17/03/2025
In recent years, the term Data Science has increasingly made way for the broader—and trendier—concept of AI (Artificial Intelligence). Where Data Science was once the go-to buzzword in data analytics and Machine Learning, AI now seems to dominate the conversation. But does that mean Data Science has lost relevance? And where does CQM fit into this shift?
From hype to integration
When Data Science first gained traction, it quickly became the umbrella term for anything related to data analysis, Machine Learning, and algorithms. Organizations invested in data scientists to extract insights from large volumes of data and build intelligent models. Today, fuelled by developments in Deep Learning and Generative AI, AI has taken over as the new buzzword.
But Data Science has not disappeared. On the contrary: it remains a crucial foundation of AI. While AI is often associated with cutting-edge applications, it is Data Science that provides the methods and analyses to enable them. Terms such as AI, Machine Learning, and Deep Learning are often used interchangeably, though they each represent distinct levels within the same domain. Want to dive deeper? Curious about the differences between AI, Machine Learning, and Deep Learning? We explore them in this article (currently available in Dutch). Prefer an English summary? Feel free to get in touch.
Why AI is gaining ground
The shift from Data Science to AI can be attributed to several factors:
1. Innovations in AI
The rise of Deep Learning and Generative AI has rapidly accelerated the AI domain. Explosive data growth, more powerful algorithms, and significantly increased computing capacity have unlocked a wave of new possibilities.
2. Marketing and perception
AI sounds more innovative and forward-looking than Data Science. Companies and media prefer using AI as a broader, more exciting term—one that attracts investors and customers alike.
3. Broader applicability
Where Data Science mainly focuses on analysis and modelling to support complex decision-making, AI also encompasses technologies like image and speech recognition, autonomous systems, and intelligent assistants that can automate tasks.
CQM’s role: AI with a strong mathematical foundation
At CQM, we’ve witnessed this shift up close. For over 40 years, we’ve been solving complex problems using mathematical models, data analysis, and algorithms. AI, to us, is not a hype—it is a valuable instrument that we apply alongside Data Science to help clients make better decisions.
We don’t believe in AI for AI’s sake. We believe in AI with a solid mathematical and analytical foundation. That means looking beyond the buzz and always seeking the most practically applicable solution. Sometimes that’s a cutting-edge AI model—but often, a classical mathematical approach is far more powerful.
Not every business case calls for Generative AI. In many cases, traditional AI techniques like regression analysis and mathematical optimization are more effective. This is especially true for supply chain optimization, where Generative AI often proves too abstract, while traditional AI continues to deliver proven impact. You can read more in our article on traditional vs. generative AI.
For our clients, this translates to fact-based decisions, transparent models, and practical applicability. Whether it’s supply chain optimization, transport planning, product development or smart warehousing—we combine the best of Data Science and AI to find the optimal solution.
The future: Data Science as the backbone of AI
Although AI has largely overshadowed Data Science in public perception, Data Science remains the backbone of AI. Without solid data analysis and mathematical modelling, AI becomes nothing more than a black box. At CQM, we make sure AI delivers real impact—rather than being just another buzzword.
AI offers vast potential, but success depends on how it is applied. At CQM, we know that a sound AI strategy goes beyond technology alone—factors such as domain knowledge, collaboration, and practical applicability are equally essential. In this article (in Dutch), we share five key insights from our international AI projects. Prefer an English version? We’d be happy to provide a summary—just get in touch.
Curious how AI and Data Science could strengthen your organization?
Get in touch and discover the possibilities.
Image composed and edited based on visuals by Gordon Johnson and Elchinator via Pixabay.
