What is AI for businesses? An explanation without the jargon

80% of professionals in the Icelandic labour market use AI at work, but only 34% have received training from their employer (Viska 2025). If you run a company or a department, you do not need to understand the technology behind the scenes. You need to understand what it does well, what it does badly and what that means for your business. This article explains exactly that, without the jargon.

What is it, actually?

What most people call AI today are so-called language models: programs such as ChatGPT, Claude and Copilot. The simplest way to think about them is this: you describe a task in ordinary words and the program returns text, an analysis or a suggestion.

There is no programming. No command language. You write as if you were emailing a bright assistant: "Summarise the main points of this 40-page report" or "Draft a reply to this complaint, polite but firm".

This is the key change. Before, it took specialists to make computers do new things. Now any employee who can describe a task can get help with it. That is why AI in business is not about the IT department. It is about finance, marketing, customer service, HR and operations.

What language models do well

In day-to-day operations they excel in three areas:

Drafts. what does it mean?: A first draft in seconds instead of hours · examples from operations: Quotes, reply emails, job adverts, meeting invitations

Summaries. what does it mean?: Long material becomes short and clear · examples from operations: Minutes, reports, long email threads, tender documents

Transformation. what does it mean?: The same content in a new form · examples from operations: Excel data into text, Icelandic into English, bullet points into a presentation

Notice the pattern: in every case a person stays at the wheel. The model delivers raw material; the employee evaluates, adjusts and takes responsibility. Used this way, research shows 25 to 56% faster completion of individual tasks (Harvard/BCG 2023). Not ten times better, but measurably and substantially faster. And some workflows, such as manually moving data between systems, can be automated so that they disappear entirely.

What language models do badly

This is where most companies stumble. Language models have three weaknesses that every single employee needs to know:

• They produce material that sounds right but is wrong. The model is designed to write convincing text, not to tell the truth. It can invent figures, sources and legal provisions with complete confidence. Everything that leaves the building needs to be read over by a person.
• They know nothing about your company. Unless you give them context, they know neither your price list, your contracts nor the history with the customer. The quality of the answer depends on the quality of the request.
• They can make experienced people slower. This surprises many: a study showed that experienced specialists who used AI without training became slower, not faster (METR 2025). Untrained use is not neutral; it can be worse than none at all.

The third point also explains why 95% of corporate AI projects deliver no measurable results (MIT NANDA): companies buy the tools but skip the training.

Why is this not just another fad?

A fair question. Managers have seen waves come and go: blockchain, the metaverse, "big data". Three things set this wave apart from the others.

First, the technology is already in use among your people. 80% of professionals already use it at work (Viska 2025). Earlier bubbles required people to change their behaviour. This one spread without anyone asking for it.

Second, there are measured results from real work. A Harvard and BCG field experiment with 758 consultants showed 25 to 56% faster task completion and around 40% higher quality (Harvard/BCG 2023). Blockchain never delivered comparable numbers from ordinary office work.

Third, the barrier to entry is almost nonexistent. It takes no new hardware, no system implementation costing tens of millions of krónur. It takes a subscription, clear rules and training. Yet only 14% of Icelandic companies have an AI policy in place (Statistics Iceland 2025). The gap between what staff use and what management has decided is the risk, and the opportunity.