Artificial intelligence (AI) is the field of computer science that builds systems able to perform tasks that typically require human intelligence. Think of understanding natural language, recognizing images, predicting outcomes or making decisions. AI works by learning patterns from a large amount of data. You already use it every time a chatbot answers you or a streaming platform recommends what to watch.
What you’ll take from this
This guide covers what everyone should know before using AI at work. It is the same starting point as the certificate program in artificial intelligence at Founderz.
- Explain what AI is in plain words, with a definition that holds up all the way to EU law.
- See how an AI system learns, where its data comes from and what happens between raw data and a prediction.
- Spot examples of AI at work and in everyday life, along with its benefits, risks and ethical concerns.
- Start using AI at work this week with a four-step method and a prompt you can copy.
What is AI? A definition of artificial intelligence
Artificial intelligence is the ability of computer systems to do things that, until recently, only a person could do. That covers problem solving, understanding a sentence, recognizing an image or picking the option most likely to be right. AI systems simulate human intelligence on specific tasks. They solve problems without human thinking, knowledge of the world or cognition.
In easy words, it is software that learns from examples instead of following only the rules a programmer wrote. More good examples make it better at one job. Despite the old idea of an artificial brain, it does not think like a human.
The legal definition in Europe
Article 3 of the EU AI Act (Regulation (EU) 2024/1689) defines an AI system as a machine-based system designed to operate with varying levels of autonomy. It may adapt after deployment. It infers, from the input it receives, how to generate outputs such as predictions, content, recommendations or decisions that can influence physical or virtual environments.
The key word is “infers.” A computer program that only follows fixed rules written by a person falls outside that definition.
A short history of artificial intelligence
The field starts with a question. In his 1950 paper “Computing Machinery and Intelligence,” Alan Turing asked whether machines could think and proposed the imitation game, now known as the Turing test.
John McCarthy coined the term “artificial intelligence” in 1955, according to Stanford HAI. He introduced it at the 1956 Dartmouth conference, which many scientists treat as the birth of the field.
For decades the field of AI lived in research labs and science fiction, while AI research moved from rules to machine learning. Then on November 30, 2022, OpenAI launched ChatGPT. Millions of people started talking to a chatbot in their browser, and generative AI became one of the AI concepts everyone uses.
How does artificial intelligence work?
Artificial intelligence works through four key components: data, an algorithm that learns from it, a model that stores what it learned and the computing power to run it. Instead of coding every rule by hand, you give the system examples and the machine learning algorithms find the patterns themselves.
Algorithms are used at every step, from cleaning the data to scoring each prediction. More data of better quality usually means better predictions, although it depends on the task.
Some artificial intelligence systems also follow rules written by people, which is how many older expert systems worked. Modern AI models get most of their behavior from patterns in data instead, and then make decisions based on those patterns.
What are the key components of an AI system?
Almost every AI system, from a spam filter to a virtual assistant, rests on the same four components. The table shows what each one does and a simple example you will recognize from office work.
| Component | What it is | Example |
|---|---|---|
| Data | The data set the system learns from | Thousands of emails already sorted by topic |
| Algorithm | The mathematical method that looks for patterns | A machine learning algorithm |
| Model | The result of training, which applies what it learned to new cases | The classifier that labels each new email |
| Computing | The computer or cloud computing service that trains and runs the model | Servers with graphics processing units (GPUs) |
How does artificial intelligence learn, and where does its data come from?
AI learns by comparing many examples until it finds the regularities that explain them. Its data comes from whoever trains it: public text and web pages, labeled images, a company’s own records such as sales, tickets or emails, and sensors. The quality of that data shapes the quality of the output, and its biases too.
There are three basic ways to learn. In supervised learning every example comes with the right answer. In unsupervised learning the system groups data without labels, for example customers who buy in similar ways. In reinforcement learning it tries actions and gets a reward when it gets them right, the way a game-playing system learns which moves win.
Before using a model, data science teams split the data into training, validation and test data sets, which shows whether it works on cases it has never seen. The goal of machine learning is to perform well on new data, not just on the examples it saw.
Machine learning, deep learning and neural networks
This ability to learn from experience is called machine learning, so AI and machine learning are not the same thing: one is a subset of AI. Deep learning goes one level deeper and uses an artificial neural network with many layers. A neural network is a mathematical model loosely inspired by the human brain: layers of nodes that adjust their connections with every example.
Deep learning powers natural language processing, computer vision and speech recognition. Analyzing written language is the most common business use of AI in Europe: 11.8% of EU enterprises did it in 2025, according to Eurostat. For more depth, read our guide to deep learning in AI.
An office example: how an AI system learns to sort emails
Say you want artificial intelligence to sort the emails that land in your customer service inbox. An algorithm would learn the job like this.
- Gather data: a few thousand past emails already labeled “billing,” “issue” or “cancellation.”
- Train the model: the algorithm compares words, phrases and context in each group and finds patterns.
- Test it on emails the model has never seen and measure how many it gets right.
- Put it to work: each new email arrives with a label and a confidence level, and a person reviews the unclear ones.
The model does not understand an angry customer the way you do; it calculates the most likely label from what it has seen. Good AI projects keep a human in the loop for edge cases, while the algorithms work behind the scenes on the routine ones.
What are the main types of AI? From narrow AI to superintelligence
There are different types of AI. By level of intelligence, each falls into one of three groups. Weak AI, also called narrow AI, handles one specific task, like the spam filter in your inbox or the recommender system of an online store, and it can solve complex problems inside that one task. All the AI that exists today is narrow AI.
Strong AI, or artificial general intelligence (AGI), would learn any intellectual task the way a person does. It remains a research goal. Superintelligence is a hypothesis. For the full map of the types of artificial intelligence, including the classification by function and by technology, see our guide to types of AI.
What is generative AI?
Generative AI is a form of artificial intelligence that creates new content from a written instruction called a prompt. It can draft text, write code or generate images, audio and video. You can use it without computer programming skills, which is why it changed office work so fast.
Assistants such as Microsoft Copilot, Claude or Gemini run on large language models (LLMs), and training them takes a huge amount of compute. An LLM is trained on huge volumes of text and predicts the most likely next token, which can be a word or a piece of a word. That is why these tools generate human language so fluently, and why they sometimes invent facts just as fluently.
How is AI used? Applications of AI at work and in everyday life
AI is used to automate repetitive work, analyze large amounts of data, predict outcomes and support content creation. You already carry it with you. Face recognition on your phone, the keyboard that suggests your next word and the map app that avoids traffic are all examples.
Business adoption is now mainstream. The Stanford AI Index 2025 reports that 78% of surveyed respondents said their organizations used AI in 2024, up from 55% in 2023. The share regularly using generative AI rose from 33% to 71%.
In Europe, Eurostat reports that 20.0% of EU enterprises with 10 or more employees used AI technologies in 2025, up from 13.5% in 2024. Among individuals, 32.7% of people aged 16 to 74 in the EU used generative AI tools in 2025.
Here is how the work splits between you and the machine where AI applications are most common.
| Area | What AI does | What you still do |
|---|---|---|
| Customer service | Chatbots and virtual assistants answer common questions and route requests | Handle complex cases and own the relationship |
| Marketing and sales | Recommendation systems, proposal drafts and personalized messages | Decide the message and approve what goes out |
| Finance | Data analysis, anomaly detection and forecasts | Interpret the results and make decisions |
| Health care | Support for medical imaging review and admin tasks such as scheduling | Diagnose, treat and take responsibility for the patient |
| Transport | Driver assistance, route planning and self-driving cars in testing | Supervise and decide when something unexpected happens |
What is the difference between AI and ChatGPT?
Artificial intelligence is the whole field, and ChatGPT is one tool inside it. ChatGPT is a generative AI assistant from OpenAI that runs on a large language model, like many other assistants.
The difference matters because artificial intelligence often works without talking to you. The system that flags fraud on your card, the one that steers a self-driving car and the one that ranks search results are artificial intelligence too. When someone says “we have AI,” ask which system, for which task and with what data.
What are the benefits and risks of artificial intelligence?
The main benefit of AI is that it saves time on repetitive tasks and finds patterns in big data that would take a person weeks to review. The main risk is that it can be confidently wrong. AI can invent facts, repeat biases from its training data and expose information if you are careless about what you share.
| Benefits | Risks |
|---|---|
| Automation frees time for higher-value work, and AI can help teams work more efficiently | Makes mistakes that look like facts, so every output needs review |
| Analyzes thousands of records in minutes | Reproduces biases in its training data |
| Personalizes services and answers at scale | Creates privacy risks if you paste personal or confidential data |
| Runs nonstop on tasks such as filtering or sorting | Its reasoning is not always explainable, which makes decisions hard to audit |
What are the ethical concerns of AI?
The biggest ethical concerns surrounding the use of AI are biased outputs, misuse of personal data, misinformation such as realistic fake images, and decisions nobody can explain. AI also changes which tasks people do at work, so employment and skills are part of the debate.
A biased output can be as simple as a hiring model that ranks candidates lower because past hires in its data looked different. The EU AI Act sorts systems by risk level. Some uses, such as social scoring, are banned outright. The European Commission’s AI framework explains how. In day-to-day work, AI governance starts with knowing what data you should never share and which decisions you should never hand over.
Our guide to the ethics of artificial intelligence covers this in depth.
How do you use AI tools at work?
The fastest way in is to pick one task you repeat every week. Try it with an assistant you already have, such as Microsoft Copilot if your company runs on Microsoft 365. These four steps avoid the most common beginner mistakes and build real AI literacy.
- Choose a small, measurable task: summarize a report, prepare a meeting agenda or write a first email draft.
- Give context: who you are, who the output is for and what format you need.
- Check every fact and figure before you use the answer, and never paste confidential data into tools your company has not approved.
- Compare how long the task took before and after. If you are not saving time, adjust the prompt or pick another task.
Here is the prompt to copy for step two. Replace the brackets with your real case before you paste it into your assistant.
Act as an assistant to a [your role] at a [industry] company. I need [specific task, for example: summarize this 10-page report] for [audience]. Give it to me as [format: 5 bullet points, a table, a 150-word email]. If you are missing information to do this well, ask me before answering. Flag any fact you are not sure about.
Save the version that works best. A good base prompt spares you from starting from scratch every time.
Frequently asked questions
These are the questions about artificial intelligence that people search for most, each answered in a few lines.
What is artificial intelligence in easy words?
AI is computer software that learns from examples to do tasks that normally need a human, like answering questions, sorting emails or spotting patterns in numbers. It calculates the most likely right answer based on the data it was trained on, so it can be fast and still be wrong.
How do you explain AI to an older person?
Compare it to an assistant who has read millions of letters and can guess what comes next. Show it working on something familiar, such as a phone that turns speech into text or a map that suggests a faster route. Then explain that it can make mistakes, so its answers need checking.
Who invented artificial intelligence?
No single person did. Alan Turing asked in 1950 whether machines could think. John McCarthy coined the term in 1955 in the proposal for the Dartmouth conference, written with Marvin Minsky, Nathaniel Rochester and Claude Shannon. Thousands of researchers have built the field since then.
Can AI think or feel?
No. Today’s AI algorithms calculate probabilities from patterns in their data. It has no consciousness, feelings or intentions, and no understanding of the world the way you have. That is why it can write a flawless paragraph around an invented fact, and why it needs human review.
Is AI the same as machine learning?
Not quite. Machine learning is the part of AI that learns from data instead of following hand-written rules. Almost all the AI you use today relies on it, but some AI techniques, such as expert systems, do not learn from examples.
Will AI take over the world?
There is no evidence that today’s AI can act on its own goals. All current systems are narrow AI built for specific tasks, and people decide how they are used, whether in software or in a robot. The real risks today are errors, bias and misuse, which is why governance and regulation such as the EU AI Act matter.
What is the future of AI?
Nobody can predict it with precision, but two trends are measurable. More companies adopt it every year, according to Eurostat and the Stanford AI Index, and regulation such as the EU AI Act sets which uses are allowed. In the short term, expect it to be built into the work tools you already use.
Further reading
To learn more about AI from the sources, start here.
- AI Act definitions, European Commission AI Act Service Desk
- The Turing Test, Stanford Encyclopedia of Philosophy
- Artificial intelligence, Wikipedia
Understanding AI is the first step; applying it with judgment to your own work is the next. The Certificate Program in Artificial Intelligence and Innovation teaches you to work with ChatGPT, Microsoft 365 Copilot and Copilot Studio. You also learn to build automations and AI agents and to lead responsible AI in your organization. It is fully online, takes 3 to 4 months, was developed in collaboration with Microsoft and ends with the Microsoft + Founderz Certification, with official Microsoft badges along the way.
