JL Verboomen
Thought leader, practitioner, and consultant in AI, data science, analytics, data architecture, and data governance. Proud team member at Massive Insights. Professor. Smithsonian Laureate.
Toronto, Canada
Actions
Born in Pointe Claire, Quebec, Jean Louis (JL) has undergraduate degrees in Computer Science from Concordia University. He also has his MSc in Statistics from Queens University and an MBA from the John Molson School of Business.
After working in the public sector early in his career, JL moved into the private sector by joining Rogers Communications in 1996 where he focused heavily on database development, reporting and Customer analytics in the wireless industry. JL was recruited to join MT&T (Now Aliant), and enjoyed success in a number of roles of increasing responsibility. In fact, in 2000 JL was made a Computerworld Smithsonian Laureate for advancements made in business applications of data science.
During this time, his work was featured in various industry publications – Including being featured in the cover story of SAS Exec Solutions magazine.
Since that time, JL has taken on increasingly more senior positions in analytics, data science, and consulting across a variety of industries such as retail, automotive, finance and gaming. During this time JL has also been an adjunct professor at Mount Saint Vincent University and Georgian College. His teaching dossier includes courses in Data Science, AI, Statistics, and MIS.
JL is now a VP of Business Solutions with Massive Insights - A leading Canadian Analytics and AI consultancy. JL posts three articles per week on Linkedin, covering a wide range of AI and analytical topics.
JL lives in Toronto, Ontario with his wife and son and is an active volunteer in numerous charitable and educational organizations across both Canada and the US.
Area of Expertise
Topics
Why Simpler Models Often Outperform Deep Learning: Defending a Data Science Heresy
In data science and AI, certain beliefs have become dogmatic truths. Among these, one stands tall: that deep learning and highly complex models hold the keys to all predictive power. Conferences brim with glossy presentations of neural networks, LinkedIn feeds echo with tales of ever-deeper architectures, and job postings list PyTorch and TensorFlow as bare necessities. Yet, amid this din, I wish to propose an unpopular — and, dare I say, heretical — opinion: In many real-world business cases, simpler models outperform their deep learning counterparts, and the blind pursuit of complexity often leads us astray.
Why Context Is King: Unlocking the Real Power of Generative AI
In the world of AI, particularly in the domain of Generative AI (GenAI), much attention has been devoted to the art of crafting the “perfect prompt.” Workshops, courses, and online guides have popped up everywhere, promising to teach users how to harness AI’s potential with a well-phrased question or statement. While there’s undeniable value in learning how to prompt effectively, a less celebrated but far more critical element often goes overlooked: context.
When Success Isn’t Enough: Lessons Learned from an Unexpected Firing
There are moments in every professional’s journey that shape not only their career, but their character as well. For me, that moment arrived without warning, on a day I believed would be business as usual. I had always identified with being a high achiever. Promotions, glowing performance reviews, and ambitious projects successfully delivered - all markers of a career on the right track. Yet, in a sudden twist, I found myself sitting across from someone I barely knew, listening to the words: “We no longer need your services.”
When Algorithms Close Doors: My Experience as a Victim of AI Ghosting in the Job Market
After almost 800 job applications, I learned the true power - and pitfalls - of AI and automated hiring. Some called this revenge as there is some irony at play - I work in the AI field. I work on solutions that likely had a hand in creating the solution (or is it a problem?) that many companies employ to handle applications. This was a taste of my own medicine. It showed me the importance of not letting AI replace the human touch completely in recruiting or other disciplines.
The Broken Window Fallacy & AI
The world is designed to make us spend time and money for things that may not be beneficial for us in the long run. The better we understand opportunity cost in AI and Machine Learning, the better we will be able to make decisions that allow us the change that — and change the trajectory of our businesses.
The Growth of the Semantic Layer in the Age of AI and LLMs: Unlocking the Next Frontier
There is no question that the worlds of artificial intelligence (AI) and large language models (LLMs) are rapidly converging with enterprise analytics. As organizations race to extract maximum value from their data, the need for a robust, scalable, and business-centric data architecture becomes increasingly more critical. At the heart of this evolution lies a transformative concept that has been around for a while now: the semantic layer.
I Don’t Love Math or IT—And That’s Okay
Here’s a confession you won’t often see on LinkedIn: I studied them, I taught them, experimented with them, and my work leverages both Math and IT, but I genuinely dislike both disciplines. Yes, you read that right. I’m surrounded by code, algorithms, and never-ending numbers, but none of it speaks to my soul, and it never has.
How AI is Changing Leadership: The New Role of Leadership in the AI Era
When Billy Joel crooned, "We didn’t start the fire," in his 1989 classic, he captured a sentiment that resonates deeply in today’s AI-driven world. The rapid emergence of artificial intelligence is not a blaze lit by any single leader, but a wildfire reshaping the terrain of business and society alike. As the flames of innovation leap ever higher, the traditional notion of leadership must evolve. The question is no longer only about who leads, but how one leads amid the dazzling, disruptive power of AI.
How AI is Changing Leadership: The New Role of Leadership in the AI Era
When Billy Joel crooned, "We didn’t start the fire," in his 1989 classic, he captured a sentiment that resonates deeply in today’s AI-driven world. The rapid emergence of artificial intelligence is not a blaze lit by any single leader, but a wildfire reshaping the terrain of business and society alike. As the flames of innovation leap ever higher, the traditional notion of leadership must evolve. The question is no longer only about who leads, but how one leads amid the dazzling, disruptive power of AI.
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top