT existed.
Prescriptive Analytics — What Should I Do?
This goes one step further. It doesn't just predict what'll happen — it recommends the best action to take.
On Google Maps this is the rerouting feature — “there's an accident ahead, I've found a faster route, turn left in 200 metres.” It's predicted the problem and prescribed the solution automatically.
This is where CPLEX lived — not just predicting that a warehouse would run short of stock, but calculating the optimal way to redistribute inventory across the entire supply chain to prevent it. Prescriptive AI makes decisions, not just predictions.
Generative AI — What Can I Create?
This is the newest category and the one that changed everything in the last two years. Generative AI doesn't just analyse existing data — it creates new content. Text, images, code, audio, video.
On Google Maps this is still emerging — but think about the natural language directions that sound like a human giving you instructions rather than a robotic voice reading coordinates.
ChatGPT, Claude, Gemini, GitHub Copilot — these are all generative AI. They've been trained on vast amounts of text and code and can generate new, original responses to almost any question or request. This is the AI most people mean when they say AI today.
AI You’re Already Using Without Realising It
You’ve been using AI in your daily life for years. It just wasn’t called AI in the marketing materials.
Your email spam filter — AI analyses incoming emails and decides which ones are spam based on patterns it’s learned from billions of emails. Every time you mark something as spam you’re training it.
Netflix and Spotify recommendations — AI analyses what you’ve watched or listened to, compares it to millions of other users with similar tastes, and predicts what you’ll enjoy next. The “because you watched” row is a predictive model running in real time.
Your bank’s fraud detection — Every time you make a transaction, AI compares it to your normal spending patterns and flags anything that looks unusual. That text asking you to confirm a purchase abroad? AI spotted something that didn’t fit your pattern.
Why I Went From Skeptical to Convinced
The hallucination problem I mentioned at the start is real and it hasn't gone away entirely. AI models can still generate confident, plausible, completely wrong answers — and that's dangerous if you accept everything they say without thinking critically.
But here's what changed my perspective.
AI isn't a replacement for your judgment. It's an amplifier of your capability.
When I used AI to review my Terraform code it didn't replace my understanding of what the code was supposed to do. It applied a layer of expertise I didn't yet have — the pattern recognition of someone who's reviewed thousands of infrastructure codebases — and gave me feedback I could evaluate with my own knowledge.
When I use it to polish my writing it doesn't replace my ideas or my voice. It helps me communicate them more clearly and efficiently.
The people who get the most out of AI aren't the ones who trust it blindly. They're the ones who bring their own knowledge and judgment to the conversation and use AI to go further, faster than they could alone.
How AI Connects to Cloud and DevOps
If you've been following this series you might be wondering — how does all of this connect to everything we've covered so far?
More directly than you might think.
AI runs on Cloud infrastructure. The models behind ChatGPT, Claude, and every other AI tool run on massive cloud data centres — the same AWS, Azure, and Google Cloud platforms we've been talking about throughout this series. Training a large AI model requires thousands of specialised processors running for weeks. That kind of compute only exists in the cloud.
AI is deployed using containers and Kubernetes. When a company builds an AI powered application — a chatbot, a recommendation engine, a fraud detection system — it's packaged into containers and deployed on Kubernetes clusters, exactly as we covered in Articles 4 and 6.
AI infrastructure is managed with Terraform. The cloud resources that run AI workloads — the GPU clusters, the storage, the networking — are provisioned and managed with the same Infrastructure as Code tools we covered in Article 7.
AI is changing DevOps itself. GitHub Copilot writes code suggestions in real time. AI tools review pull requests and spot bugs before humans do. Pipelines are becoming smarter — able to predict failures before they happen and suggest fixes automatically.
The boundary between AI and DevOps and Cloud is dissolving. They're becoming one interconnected discipline and understanding all three is becoming one of the most valuable skill sets in technology.
AI Isn't Going Away — And That's a Good Thing
A decade ago AI was a specialist tool for specialist problems. Today it's woven into almost every digital product you use. In another decade it'll be as invisible and essential as electricity — present in everything, noticed only when it's absent.
The question isn't whether AI will affect your work and your life. It already has. The question is whether you understand it well enough to use it intentionally, critically, and effectively.
You don't need to become a data scientist or a machine learning engineer. But understanding what AI is, how it works at a high level, and where it's already present in your daily life puts you in a far stronger position — whether you're in technology, business, healthcare, education, or anywhere else.
Quick Recap
AI has existed for decades in specialist forms — predictive analytics, optimisation engines, recommendation systems — long before ChatGPT made it mainstream
There are four types of analytics and AI: descriptive (what happened), predictive (what will happen), prescriptive (what should I do), and generative (what can I create)
You're already using AI every day — in spam filters, Netflix recommendations, bank fraud detection, voice assistants, and search engines
Generative AI like ChatGPT and Claude is powerful but requires critical thinking — it amplifies your capability rather than replacing your judgment
AI runs on Cloud infrastructure, is deployed using containers and Kubernetes, and is managed with Infrastructure as Code — it connects directly to everything in this series
What’s Next?
Next up we're going deeper into Generative AI and Agentic AI — how large language models actually work, what they're good at, where they fall short, and how to use them effectively in your daily work whether you're in technology or not.
We'll also start to talk about something that's changing the industry right now — Agentic AI — AI that doesn't just answer questions but takes actions, makes decisions, and completes complex tasks on your behalf.
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