by Raffaella Aghemo, Lawyer
Artificial Intelligence in business
Artificial intelligence has revolutionized technology, as well as the way we live and work. Although AI is currently creating new opportunities across a wide range of fields, particularly in terms of significant improvements in the world of business and entrepreneurship by offering fresh perspectives on data analysis, automation and innovation, these approaches are leading to a shift in entrepreneurial mindset and, consequently, in the decision-making processes of many companies.
Although several studies have addressed the growing interest in incorporating AI into education, there has been little research into how entrepreneurial education can be enhanced through Artificial Intelligence. AI is not entering the field as a technology, but as an applied way of thinking. It is not an addition to the process; it is something that alters the very way in which the process takes shape[1].
Therefore, in the current landscape, it is clear that the algorithmic process intervenes above all where uncertainty previously prevailed. The initial phase of a project – the most fragile and least codifiable – suddenly becomes more transparent. Information is aggregated, scenarios are anticipated, and alternatives multiply without requiring lengthy exploration. This is what truly changes: the speed with which an idea moves from intuition to concrete possibility.
And yet, just as everything seems to be becoming more manageable, an interesting divide emerges. AI works perfectly well as long as the problem can be formalised. However, when relationships, the human context and the ambiguous nature of choices come into play, its contribution is drastically reduced. Not because it is any less powerful, but because it operates on a different level.
Various kinds of ‘intelligence’
Artificial intelligence is a machine of correlations. It does not understand; it recognizes. It does not decide; it suggests. It works on what has already been expressed in the form of data, even when it appears to generate something new.
Human intelligence, understood in a general sense, is instead an ability to attribute meaning. It does not merely link elements; it interprets them. It introduces discontinuities, deviations and errors that are not flaws but conditions enabling innovation.
Then there is something even more specific, which we might call the intelligence of Homo sapiens[2]. It is not merely cognition; it is embodied experience. It is the way in which decisions, emotions, culture and context intertwine. It is what enables us, for example, to recognize an opportunity even when there is not yet enough data, or to reject a ‘rational’ choice because it is ethically unacceptable.
In Pinker’s words, “the big surprise is – and I must confess it surprised me – just how much intelligence is implicitly hidden in linguistic databases if they are large enough” (Pinker et al., 2023). This brings to light two main sources of wonder: a form of unrecognized intelligence and the GPT-like technology that reveals it[3]. The “implicit intelligence” that astonished Pinker is not artificial in nature. Any linguistic database is, in essence, a snapshot of human expressions and imprints on the cosmos. It is also more comprehensive than the individual human intellect with which we usually compare AI… This ‘implicit intelligence’ that Pinker and many of us find surprising seems more akin to a universal intellect than to mere collective wisdom. To which we actively contribute or of which we are simply unaware, but in which virtually all of us participate through our individual data, in one way or another, in shaping ChatGPT’s training and, consequently, its capabilities. The collective sense of wonder we feel might suggest that this capacity is beyond our individual comprehension, or perhaps we have simply underestimated the scope of human intellect. In either case, it points to a higher form—in the Platonic sense—of human intelligence that transcends individual intellect.
Artificial intelligence does not replace skills such as leadership, critical thinking or emotional management.
This also gives rise to the most sensitive issue: dependency. Not in the trivial sense of ‘overuse’, but in a more subtle form. If decisions become increasingly assisted, the risk is not that of losing operational autonomy, but of losing interpretative depth. The entrepreneur, like any person in their everyday life, continues to make choices, but within an increasingly pre-structured framework.
It is as if AI were reducing the scope for doubt. And doubt, in entrepreneurial work, is not an obstacle but a resource. It is what compels us to question data rather than simply accept it.
This is particularly evident in education, which still lags behind the pace of change. AI is entering educational programmes, but often merely as a technical skill. There is a lack of reflection on how this technology redefines the way we think about problems, not just how we solve them.
The result is an interesting tension. On the one hand, students are acquiring increasingly sophisticated tools; on the other, there is a growing difficulty in developing an independent perspective. This gives rise to the need to integrate human and technological skills; indeed, it is not a matter of integration, but of preventing one dimension from dominating the other.
Where AI is seen as a strategic asset, it tends to play a role in the most creative and decision-making moments. Where scepticism prevails, it remains confined to more operational areas. It is not a question of greater or lesser trust, but of how the relationship between technology and responsibility is conceived.
Ultimately, the very concept of entrepreneurial mindset is being redefined. This no longer appears as a set of static skills, but as an ability to navigate between different types of intelligence, knowing when to delegate and when to resist delegation.
In this sense, AI is neither a risk nor a solution. It is an accelerator. And like all accelerators, it makes what is already there more evident. If there is a lack of vision, it amplifies superficiality. If there is depth, it can become an extraordinary tool.
Conclusion
The point, then, is not to learn how to use artificial intelligence more effectively. It is to understand to what extent we are still willing to use our own.
Perhaps, at this stage, it is no longer a question of understanding what artificial intelligence can do better, but what is changing as we use it. AI improves processes, reduces uncertainty and supports decision-making. And that is true, but only up to a certain point. Because the moment information becomes abundant, filtered and pre-organized, the decision itself ceases to be what it was. It is not simply better informed; it is less exposed. Less plagued by doubt, less forced to confront the void that often precedes the most radical insights. And this, in the long run, risks affecting precisely those skills we continue to regard as ‘human’: not because they are replaced, but because they change form, adapt, perhaps diminish.
In this sense, the distinction between artificial intelligence, human intelligence and the intelligence of Homo sapiens becomes less theoretical than it seems. The first organizes, predicts, identifies patterns. The second interprets, deviates, constructs meaning. The third, harder to isolate, brings together experience, context, responsibility – everything that makes a decision something that cannot be completely delegated. It is on this level that the most interesting, and perhaps most overlooked, game is played out: not in the efficiency of choices, but in their nature.
One question therefore remains: what sort of entrepreneur emerges from a decision-making environment that is increasingly supported by technology? Not necessarily one who is faster or more precise, but a different one. And understanding the direction in which this difference is heading is probably the real task before us.
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Raffaella Aghemo, Lawyer
[1] “Artificial intelligence as a strategic resource for journalism entrepreneurship: perceptions of the entrepreneurial mindset and decision-making” – Sánchez Gonzales, H. M., Cartes-Barroso, M. J., & Ftah Ftah, K – 2026
[2] “Human Intelligence, Articial Intelligence, and Homo sapiens Intelligence?” – Xiao-Li Meng – 2023
[3] “Human Intelligence, Articial Intelligence, and Homo sapiens Intelligence?” – Xiao-Li Meng – 2023