What is AI?
AI stands for Artificial Intelligence. It is a broad field within computer science concerned with developing machines that can perform tasks that would normally require human intelligence.
Think of tasks such as:
- Recognising patterns (as in image or speech recognition)
- Making predictions from data (as in predictive maintenance or churn prediction)
- Making decisions (as in a self-driving car or in medical diagnoses)
- Automating processes (such as robotising administrative tasks)
You can recognise classic AI by:
- Rule-based systems (if-this-then-that)
- Machine learning models trained on historical data
- Optimisation algorithms
- Robotic Process Automation (RPA)
Example: an algorithm that processes invoices automatically and picks the right general ledger account based on the past. No creativity, no text, just data in, data out.
What is GenAI?
GenAI stands for Generative AI. It is a specific application within AI in which the technology generates new content: text, images, video, audio, code, presentations and more.
Examples of GenAI:
- ChatGPT writing a text for you
- Midjourney or DALL·E generating an image from a prompt
- Synthesia making a video with an AI avatar
- GitHub Copilot generating code for you while you program
GenAI usually works on the basis of foundation models (such as large language models, LLMs), trained on enormous amounts of data from the internet.
Example: you type “Write an invitation to a birthday party as a poem” and within 5 seconds you get a cheerful poem back. That is GenAI.
What are the main differences?
| Aspect | AI (classic) | GenAI |
|---|---|---|
| Goal | Automating decisions | Generating new content |
| Output | A choice, prediction or action | Text, images, video, code, etc. |
| How it works | Mainly on the basis of rules and data | On the basis of trained foundation models |
| Example | Spam filter, chatbot that classifies questions | ChatGPT, AI that writes a blog post for you |
| Creativity | None | Yes |
| Transparency | Often clear how the model works | Often a black box and less transparent |
Why does this difference matter?
1. Managing expectations
Not all AI is creative or human-like. AI is not automatically “smart” in the sense of a thinking robot. Classic AI can in fact be very dumb, yet lightning fast at making decisions based on data. If you expect GenAI but get a simple classification, you will be disappointed (and vice versa).
2. Field of application
You use AI for different things than GenAI:
- You deploy AI to automate processes, make predictions or support decisions.
- You use GenAI to create content, to brainstorm or to speed up creative processes.
3. Risks and responsibilities
The risks differ. AI decisions have to be explainable (in finance or healthcare, for example). GenAI, on the other hand, can produce unreliable output or hallucinated content. Control and governance work differently.
4. Confusion in the market
Many tools claim to be “AI-powered” when they simply use standard rules. Or they call something AI when it is really about generative models. That leads to misunderstandings in purchasing, implementation and expectations.
Why isn’t GenAI just AI?
Technically, GenAI falls under the larger AI umbrella. But in practice the distinction is crucial:
- GenAI can create, interpret and improvise.
- Classic AI can optimise, decide and automate.
Compare it with transport: a bicycle and an aeroplane are both means of transport, but you use them in completely different ways.
Summary
- AI is a broad field focused on intelligent systems that predict, classify, decide and automate.
- GenAI is a subfield of AI focused on generating new content.
- The difference lies in the output, the applications, how it works and the risks.
- The terms are often used interchangeably, but that leads to confusion and wrong choices.
Finally
As an organisation, it is important to be clear about what you need:
- Want to make processes smarter? Then look at classic AI solutions.
- Want to create content faster, or let customers search large amounts of information themselves? Then GenAI is your route.
Both technologies are powerful, as long as you use them in the right way.