What is AI? Explained for business, in plain language

You hear a lot of words for AI used interchangeably: algorithms, machine learning, deep learning, LLMs, agents. They fit inside each other like boxes within boxes.

• AI is the outer box: all software that does tasks that normally take human thinking.
• Machine learning sits inside it: software that learns patterns from examples, instead of from rules that someone wrote down.
• Deep learning is a form of machine learning using large neural networks with many layers. It's behind speech recognition, image recognition and today's language models.
• Generative AI is a recent branch of that: AI that creates something new. The language models (LLMs) behind ChatGPT and Claude belong here.
• AI agents use generative AI to take steps in your systems by themselves: reading an email, looking up an order, preparing a reply.

And then there's software that isn't AI: ordinary automation with fixed rules. 'When an order comes in, send a confirmation.' In that sense an algorithm is simply a recipe, a fixed series of steps. That works fine as long as everything is predictable. In practice, rules and AI often work together: the rules set the route, and AI reads the free text along the way.

For a business, the key distinction is between AI that predicts and AI that creates. Machine learning in the classic sense predicts or classifies: is this spam, how much will we sell next month? Generative AI makes something new: an email, a summary, a first draft of a quote. How that works is explained in what is generative AI?

Below are the six terms side by side, with where to read more.

To be fair: AI is powerful, but it isn't a colleague you trust blindly. Five limits to know before you start:

• It doesn't know anything for certain. Generative AI predicts what probably fits. That's why a wrong answer sounds just as convincing as a right one. Sometimes it makes up a source or a number. That's called a hallucination.
• It doesn't know your business. It knows a lot about the world, but nothing about your customers, prices and agreements. Whatever it needs to know, you give it.
• It doesn't take responsibility. A decision about a customer, a price or an employee is made by a person. AI prepares.
• It struggles with what it has never seen. An exception that appears in no example at all, or a customer who is upset about something that isn't in the email. That takes experience and people skills.
• It's not a calculator or a database. Exact calculations and current facts don't automatically come out right. Let the numbers come from your own systems.

Research also shows that AI on the wrong task can work against you. In a study of 758 BCG consultants, consultants using GPT-4 worked 25.1% faster on tasks within AI's capabilities. On a task outside them, they were 19 percentage points less likely to get the right answer than colleagues without AI (Dell'Acqua et al., Organization Science, 2026). AI makes good work faster, but it also produces convincing wrong work if you put it on the wrong task.

That's why we always work with a human in the loop: AI does the groundwork, a colleague checks and decides where it matters. That isn't a brake, it's how you build trust. Start with every proposal checked, and move the line step by step as it proves itself. More in human in the loop.

Start with the work, not the tool. You only know which tool to choose once you know which work you want to change. After that there are roughly four routes. They don't exclude each other: most organizations combine them.

1. Learn: understand what AI can do

You can only make good choices once you know what AI can do and where it goes wrong. Start with this knowledge base and the AI glossary. Want to bring your whole team to the same level in one go? Then a talk or workshop on your own work does the job: a talk for a large group costs € 2,450, a hands-on workshop € 4,500 per half day. Since 2 February 2025, the AI Act has also asked you to work on AI literacy.

2. Try it yourself with your own team

Choose a business version of ChatGPT, Claude or Copilot, agree on a few house rules and practise on real work. Learn good prompting and then build your own AI assistant that knows your tone and way of working. Which tool fits is covered in ChatGPT, Copilot or Claude. With the free AI self-assessment you see in two minutes where you stand.

3. Get advice: work out what it delivers

If you want to know which of your processes are suitable and what they deliver, you work it out per process. You can do that yourself, with an AI business case. Or you let us do it in the AI Quick Scan: one day with you on site, and within a week a baseline, a business case per opportunity and a roadmap. The Quick Scan costs € 6,000.

4. Have it built: AI in your processes

Workflows and agents that really take over work need building, integrations with your systems and maintenance. After the Quick Scan, we do that together in the AI Partnership: a dedicated team that builds, trains and shows every month what it delivers. Package S, M or L costs € 4,500, € 7,500 or € 12,000 a month, for a minimum of twelve months. We only start when the business case pays back at least twice in the first year. What gets built is shown under AI automation and AI agents.

All amounts exclude VAT. An overview of all costs is on what does AI cost?

When people look at AI seriously for the first time, they often run into the same assumptions. These are the five we hear most:

• 'AI is something new.' AI has been around for decades: in spam filters, product recommendations and fraud detection. What's new is that AI now reads and writes in plain language. That's why, for the first time, it reaches everyday office work.
• 'What AI says is true.' AI predicts what probably fits, and that's not the same as what's true. Have it work with your own sources and check what matters. Read AI hallucinations.
• 'Once we have licences, we're done.' Rolled out isn't the same as used. 70% of SME owners say they use AI, but 47% don't yet know how to use it effectively (Exact, 2026). The difference is training on your own work, house rules and an owner per process. See AI adoption.
• 'AI is an IT project.' In our projects, AI rarely fails on the technology and often on the work floor: nobody has time, nobody knows what's allowed. So bring the people who do the work to the table. More in why AI projects fail.
• 'First, choose the right tool.' Start with the work: where is information retyped, searched for or the same document made over and over? That's the first principle of our APO method: process first, technology later. An overview of the kinds of tools is in AI tools for business.