8Apr2026
Podcast: How to Use AI Without Letting It Rot Your Brain
by: Nordic Business Forum
AI can boost your thinking, but it can also replace it completely. In this new podcast episode, we explore how to use AI in a way that strengthens your brain instead of weakening it. Host Olivia Ojala is joined by neuroscientist Mona Moisala and Pactum AI co-founder Kaspar Korjus to discuss what research says about AI’s impact on cognition, memory, and decision-making. When does AI enhance learning, and when does it lead to shallow thinking?
If you prefer reading, under the player is a thorough summary of the podcast discussion:
Artificial intelligence has entered our work lives fast. For many professionals, it already feels like something you cannot ignore if you want to stay relevant. But there is a deeper question that leaders should be asking right now: Are we becoming more intelligent with AI, or just more efficient?
In a Nordic Business Forum podcast discussion, Mona Moisala and Kaspar Korjus explore this from two complementary perspectives. Mona, as a brain health scientist with a PhD in psychology, brings the neuroscience lens, focusing on how AI affects our cognitive abilities. Kaspar, as the co-founder of Pactum AI, brings practical experience from building AI systems inside organizations.
Their shared conclusion is simple but easy to misunderstand: “So really, it’s all about how you use AI.”
When AI Helps Thinking and When It Replaces It
According to Mona, there is currently no strong scientific evidence that AI changes the structure of the brain. But there is growing evidence of something more subtle and more immediate: how AI changes the way we think.
The key concept here is cognitive offloading. This means outsourcing parts of your thinking, memory, or decision-making to external tools. Used well, this is not a problem. In fact, it can improve short-term performance and even increase motivation. But the problems begin when people start relying on AI before they have done any thinking themselves. In those cases, studies show weaker memory, shallower information processing, and reduced critical thinking ability.
Mona summarizes the difference clearly: “You can use it to enhance your cognitive functioning and boost your learning, or you can use it in a way where you’re substituting your thinking.”
This distinction is where most professionals either gain an advantage or slowly lose one.
Think First, Prompt Second
One of the most practical ideas from the discussion is also one of the simplest: “Think first and prompt second. Always go through that initial thinking phase and problem-solving phase by yourself and then take that material to AI and ask it to improve, elevate, or challenge you”, said Mona.
In practice, this means resisting the urge to immediately open AI when you face a task. Instead, spend a few minutes forming your own perspective first. Sketch ideas, define the problem, or outline your thinking, even if it is incomplete. Only after that bring AI into the process.
At that point, AI becomes something very different. It is no longer doing the work for you. It is working with you. It can challenge your ideas, refine your thinking, and push you further than you would go alone. Without that first step, however, something important is lost. The work may get done faster, but your ability to think does not develop.
Why AI Still Needs Human Expertise
Kaspar offers a useful way to understand how AI actually works in organizations. He compares AI agents to junior employees. They are capable, but they are not independent thinkers. They need training, context, and continuous feedback. Without that, they produce inconsistent or even meaningless results.
This becomes especially visible in what both Mona and Kaspar describe as a growing problem: outputs that look convincing but are fundamentally flawed. Kaspar gives a concrete example of receiving a visually impressive framework generated by AI. It looked professional, structured, and insightful. But when he tried to understand it, it made no sense. Even the person who shared it could not explain it. He describes this trap directly:
“If you fall into that trap that you just allow AI agents to do something for you, eventually this is nonsense.”
This is the real risk. Not that AI makes mistakes, but that people stop noticing them. Without expertise, you cannot evaluate AI output. And without evaluation, you cannot trust it.
Efficiency Can Hide a Dangerous Trade-Off
There is no question that AI increases productivity. Tasks that used to take hours or days can now be completed in minutes. Kaspar shares examples from HR, sales, and legal work where AI already saves significant time. This creates a strong incentive to use AI everywhere.
But Mona points out a less visible consequence. When people rely too heavily on AI, they may feel more efficient, but at the same time, less motivated and less engaged in their work. This creates a quiet trade-off. You gain speed, but you risk losing depth. Over time, this can lead to a situation where people are no longer building the expertise that is required to make high-quality decisions. And without experts, organizations become dependent on tools they no longer fully understand.
The Real Opportunity: Better Thinking, Not Less
The discussion does not argue against AI. Quite the opposite. Both Mona and Kaspar see enormous potential in these tools. AI can remove repetitive work, reduce cognitive load, and free up time for more meaningful tasks. But that only works if people actually use that time to think.
If AI replaces low-value execution, the natural next step should be more focus on problem-solving, creativity, and decision-making. In other words, more of the work requires human intelligence. The future advantage will not come from using AI the most. It will come from using it with intention.
A Leadership Responsibility
For leaders, this creates a new kind of responsibility. It is no longer enough to introduce AI tools into the organization. The real challenge is shaping how people use them.
Fear-based adoption does not work. If people feel forced to use AI, they either resist it or use it poorly. Instead, Mona highlights the importance of curiosity and experimentation. People need space to try, share, and learn from each other. They need to see practical examples of how AI improves their work, not just instructions on how to use it. Because the biggest risk is not that people refuse AI, but that they use it without thinking.
