Events like Poli International Fest play an important role in bridging the gap between academic environments and the real world of work. They give students the opportunity to go beyond theory and interact directly with professionals, companies, and perspectives that reflect how industries are evolving in practice.
As part of this edition, Stefanini Group participated in the event, contributing to the conversation through the presence of three colleagues, Emoke Guiu, Head of Data Strategy, Alexia Gusa, EMEA L&D Senior Manager, and Mihnea Botocan, DWP Sr. Tech L&D Coordinator. Their session focused on how learning, collaboration, and professional development are being reshaped by global work environments and AI, and what this means for students preparing to enter the workforce.
Not long ago, learning was easy to define. It meant attending courses, going through structured content, and validating knowledge through exams or certifications. It was linear, predictable, and clearly separated from work. Today, in a global environment like Stefanini Group – operating in more than 100 countries and working across areas such as AI, data analytics, application development, and digital workplace solutions, this model no longer reflects reality.
Learning is no longer something that happens before work. It happens inside it. It emerges in real projects where no two situations are identical. It happens through collaboration with people from different cultures, through constant feedback, and through the need to adjust quickly when context changes. In this sense, learning becomes less of an “event” and more of a continuous process embedded in everyday work.
This naturally changes the way we look at collaboration as well. In multicultural teams, you are not only working with different skill sets, but with fundamentally different ways of thinking and communicating. Some people prefer to analyze deeply before contributing, while others learn by engaging directly and iterating in real time. Some cultures encourage asking many questions before adopting a new tool, while others prefer to test first and understand through practice.
None of these approaches are right or wrong. The challenge appears when we treat them as if they should be the same. The real value of global collaboration comes from understanding these differences and using them as a strength rather than a source of friction.
From here, the conversation moved to something less visible but highly influential: cognitive biases. These mental shortcuts shape how we interpret information and interact with others, often without us realizing it. Confirmation bias leads us to favor information that supports what we already believe. The halo effect allows a single trait to influence our entire perception of someone. Similarity bias pushes us to feel more comfortable with people who think and behave like us.
In diverse environments, these biases can quietly limit collaboration and innovation if they are not acknowledged. They don’t just affect decisions, they affect how open we are to different perspectives and how we build trust across differences.
Another key topic was the impact of Artificial Intelligence on learning. AI is already reshaping how people develop skills by identifying gaps, recommending personalized learning paths, and supporting continuous development through intelligent platforms. AI-powered tools and learning systems can act as personal coaches, helping individuals understand where they are and what steps they need to take next based on their goals and existing skills.
This shifts learning from a standardized model to a highly personalized and continuous experience. Development is no longer tied to a specific place or moment, it becomes accessible anytime and integrated directly into daily work.
However, as technology expands access to knowledge, it also changes what truly creates value. Memorizing information is no longer the key differentiator. Instead, success increasingly depends on human capabilities such as critical thinking, adaptability, communication, emotional intelligence, collaboration, and curiosity. Technical skills remain important, but they are no longer sufficient on their own in a world where tools evolve constantly.
Mihnea illustrated this shift through his own career journey, moving from law into IT. His experience highlighted that modern careers are no longer linear paths, but sequences of transitions shaped by learning and adaptation. Early career experiences, especially those that are challenging and hands-on, build discipline and practical understanding that cannot be replaced by theory alone. He also emphasized the importance of personal experimentation through home labs, projects, and certifications as a way to truly understand how systems work in practice.
Emoke and Alexia added another layer by focusing on how people respond differently to change, especially when adopting new technologies like AI. Some individuals require detailed explanations and validation before adopting new tools, while others prefer to experiment immediately and learn through usage. These differences are influenced by culture, experience, and technical background, and they become especially important when scaling change across global teams.
They also highlighted a growing challenge in today’s environment: perfectionism. In a world where change is constant and fast, waiting to feel fully prepared is often unrealistic. As a result, learning shifts toward experimentation, iteration, and the acceptance that mistakes are part of the development process.
In the end, the key message from all three speakers was consistent. While AI and global collaboration are transforming how we work and learn, the human dimension remains central. What defines success today is not only what we know, but how we think, how we collaborate across differences, and how willing we are to keep learning in an environment that never stops changing.
