Give Yourself Permission to Learn Like a Child!
⏱️ Read time: ~3 min
Children would never learn to walk if they were afraid of falling.
They wobble. They fall. They look around, recalibrate, and try again. No child waits for a complete risk assessment before taking the next step.
Yet that is often how organizations are approaching AI (the very organizations whose leaders many of us work with on a daily basis).
Before anyone tests a use case, the questions begin: Is the tool fully compliant? What if the output is wrong? What if someone makes a mistake? What if we cannot predict every consequence? And who is actually accountable in the end?
These are valid questions. But when we demand complete certainty before allowing any movement, we do not create safety. We create paralysis.
And in a world where AI is accelerating, paralysis has a cost.
Europe has already seen the cost of moving too slowly on digital transformation. There is no reason to assume AI will be different if we continue to wait for complete certainty.
Regulation matters. GDPR and the EU AI Act exist for good reasons. But when regulation becomes a reason not to test, learn, or move at all, it becomes part of the problem.
Europe may not build the world’s most powerful LLMs, at least not soon. That does not mean European businesses cannot win with AI.
Our hidden champions hold deep expertise, customer knowledge, and technical excellence. They can use powerful models to strengthen existing advantages and develop new products and services.
This requires access to capable models, sovereignty over data, and investment in infrastructure. But it also requires something harder: learning to make small, safe mistakes, and learning from them.
So how can we start experimenting with AI?
The difference between an AI experiment and a deployment
A useful distinction for leaders is this: an AI experiment is not the same as rolling out an AI system across the organization.
An experiment can be small, time-boxed, with clear evaluation and exit points. The result is not a polished solution. The result is learning.
What worked? What did not? Where did human judgment matter most? What guardrails are needed before expanding?
This is how capable organizations build AI fluency. Not by pretending mistakes will not happen, but by designing spaces where small mistakes are safe, visible, and useful.
For all of us who are working in the coaching and consulting space, this is a powerful place to contribute. We can help clients create those protected learning environments. We can reframe experimentation from “something that might go wrong” to “a disciplined way to find out what works.”
That also means changing the narrative. Instead of: “We cannot make mistakes.” Try: “We make small mistakes early, so we avoid expensive mistakes later.”
Key takeaway
Play like children. Govern like adults.
Do not confuse responsible AI with waiting for perfect answers. Create small, safe experiments, learn quickly, and let that learning guide the next step.
The people who move forward will not be those with the longest list of reasons to wait. They will be those who cultivate a learning mindset: experiment, reflect, and try again. Give yourself permission to learn like a child!
Elena
P.S. Reflection question if you're up for it: Where could you create one low-risk AI experiment this week, simply to learn?
