The world of data science continues to attract professionals from diverse backgrounds, bringing fresh perspectives to an increasingly complex field. And that's a good thing! In a recent conversation, Yoann Veny, Data Scientist at Agilytic, shared his unique journey from sociology to artificial intelligence.
You can watch (or listen to) the whole interview here!
An unconventional path to data science
Yoann's story begins with what he calls an "atypical" background for a data scientist. Originally pursuing sociology at university specifically to avoid mathematics and sciences (his words, not ours), he discovered an unexpected passion for statistics during his social sciences studies. This revelation led him to complete a master's degree in statistics for social sciences at KU Leuven, setting the foundation for his future career.
"When I had statistics courses, I don't know why, but there was a click that said 'Ok, this can be used to do this... this kind of thing.' It was really a revelation."
This interdisciplinary foundation has actually proven invaluable throughout his career. After transitioning from academia to industry, Yoann spent a decade at a telecommunications operator, where he experienced firsthand the power of data-driven insights. A particularly memorable project involved combining marketing and technical network data to predict customer churn: a perfect example of how different data silos within organizations can be united to solve complex business problems.
The consulting advantage: diversity and methodological rigor
After ten years in a large corporation, Yoann joined Agilytic, embracing the world of data science consulting. In his experience, this transition highlighted a lot of differences between in-house and consulting roles.
"We can work in retail, pharmaceuticals, financial institutions, the public sector... What I find most fascinating is how data and statistics reveal that some challenges are universal across industries. The same skills can be applied to solve problems that, on the surface, seem completely different."
In other words: while business contexts may vary dramatically, the underlying methodological approaches remain remarkably consistent.
Navigating the GenAI hype: beyond the chatbot request
"The real challenge today is guiding businesses as they define what they want and could do with generative AI. Companies often tell us 'I want a chatbot.' Technically, we can build one. But the bigger challenge is sitting down with these stakeholders and taking the time to define the concrete problem they're trying to solve, and whether a chatbot is actually the best solution."
This challenge has fundamentally changed Agilytic's approach to client engagement. While traditional machine learning projects could often be scoped in one or two workshop sessions, generative AI initiatives require deeper diagnostic phases lasting five to ten days. This extended discovery process addresses several critical questions:
What specific business problem needs solving?
Is generative AI actually the right solution?
What technical infrastructure and data requirements exist?
How will the solution scale from proof-of-concept to enterprise deployment?
The data quality imperative: why GenAI projects remain data projects
"At Agilytic, we have the extremely strong conviction that, despite all the facilitations we have today to deploy GenAI solutions (and it's becoming easier and easier), these projects are not IT projects. We firmly believe that GenAI models remain above all data projects."
For instance, organizations rushing to implement RAG (Retrieval-Augmented Generation) systems with poorly maintained document bases will inevitably fail. The same data quality principles that govern traditional machine learning (clean, well-organized, regularly updated information) apply with equal force to generative AI systems.
This creates new challenges: organizations must now consider unstructured data sources that may have never been subject to rigorous data governance. Legal departments maintaining policy documents, for example, must now understand that their work feeds into automated processes requiring consistent quality and maintenance standards.
Democratizing AI innovation: public support for enterprise transformation
One encouraging development is the growing public sector support for AI adoption among businesses. Both the Walloon and Brussels regions offer substantial financial assistance to help organizations overcome the initial barriers to AI implementation:
"Start IA" supports diagnostic phases and roadmap development.
The Walloon region's "Tremplin IA" can finance up to €40,000 in proof-of-concept development, with 70% public funding.
For Brussels-based companies, Innoviris offers specific support for generative AI projects.
"Most companies aren't even aware that these solutions exists. We're often the ones introducing them to prospects or clients. But I think this really helps overcome that hurdle of de-risking the POC."
These programs recognize a fundamental truth: the biggest barrier to AI adoption isn't technical capability but rather the financial and organizational risk of initial experimentation.
The future of enterprise AI: beyond technical implementation
Looking ahead, the success of generative AI initiatives will depend less on technical sophistication and more on organizational readiness. This encompasses several dimensions:
Cultural adaptation: teams must understand how their work contributes to automated systems and adjust their processes accordingly.
Scalability planning: solutions that work for five test users must be architected to handle hundreds of concurrent users in production environments.
Framework selection: rather than building everything from scratch, organizations benefit from methodical evaluation of open-source frameworks across multiple dimensions, from OCR capabilities to maintenance requirements.
Personal perspectives: AI in daily life
Beyond professional applications, Yoann uses AI technologies in his personal life: like many professionals, he uses ChatGPT for everyday queries.
More uniquely, his musical hobby has benefited from AI-powered audio separation tools that can isolate individual instruments from complete recordings.
"Starting from a single track, separating it between different tracks (drums, bass, vocals) is quite incredible. It's nice to be able to take a song, remove the guitars from the song to leave the drums, bass and possibly the vocals, and play the guitar yourself over it to join Metallica or any other music group."
This shows how AI can boost creativity, and hints at how it could fit seamlessly into all kinds of everyday activities.
Yoann's takeaways
As organizations continue to navigate the generative AI landscape, Yoann's experience offers several key lessons:
Take time for proper diagnosis: the complexity of GenAI implementations requires more thorough upfront planning than traditional ML projects.
Remember data fundamentals: despite technological advances, data quality remains the foundation of successful AI initiatives.
Leverage available support: public funding programs can significantly reduce the financial risk of initial AI experimentation.
Focus on business problems: technology should serve clearly defined business objectives, not drive them.
Plan for adoption: technical success means nothing without organizational adoption and cultural change management.
The generative AI revolution is real, but its success will be measured by how effectively organizations can integrate these tools into their operations; not by the sophistication of the underlying models.