AI can help us notice patterns in our lives. But can it recognize the moment when a seemingly absurd idea becomes the right one?
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The lesson of the salceson
Some of the most useful lessons about strategy do not begin with a strategy deck. In this conversation, Bartosz Jędrzejczak begins with salceson — a traditional Polish cold cut — and a meeting that happened roughly two decades ago.
At the time, Bartosz was helping build an innovative centre for people with disabilities in Mielnica, a small place near Lake Gopło. The project was designed to make it possible for adults with disabilities to live and work locally rather than being forced to leave their communities. It was an ambitious social project, and at one point it needed electrical installations, a new connection to the power grid, and practical support from an experienced engineering company.
Bartosz met a businessman who lived in the United States and visited Poland occasionally. Instead of leading with the project’s mission, its funding needs, or a polished presentation, Bartosz invited him to try the best salceson in the area. They ate together at the construction site. The businessman looked around, asked what was needed, and eventually offered to send engineers and a team to install the necessary systems.
The story became a lasting metaphor. Bartosz had not predicted the exact conversation. He had listened for something the other person might genuinely care about, rather than forcing the conversation toward what he himself wanted to say. The salceson was not a sales tactic in the narrow sense. It was an opening: a small, human detail that created the conditions for trust and curiosity.
The same pattern appeared in other stories from the conversation: an unconventional welcome for a foundation leader, an unexpected visual joke that helped unlock funding for training apartments, and a seemingly irrational decision to distribute rainwear in towns expecting a papal visit. In each case, the decisive move looked strange when judged only by the obvious data. It made sense when someone noticed the human context around the data.
Why intuition is not the opposite of strategy
Intuition is often presented as the enemy of analysis. The discussion suggests something more nuanced. Analysis can create a useful map: interests, strengths, experience, goals, risks, and constraints. It can help us understand the majority of situations. But it may not reveal the small detail that changes the direction of a conversation.
That detail may be a joke, a pause, an unusual association, or an idea that initially sounds absurd. It may be the part of the conversation that someone else would dismiss as a digression. In Bartosz’s examples, these details became signals. They did not replace preparation; they allowed preparation to connect with the person standing on the other side of the table.
This matters in business, social projects, education, and personal development. A proposal can be technically sound and still fail to create energy. A person can have a clear list of competencies and still be moving toward work that does not fit them. A conversation can follow a well-designed script and miss the sentence that reveals what really matters.
The challenge is that intuition does not arrive with a spreadsheet label. It is built from experience, observation, and a willingness to notice what does not yet fit the model. It also requires trust in one’s own perception. Many people can identify a quiet internal signal and then immediately talk themselves out of it because the signal does not look rational enough.
Education should not make everyone average
The conversation then moves from intuition to education. One of its strongest arguments is that education often spends too much energy helping people become moderately competent at things they are fundamentally bad at.
That is a familiar pattern. A learner struggles with a subject, receives more exercises in the same subject, and is measured by how close they can come to an average standard. Meanwhile, their strongest abilities may receive less attention because those abilities already look natural. The result is an education system that can confuse improvement with conformity.
Bartosz points toward approaches that begin with strengths: identifying what a person is naturally drawn to, what they do well, what experiences energize them, and what unusual combinations might become meaningful. The most successful development stories he describes did not come from the first obvious classification. They emerged from later layers — details that appeared in the background of a conversation and did not fit a standard template.
This does not mean that standards, fundamentals, or discipline are unnecessary. It means that they should not be the whole definition of development. A useful system should help a person build enough competence to function, then create room for them to become distinctive.
Where AI helps — and where it still falls short
AI can already play a valuable role in this process. Bartosz describes an experiment in which he fed years of digital notes and meeting records into an AI system and asked it to identify blind spots. An earlier model produced a fairly ordinary answer. Newer models, working with a much larger context, produced a far more useful outside perspective.
The important point is that the model did not discover a mystical new intuition. It surfaced patterns that were already present in the material. The information was visible in the notes, but the person who wrote them could not see it because they were too close to their own story. AI became a mirror and a pattern-finding partner.
That is already powerful. It can organize scattered observations, compare events across years, identify repeated choices, and formulate questions that a person might not think to ask. In education, it could help reveal strengths that are hidden behind a learner’s formal performance. In coaching or tutoring, it could support the first layer of reflection and reduce the amount of repetitive analytical work.
But the limits are equally important. The conversation distinguishes between finding a pattern in explicit data and sensing something that has not yet become explicit. Human work often depends on trust, vulnerability, grief, love, fear, confidence, and the subtle signals that appear in a live relationship. Those experiences are not simply missing fields in a database.
A coach or tutor does not only process what a person says. They notice how the person says it, what they avoid, what changes in their voice, and when a seemingly irrelevant sentence suddenly becomes central. That does not make every human judgment correct, and it does not make technology useless. It does mean that replacing the relationship with a system would change the nature of the work.
The question of trust
Trust is not just a feature that can be added to a product specification. It is something earned through repeated interactions and demonstrated safety. When people share personal information with a coach or tutor, they are not merely submitting data for analysis. They are entering a relationship with expectations about discretion, care, and responsibility.
AI may become a place where some people feel safer than they do with other people, especially if previous relationships with therapists, coaches, institutions, or employers have disappointed them. That possibility should not be dismissed. But it also creates a need for clear choices: what is stored, who can access it, how it is used, and what kind of support a system can honestly provide.
The right question is not whether AI is good or bad in the abstract. It is what kind of help someone is asking for, what risks are acceptable, and where human responsibility must remain visible.
Learning to choose the human element
The conversation closes with an image from Blade Runner: a test designed to expose what is not human, and the possibility that the test may reveal something about the people administering it. The reference points to a larger question. If machines become increasingly capable of recognizing patterns, simulating empathy, and producing convincing advice, how will we decide which parts of life we want to keep deliberately human?
There may be no single answer. Someone may choose a virtual companion for a particular kind of conversation. Someone else may prefer a live musician, a craftsperson, a tutor, or a mentor precisely because the work contains human presence and imperfection. The important thing is awareness: knowing what each option can do, what it cannot do, and what we value enough not to outsource.
The salceson story offers a practical conclusion. The most important move in a conversation may not be the most polished one. It may be the unexpected gesture that lets another person feel seen. AI can help us analyze the record of our lives and notice patterns we missed. It may even help us ask better questions. But the decision to listen, to trust an intuition, to recognize a person, and to take responsibility for what happens next remains a human choice.