Technology, processes and people
As a Principal Expert, Mike translates organisation-wide challenges into scalable data and AI solutions. But it is never just about technology. Successful implementations only emerge when technology, processes and people are aligned with the organisation’s strategic objectives.
“That sometimes means making difficult decisions and occasionally saying no, because you simply can’t meet every request,” Mike explains. “Especially when it comes to security. And that can lead to some pretty tough discussions. Sometimes a client already has a particular solution in mind beforehand, but that usually isn’t the best approach. We first go all the way back to defining what the problem actually is. Once that’s clear, we look at the functional requirements. Then, based on the problem analysis and those requirements, we decide which technology is best suited to solve it.”
This approach requires both in-depth expertise and hands-on experience. Mike didn’t start his career in IT, but in commercial real estate. However, he soon discovered his real interest lay in automation and data. What began with making processes smarter grew into a specialisation in data engineering, development, architecture and AI. By teaching himself to code and continuously experimenting, he developed into an expert who effortlessly connects technology and business.
Building the Data & AI practice
Since January 2025, Mike has been helping to build the Data & AI practice at Sopra Steria in the Northern region of the Netherlands. What started as a small team has grown into a mature practice of nine specialists with a clear focus: guiding organisations from strategy to fully operational solutions in production. This end-to-end approach is characteristic of Sopra Steria. From vision development and adoption to data engineering, architecture, machine learning and governance, clients can rely on an integrated approach in which all required expertise comes together.
AI Center in practice
A concrete example of this approach can be seen in Mike’s work for the National Coordinator Groningen (NCG), the organisation responsible for investigating and reinforcing homes in the earthquake-affected region of Groningen. There, Mike serves as coordinator of the centrally positioned AI Center.
His role is broad, ranging from leadership and advice on ethics, privacy and governance to architecture, code reviews and stakeholder management. At the same time, work is being done on the structural classification of an enormous volume of documents. This is a technically complex challenge, not only because classification models must be developed and trained, but also due to limited data quality.
Mike Huls: “To make the assignment manageable, we started working in short iterations of experimenting, implementing and evaluating. This short-cycle way of working ensures better alignment with the client and makes it possible to adjust quickly where needed.”
Setting up a data department
In earlier roles too, Mike demonstrated how important it is to bring technology, processes and people together. At a marketing automation company with DPG as a major client, he set up a data department to cope with the ever-growing workload. It was a challenging job in which he gathered a number of people around him and ultimately built a real team to make the data department a success.
“When I started there, the onboarding period took months,” says Mike Huls. “Everything ran on legacy technology. I introduced new tooling, improved processes and trained people. When I left, we had scaled up significantly and were able to handle a multiple of the previous workload with only a fraction of the people. In addition, a new colleague could submit their first pull request after just three days. That’s a really great result and a good example of bringing technology, processes and people together to contribute structurally to organisational goals.”
The foundation must be solid
In his current work, Mike encounters many organisations that all want to do something with AI, because they are afraid of being left behind.
“As an organisation you may want to do something with AI, but that’s only possible if the foundation is solid. If your data is inaccessible, low quality or simply doesn’t exist, it becomes difficult to solve data-related challenges: it’s garbage in, garbage out. For AI applications, that effect is amplified even further.”
Without good data quality, clear governance and concrete business objectives, AI remains an experiment, in his view. Expectations are also sometimes unrealistically high.
“AI isn’t some miracle cure or magic wand you can wave and suddenly all problems are solved. It requires a solid foundation and proper direction. Fragmented initiatives without central steering can create risks in the areas of security, privacy and compliance. That’s exactly why a well-thought-out, integrated approach is essential.”
A bit of a geek
To stay ahead in his field, Mike continuously challenges himself. “You have to be a bit of a geek,” he says. “I experiment a lot with new technologies and try things myself to really understand them. For example, I recently built a web application that allows you to anonymise sensitive information in documents and images before uploading them to an LLM. I also write on my blog and on Towards Data Science about software engineering, machine learning, AI, system design and architecture. I strongly believe you only truly understand something once you can explain it simply. Blogging forces me to really get to the bottom of it.”