What AI is, and what it is not: a plain-language guide for business leaders

AI has become a boardroom topic, often wrapped in strong claims. A clear, calm view helps leaders make better decisions. Here is a plain-language guide to what current AI tools are, and are not.

What most people mean today

When people say AI now, they usually mean generative AI: tools like chat assistants that write text, summarise documents, translate, answer questions and generate images or code. These tools are built on large models trained on huge amounts of data.

What these tools do well

• Language tasks. Drafting, rewriting, summarising, translating, changing tone.
• Pattern work. Sorting, labelling and extracting information from large volumes of text.
• First drafts and options. Quickly producing several versions for a person to choose from and improve.
• Explaining. Turning something complicated into plain words.

What they do not do well

They can be confidently wrong. These models generate likely-sounding text. They have no built-in sense of when they are making something up. Invented facts, figures and references are a known problem.

They do not know your business. Unless you give them your information, they know only general public material.

They are inconsistent. The same question can produce different answers. For tasks that need exactly the same result every time, traditional software is often better.

They do not take responsibility. A model cannot be accountable for a decision. A person must be.

Useful ways to think about them

Think of a fast, well-read assistant who has never worked in your company, writes well, and occasionally makes things up without noticing. You would give that person clear instructions, relevant material and a review of their work. The same applies here.

Questions leaders can ask

1. What exact task is this for? Vague goals produce vague results.
2. What does it need to know, and do we have it in usable form?
3. Who reviews the output, and how?
4. How will we measure value? Time saved, errors reduced, response time improved.
5. What data goes into it, and where does that data go?

Hype, and how to read it

Be careful with claims of full automation or instant transformation. Real gains tend to come from many specific, modest applications that are well set up. Companies that do the groundwork, clean information, clear processes and trained staff, get the most from the tools.

A good stance

Be curious and be practical. Try tools on safe tasks. Build experience in your team. Keep humans responsible for decisions. That combination avoids both panic and neglect.