Layoffs are the lazy answer to AI
Written by Aija Bärlund
When AI makes your organisation 15% more efficient, someone decides what happens to that capacity. In most companies, nobody does. It dissolves into everyday workload, or it becomes a cost-cutting target by default.
I just returned from a study trip to Poland (24-27 September 2026), where one of the main themes was AI and workforce change. The most important lesson was surprisingly simple: when AI frees up 15% of work capacity, people in Poland are not laid off. The capacity is invested in business development and in creating new businesses.
The question is not how many jobs AI will take. It is who owns the capacity that is freed up, and where it goes. That is a decision for owners, boards, and executive teams.
Why Poland thinks differently
Part of the answer is the market. Unemployment in Poland is 3.9%, in Finland 10.5%, and the EU average is about 6.1%. When skilled people are scarce, you don't let them go the moment a task is automated. You find something better for them to do.
Demographics push in the same direction, slowly but permanently. By the end of the century, the share of working-age people in Europe will fall from roughly 58% to 50%. In Poland, the share of people over 65 has grown from 13% to 21% in just 20 years. When labour supply narrows structurally, freed-up capacity is not a saving to be banked. It is an opportunity to be used.
There is no single European labour market. There are local markets where the same technologies meet very different conditions. What works in Warsaw will not automatically work in Helsinki. But the question travels well: is our default response to freed-up capacity a deliberate choice, or a reflex?
Freed-up capacity needs new skills
Moving people into new work only succeeds if skills follow. The employees need reskilling.
The skills of the future combine three things: 1) domain and process expertise, 2) AI and data fluency, and 3) human skills such as critical thinking, collaboration, communication, and handling uncertainty. AQ, IQ, and EQ are all needed: algorithm quotient - understanding what algorithms can do and where they fail, is as important as intelligence quotient and emotional quotient.
Boards should also note the risk. HR and risk professionals fear AI will be deployed without adequate training. Capacity that isn't deliberately directed toward new work and new skills doesn't wait patiently. It vanishes.
Five decisions for the board and executive team
These are not tool decisions. They are strategic and operational ones.
Which tasks and decisions will change? Start with task analysis, not job titles.
Who owns the freed-up capacity, and where does it go? Reskilling, expansion, new businesses, or cost savings? This is the most important decision, and it has to be made before the capacity appears.
How do we protect the capability pipeline? If entry-level tasks are automated, how do people grow into experts?
How is supervisory work redefined? Where does human accountability end and AI begin?
Does our location model still make sense? Does it match the capabilities and business we need, not only the cost we want?
The real test
AI creates capacity. Whether it becomes savings or new business is decided in the boardroom. Poland's lesson was clear: when skilled people are scarce, the freed-up 15% is a growth opportunity. Seizing it takes task analysis, workforce architecture, investment in data, and strong leadership.
So here is the question for every board: when AI frees up 15% of our capacity, do we already know what we will use it for? And do we have people who can lead this change?
If you need help in building AI-savvy boards or leadership teams, I am here to help.
Aija Bärlund|Partner|aija.barlund@chief.fi| +358 40 681 0245
