Inez Zwetsloot is Director of the AI4Business Lab and Associate Professor of Statistics and Business Analytics. From start-ups and SMEs to large corporates and NGOs: she works with many different partners to tackle real-world data and AI challenges together with students.

Data and AI are increasingly part of day-to-day decision-making in organisations of all sizes – from SMEs to global corporates and NGOs. At the AI4Business Lab, students work on real-life AI and analytics challenges as part of their thesis projects, ranging from optimising coffee roasting to improving global finance systems. In this lab, Inez Zwetsloot shows how challenge-based learning can connect education, research and societal impact, driving innovation in companies while giving students hands-on experience and tangible results.

Can you briefly describe your lab and its objectives?

We collaborate with students from the Bachelor’s in Business Analytics and the Master’s in Business and IT Management. As part of their dissertations or course work, students work in teams on analytics and AI challenges posed by partner organisations. These projects offer students valuable learning experiences while producing real impact for the companies involved.

How does your project address societal challenges?

Our projects involve a diverse range of organisations, including large companies, small and medium-sized enterprises (SMEs), educational institutes and NGOs. This year, for the first time, we matched students with 30 SMEs. SMEs are a vital part of the Dutch economy but often have limited research and development resources. Through challenge-based learning, students help SMEs experiment with AI technologies, making innovation accessible and supporting economic growth.

Could you give an example of a case?

One project was with Coffee and Coconuts, a coffee company. They wanted to understand how to roast coffee beans in a more energy-efficient way. They already collect data from their roasting machines, and students will built a model to map energy usage. From there, they can investigate whether the beans could be roasted with the same quality using less energy. It’s a practical, real-world problem that students can tackle with data analytics skills.

What have you changed compared to the earlier version of the course (or the standard approach)?

This is the first collaboration with SMEs in this format. Previously, we’ve run similar challenge-based learning projects for 2 years with a range of organizations. What’s new now is the link between education, research, and impact. Students not only learn to build analytics systems, but we also study the effect of these interventions on employee satisfaction and organisational productivity.

The projects are supported by consultants who supervise the students, ensuring that ideas are actionable, not just theoretical. If successful, we hope to scale this pilot from 30 SMEs to potentially 10,000 SME owners in the longer term.

How do you assess the impact of the course?

Impact is evident in multiple ways:

Companies are enthusiastic and return each year. Just this morning, I spoke with a company where a student did a challenge during the Bachelor thesis then worked their part-time during his Master and is now a full-time employee no proposing a challenge for the next cohort.
Students find it extremely useful. They learn how to apply classroom knowledge in practice.
Concrete impact examples: At ING, students improved upon a solution the company had already developed. At Zengate, students helped create a business analyst position, and that student is now working in Japan. FrieslandCampina implemented a student-developed model in its global finance system. These projects clearly go beyond assignments, generating tangible outcomes.

What was the most difficult part to implement?

Recruiting suitable projects takes time and effort, which isn’t formally part of our job descriptions. Supervising projects in companies is new for the university, and students need professional skills such as preparing meetings, presenting themselves, asking the right questions, and explaining analytics concepts to non-specialists.

Some projects inevitably fail because of these challenges. Still, it is extremely rewarding to see how education, research, and societal impact can be combined in a single project, showing the real potential of challenge-based learning.

Any practical tips for colleagues who want to try something similar?

Start small, focus on a few projects on meaningful topics. Ensure that students’ work produces tangible value for partner organisations. Combining educational rigor with societal relevance enhances both student learning and institutional impact.

Do you want to learn more about Inez’ her work? Visit the AI4Business website and Linkedin