Bringing Real Life and AI to the Classroom
My colleague Ricardo Galante from Portugal is a Principal Systems Engineer in SAS' Global Customer Advisory division. And he is a strong supporter of our SAS Global Academic Program, as he sees it as a mission to bring real-life business experience in the classroom - which is highly demanded and appreciated by his students. I had the chance to chat with him about his engagement and motivation.
Ricardo, beside your “normal” SAS role, what's your engagement in universities, and in what subjects?
Besides my role at SAS, I am involved in academic activities mainly in the areas of data science, analytics, artificial intelligence, digital transformation, and business decision-making. I also supervise master’s students, particularly in topics related to digital marketing, customer analytics, data-driven decision-making, and the use of analytics to solve real business problems.
More recently, I have also been involved in sessions and webinars focused on data science applied to healthcare, AI governance, trustworthy AI, and the practical use of analytics platforms such as SAS Viya in academic and research contexts. For me, teaching is not only about explaining concepts. It is about helping students understand how data, analytics, and AI can be applied to real decisions, real organizations, and real societal challenges.
What motivates you to combine your role at SAS with teaching, and how does your experience with customers enrich your teaching?
What motivates me most is the possibility of connecting two worlds that should be much closer: academia and industry. I work with customers who are dealing with real challenges: modernization, AI adoption, risk management, data governance, model management, regulatory pressure, and the need to create business value from data. This gives me a very practical view of what organizations are really trying to solve. When I bring that experience into the classroom, students are not only exposed to theory; they also understand why these topics matter in the real world.
Customer experience enriches my teaching because it allows me to share practical examples, common challenges, and lessons learned from real projects. It helps students see that analytics is not just about building a model. It is also about asking the right question, understanding the business context, explaining results, governing models, and supporting better decisions.
What advice would you give to students who want to build a career in analytics and AI?
I believe students need a combination of technical, analytical, business, and human skills. Of course, technical skills are important: statistics, machine learning, programming, data preparation, visualization, and knowledge of modern analytics and AI platforms. But that is not enough. Students also need to develop critical thinking. They need to understand the problem before jumping into the solution. They need to ask: What decision are we trying to support? What data do we have? Can we trust this data? What are the risks? How will the result be used?
My advice to students is: do not focus only on algorithms. Focus on solving problems. Learn the technology, but also learn how to communicate, how to explain your work, how to work with business teams, and how to think responsibly about the impact of AI.
In analytics and AI, the best professionals are not necessarily the ones who know the most complex model. They are the ones who can turn data into value, insight, and better decisions.
How important are topics such as AI governance, ethics, and trustworthy AI in the classroom today?
They are absolutely essential. AI is no longer just an experimental topic or a technical discussion. AI is now part of real business processes, customer interactions, risk decisions, healthcare, finance, public services, and many other areas. Because of that, students need to understand that building AI is not only about accuracy or performance. It is also about trust, transparency, fairness, accountability, privacy, and governance.
In the classroom, we need to help students understand questions such as: Can we explain this model? Is the data biased? Who is responsible for the decision? How do we monitor the model over time? What happens if the model starts behaving differently? These topics are not optional anymore. They are part of what it means to be a responsible data scientist, AI professional, or analytics leader. Trustworthy AI should be taught as a core component of AI, not as something added at the end.
What is the value of exposing students to real business cases and real technologies during their studies?
The value is enormous. When students work only with theoretical examples, they may understand the method, but they do not always understand the complexity of applying it in the real world. Real business cases help students deal with ambiguity, imperfect data, business constraints, ethical considerations, and the need to explain results to different audiences.
Real technologies are also very important because they help students understand how analytics and AI are actually operationalized. In business, it is not enough to build a model in isolation. Organizations need to manage data, build models, deploy them, monitor them, govern them, and integrate them into decision processes.
By exposing students to platforms such as SAS Viya, for example, they can see the full analytics life cycle: from data preparation and exploration to modeling, deployment, monitoring, governance, and decisioning. This makes learning much more realistic and prepares students better for the professional world.
How can collaboration between academia and industry help prepare the next generation of data and AI professionals?
Collaboration between academia and industry is fundamental.
Academia brings research, critical thinking, scientific methods, and the ability to explore new ideas deeply. Industry brings real problems, practical constraints, current technologies, and a clear view of the skills that are needed in the market. When these two worlds collaborate, students benefit enormously. They can learn theory, but also understand how that theory is applied. They can work on real use cases, interact with professionals, use modern tools, and develop a much clearer view of career opportunities. For companies, this collaboration also creates value. It helps build talent, encourages innovation, and strengthens the connection between research and practical business impact.
In areas such as data science, AI, and analytics, this connection is particularly important because the field is evolving very quickly. Universities and companies need to work together to ensure that students are not only learning what was relevant yesterday, but also preparing for what organizations will need tomorrow.
AI / LLMs are everywhere these days. Have they changed students’ behaviours and expectations – and yours as well?
Yes, absolutely. AI and LLMs have changed the way students search for information, write, study, and approach assignments. In many cases, students now expect faster access to explanations, examples, summaries, and even code. This creates opportunities, but also challenges.
The opportunity is that AI can support learning, personalization, creativity, and productivity. It can help students explore ideas, clarify concepts, and accelerate some parts of their work.
The challenge is that students also need to learn how to use these tools critically and responsibly. They need to understand that AI can generate useful answers, but also inaccurate, incomplete, or biased ones. They still need to validate, question, interpret, and take ownership of their work.
For me as a teacher, this also changes expectations. It is no longer enough to ask students to simply reproduce information. We need to design learning experiences that require critical thinking, application, reflection, and real understanding. In a way, AI forces us to focus on what really matters in education: not just producing an answer, but understanding the problem, evaluating the answer, and knowing how to use it responsibly.
Ricardo, thanks a lot for these insights!
You can connect with Ricardo here.
