The four types of AI users

Which AI user are you?

Not everyone uses AI in the same way. Some people just ask ChatGPT for travel tips, while others are building the next breakthrough model from scratch. The reality is that people interact with artificial intelligence across a wide spectrum of complexity and purpose. But there’s a big difference between someone translating a menu with Google Translate and someone developing AI systems that could transform entire industries. 

These four categories show the distinct roles different groups play in this rapidly changing field, from simple consumption to complex creation. 

1. General User

Focus: Uses ready-made AI tools 

General users are people who interact with AI tools that work right out of the box, no coding required and no computer science degree needed. They’re the ones asking ChatGPT questions, using Google Translate, or experimenting with AI image generators. 

The popularity of ChatGPT has led to an explosion of AI-powered apps and services. Companies are racing to add AI features to everything from email assistants to photo editors, and businesses are building their own AI tools to help employees work faster and smarter. 

But most general users don’t really know how AI works. They might not realise that when ChatGPT writes a response, it’s essentially picking the next likely word based on patterns it learned from massive amounts of data. It’s not “thinking” the way humans do. 

This gap in understanding can be risky. When AI feels human-like, users might trust it too much, following bad advice or believing false information (also called “hallucinations”) without question. AI tools can be incredibly helpful, but they work best when users approach them with a healthy dose of scepticism. 

Example: A traveller planning their vacation with ChatGPT’s help, or a designer using Midjourney to create artwork for their project. 

2. AI Power User

Focus: Customises AI for specific problems 

AI power users are creative problem-solvers who see AI’s potential beyond basic tasks. They don’t need programming skills, but they do have a strong understanding of how AI works and what its limitations are. They’re also experts in their own fields, which helps them find the right opportunities to use AI. 

Think of a university lecturer who creates a personalised GPT assistant to answer student questions, or a consultant who designs a specialised chatbot for each client’s needs. They often use no-code or low-code platforms to build their tool, and quickly become skilled at prompt engineering to get AI to deliver exactly what they want. 

What sets power users apart is their deeper knowledge of AI capabilities. They understand when to trust AI’s output and when to double-check its work. This knowledge lets them push AI tools further than casual users while staying realistic about what’s possible. 

Example: A researcher in digital finance using AI to scan through earnings calls and financial news in real time, looking for signals that could improve inflation forecasts. At the University of Luxembourg, researchers have been doing this with large language models, turning mountains of financial data into sharper economic predictions. 

3. AI Integrator

Focus: Embeds AI into existing systems 

AI integrators are professionals who embed AI into real-world processes. They take workflows – whether it’s a factory production line or a corporate approval system – and figure out where AI can make things run more efficiently. 

These professionals are typically skilled IT specialists with strong domain knowledge in their industry. They work closely with analysts, engineers, and operations teams to identify the right integration points. They might install sensors throughout a manufacturing plant and train AI to anticipate failures through predictive maintenance, or integrate AI at a bottleneck stage of a workflow to optimise resource allocation. 

4. AI Developer

Focus: Builds AI from scratch 

AI developers are the architects who build AI from the ground up. They need solid programming skills in languages like Python and R, plus a strong foundation in mathematics and statistics. They’re experienced with machine learning frameworks and comfortable working with complex datasets. 

While others use existing AI tools, developers create them. They make critical decisions about training data, model architecture, and optimisation strategies. They might spend months perfecting an AI system that personalises product recommendations, constantly refining it to improve both sales performance and customer satisfaction. 

Example: A computer vision researcher creates AI models and trains them to recognise deepfake images and videos to prevent fraud. For example, researchers have been developing advanced AI models to detect deepfakes since 2022, tackling increasingly sophisticated AI-generated content. 

Plus one more: The Observers

Focus: Studies AI’s impact and implications 

There’s also a fifth group that doesn’t fit the user model but plays an equally important role in shaping AI’s future. AI analysts and thinkers are the observers who study how artificial intelligence impacts business and society. 

These individuals combine deep technical knowledge with expertise in business, ethics, law, or sociology. AI analysts help organisations understand how to integrate AI strategically, while AI ethicists and policy advisors examine the broader implications for society. They’re the ones asking the hard questions about bias, transparency, and responsible development. 

Their work requires multiple areas of expertise and often involves collaborative teams of specialists. They need to understand both the technical capabilities of AI and its real-world consequences, from boardroom strategies to societal trust. 

An AI ethicist studies the ethical aspects of AI tools and their applications. For example, an interdisciplinary group of researchers at the University of Luxembourg has been examining how to use AI for Education in a sensible and measured way.