I get the question in almost every leadership team and communications department I work with: what actually is AI? It is often asked a little apologetically – as if one ought to know by now. One ought not. The field moves fast, the words are used imprecisely, and most explanations fall into one of two ditches: the technical one, which drowns in neural networks, and the grandiose one, which promises a revolution every quarter.

This article is my attempt at a third way – a calm, basic explanation for people who will not be building artificial intelligence, but leading and communicating with it. I start with the word, move through the three layers every leader should be able to tell apart, and finish where the definitions stop being helpful: with the work.

The word AI is older than most people think

AI stands for artificial intelligence – in Danish, kunstig intelligens. The term was coined in 1955, when the mathematician John McCarthy, together with Marvin Minsky, Nathaniel Rochester and Claude Shannon, sought funding for a summer workshop at Dartmouth College. The 1956 conference is today regarded as the birth of the field, and the proposal is still worth reading: the basic assumption was that every aspect of learning and intelligence can, in principle, be described so precisely that a machine can simulate it. In other words, the ambition is 70 years old – what is new is that the technology has begun to deliver on it.

In Danish, the two terms live side by side. Kunstig intelligens is the formal word used in legislation and strategies; AI is what we say in everyday speech. A Danish abbreviation never gained a foothold – unlike in German, where KI is the common term – and that says something important: the conversation about artificial intelligence is conducted on English terms, with English words and English examples. That is precisely why precise explanations are needed in Danish.

The three layers a leader needs to know

Artificial intelligence is not one thing, but a field with several layers stacked on top of each other. Three of them are essential to be able to tell apart if you are to make decisions about AI in an organisation.

The first layer is classic AI and machine learning. Machine learning is software that learns patterns from data rather than following rules a human has written in advance. It is the artificial intelligence we have lived with for decades without calling it anything at all: the spam filter in your inbox has learned from millions of emails what unwanted mail looks like, and therefore filters out most of it before you even see it.

The second layer is generative AI. Generative AI is software that produces new content – text, images, audio and video – by calculating what probably comes next. It is this layer that, since 2022, has moved artificial intelligence from the engine room to the boardroom. When ChatGPT writes a draft of a difficult email in ten seconds, it is generative AI at work.

The third layer is AI agents. An AI agent is software that does not just answer questions, but carries out tasks itself in several steps – it plans, acts and adjusts along the way. Where generative AI waits for your next message, an agent can research a topic, compare sources and deliver a complete memo while you do something else. This layer is the youngest and least mature of the three, but also the one that will change the most workflows.

What AI is not

Misconceptions about artificial intelligence are at least as widespread as the technology itself. Three of them are so persistent that they deserve to be properly laid to rest.

The first: the AI does not "know" anything. A language model does not contain a list of true statements it can look up. It calculates which words are likely to follow each other, based on patterns in vast amounts of text. Most of the time the calculation is right; sometimes it produces errors – delivered in the same confident tone as everything else. That is not a defect in the machine. It is how the machine works.

The second: generative AI is not a search engine. A search engine finds existing documents and shows them to you; a language model generates a new answer every time it is asked. Several tools now combine the two – they search first and phrase the answer afterwards – but the basic mechanism is still production, not look-up. Ask for a source, and you may get one that sounds right and has never existed.

The third: artificial intelligence has no intentions. Phrases such as "the AI wants us to..." or "the model is trying to..." are metaphors, and they are not innocent. Software has neither goals, interests nor will – the goals that exist in a system have been put there by people. That distinction is crucial when responsibility has to be assigned: responsibility always lies with people and organisations, never with the model.

AI is already part of everyday life

Much of the conversation about artificial intelligence is about the future. It overlooks how much has already happened. When your email suggests the rest of the sentence, a language model is calculating probable continuations. When the meeting ends and the minutes are ready with decisions and next steps, artificial intelligence has transcribed the audio and summarised the content. When your phone removes a passer-by from your holiday photo, or when a document in Polish becomes readable Danish in a second, it is the same underlying technology.

The point is not that all of this is impressive. The point is that artificial intelligence is already infrastructure – it is woven into the tools you already use. The question "should we use AI?" is therefore outdated long before it is asked. Most organisations already use artificial intelligence every day; they just have not decided how.

Benefits and limitations in an honest account

If you are to lead with artificial intelligence, you need to see both columns of the ledger. The benefits first, without hyperbole:

  • Speed: drafts, summaries and translations that used to take hours now take minutes.
  • Availability: sparring on text, figures and ideas is available around the clock – also for those without an experienced colleague beside them.
  • Pattern recognition: artificial intelligence finds connections in volumes of data that no human can take in.
  • Lower barriers: more people can now write, analyse, code and design at a level that used to require specialists.
  • Time freed up: routine tasks take up less space, so time can be spent on what requires judgement.

And the limitations, just as soberly:

  • Errors with confidence: the models sometimes produce wrong answers in a convincing form, and the errors are hard to spot without expertise.
  • No accountability: artificial intelligence cannot answer for anything – responsibility for content and decisions stays with the human.
  • Bias: the models have learned from existing data and inherit the skews that the data contains.
  • Confidentiality: what is shared with an AI tool leaves the building – without clear agreements on data handling, that is a real risk.
  • Mediocrity by default: the models pull towards the average of what they have seen. Without demands and editing, you get the competent but characterless answer.

Where artificial intelligence is heading

Updated July 2026. Predictions about artificial intelligence age quickly, so I will limit myself to three movements that are already under way. The first is the shift from answers to action: AI agents that carry out tasks themselves in several steps are moving from demonstration to operation – and they raise new questions about control and accountability. The second is regulation: the EU's AI regulation, the EU AI Act, entered into force in August 2024, and its requirements are being phased in over these years; the rules for general-purpose AI models already apply, and the requirements for high-risk systems will follow. For organisations in Europe, legislation has thus gone from a future topic to an operating condition. The third is multimodality: the same models increasingly work with text, image, audio and video at once, and the boundaries between writing tools, image tools and meeting tools are becoming correspondingly less sharp.

How far each movement will go, I dare not put a year on. The direction, on the other hand, seems clear to me: artificial intelligence is becoming less a separate tool and more a layer in everything else.

What it means for communication and leadership

That leaves the question the definitions do not answer: what do you do with all of this as a leader or as someone responsible for communication?

My answer is that artificial intelligence is a leadership task and a language task before it is a technical one. A leadership task, because someone has to decide which tasks AI should take over, which it should support, and which should stay with people – that is the core of AI strategy and AI leadership. A language task, because the value of generative AI depends on the ability to formulate precisely what you want – I have described that craft in my guide to the good prompt. Then there is the framework: who may use which tools for which data is no longer a matter of taste, but regulated – a guide to the EU AI Act is a reasonable place to start. And if a whole organisation is to come along, it often begins with a shared understanding – that is where a keynote on artificial intelligence can open the conversation.

Perhaps the most useful answer to "what is AI?" is therefore not a definition, but three questions you can take home: Which of your tasks actually require human judgement – and which have simply always been given it? Who in the organisation can explain the difference between machine learning, generative AI and agents today – and who ought to be able to? And if artificial intelligence is already infrastructure, who is responsible for making sure it is used thoughtfully in your organisation? The definition of AI will change again. Those questions will remain.

Sources

  • McCarthy, J., Minsky, M. L., Rochester, N. & Shannon, C. E. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.
  • Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433–460.
  • Regulation (EU) 2024/1689 of the European Parliament and of the Council (the EU AI Act). EUR-Lex.