Agilytic

17/06/2026

PME wallonnes, le programme Tremplin IA de Digital Wallonia vient de rouvrir ses candidatures !

Il couvre le développement d'un Proof of Concept en IA, financé à 50% par la Région wallonne (jusqu'à 20 000 € de subvention).

Cette édition, 50 dossiers seront sélectionnés.
⚠️ Clôture des candidatures : 17 juillet 2026

Et comme Agilytic est partenaire officiel du programme, nous pouvons vous aider à monter votre dossier et à cadrer votre projet pour maximiser vos chances de sélection !

Plus d'infos sur notre site :

Tremplin IA by Digital Wallonia helps you de-risk you A.I. proof of concept. As is an official partner, Agilytic can help you submit your project. Applications close on April 13, 2025.

15/06/2026

This is probably the trickiest type of ML drift: the concept drift.

Classic example: a credit risk model trained before an economic downturn. Even if the features don't change, what those features mean in a recession is completely different:
💼 employment status,
💸 spending patterns,
🔄 repayment behavior...

The model is still using yesterday's logic.... in today's world.

The fix? On our blog as always!https://hubs.la/Q04l26lY0

08/06/2026

𝗧𝗵𝗲 𝟳 𝗱𝗲𝗮𝗱𝗹𝘆 𝘀𝗶𝗻𝘀 𝗼𝗳 𝗔𝗜 | #5: Gluttony 🍩

PSA: buying powerful AI tools is 𝘯𝘰𝘵 the same as using them.

Yet most budgets treat it that way: heavy spending on platforms and licenses / almost nothing on training, change management, or process redesign.

Tools sit unused, simply because nobody was ever set up to succeed with them.

The rule of thumb: 50% tech, 50% change management. Every time.

Swipe to get the full breakdown.

Psst... Speaking of AI adoption done right: we wrote about what it looks like when it's driven from the C-suite down 👉

Struggling with AI adoption? Agilytic’s executive coaching helped a top financial firm turn Microsoft Copilot theory into practical ROI.

01/06/2026

🌱 𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 𝗰𝗮𝗻 𝗮𝗹𝘀𝗼 𝘀𝗲𝗿𝘃𝗲 𝗮 𝘀𝗼𝗰𝗶𝗮𝗹 𝗺𝗶𝘀𝘀𝗶𝗼𝗻.

Backstage Network supports the socio-professional integration of Brussels residents. And we're proud to have contributed to this amazing project!

Swipe to read what Thomas Prévost, Co-founder of the startup, had to say about our collaboration 👉

27/05/2026

🥁 𝗙𝗿𝗶𝗱𝗮𝘆 𝟭𝟱𝘁𝗵 𝘄𝗮𝘀 𝗖𝗹𝗮𝘂𝗱𝗲 𝗗𝗮𝘆 𝗮𝘁 𝗔𝗴𝗶𝗹𝘆𝘁𝗶𝗰!

A full day dedicated to one question: how do we get the most out of AI? Not in theory, but in our actual day-to-day work with clients?

Here's how it went.

The morning covered the basics for those just getting started:
☕️ A deep dive into using Claude for Power BI
☕️ A session on writing skills and agents that are actually efficient and accurate

The afternoon went further:
☀️ Reducing token costs
☀️ Knowledge management methods
☀️ An open floor where anyone could bring their real frustrations and questions

The best part? The conversations that happened between the sessions: we compared approaches, discovered how our colleagues solved the same problem in completely different ways... and left with a list of things to try on Monday morning.

A big thank-you to the whole team for participating, and a special shoutout to Javier for the organization!

25/05/2026

"Is it the model?"
"Nobody touched it."

"Is it the input?"
"It looks the same."

"Well, our spam filter was 99% accurate... and now it's letting things through. Where does this come from?"

Actually, the devil lies in the outputs.
The 𝙤𝙪𝙩𝙥𝙪𝙩𝙨 of the real world changed. And the model didn't follow.

This is 𝗹𝗮𝗯𝗲𝗹 𝗱𝗿𝗶𝗳𝘁: the second type of drift in machine learning, and one of the sneakiest.
The model slowly stops doing its job properly, with no error or warning.

The fix? On our blog: https://hubs.la/Q04h44xR0
And stay tuned: 2 more types of drift still to come 👀

Drift in machine learning occurs when a model’s performance declines due to changes in data or relationships over time. Learn about feature, label, prediction, and concept drift, how to detect them, and best practices.

20/05/2026

𝗧𝗵𝗲 𝟳 𝗱𝗲𝗮𝗱𝗹𝘆 𝘀𝗶𝗻𝘀 𝗼𝗳 𝗔𝗜 | #4: Envy 🪞
"Amazon does it. Google does it. Why aren't we doing it?"

Simply because copying a solution without understanding the problem it was built for, is like buying someone else's prescription glasses because they said it helped them see better.

Swipe through the carousel for the full breakdown 👉

18/05/2026

𝗗𝗼𝘇𝗲𝗻𝘀 𝗼𝗳 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗿𝗲𝗽𝗼𝗿𝘁𝘀. 𝗙𝗶𝘃𝗲 𝘄𝗼𝗿𝗸𝘀𝗽𝗮𝗰𝗲𝘀. 𝗧𝘄𝗼 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀. 𝗡𝗼 𝘀𝗵𝗮𝗿𝗲𝗱 𝗹𝗼𝗴𝗶𝗰.

A leading food & beverage company had data everywhere... but no coherent picture.

We stepped in to rationalize, harmonize, and automate their entire reporting setup, bringing Belgium and the Netherlands onto a single, unified data model.

The result: stronger operational efficiency, faster responsiveness to business needs, and data governance that actually holds up.

Swipe to see how 👉
Full breakdown on our website: https://hubs.la/Q04gp9G_0

11/05/2026

"𝗢𝘂𝗿 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹 𝘄𝗮𝘀 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗳𝗶𝗻𝗲 𝗹𝗮𝘀𝘁 𝘆𝗲𝗮𝗿. 𝗪𝗵𝘆 𝗶𝘀 𝗶𝘁 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝙣𝙤𝙬?"

Nobody changed the model. Nobody touched the code. And yet...the predictions are getting worse.

This is what we call a drift.

AI models don't learn continuously: they learn once, from a snapshot of reality. So when the world changes (your customers evolve, your market shifts, your data looks different) the model keeps applying yesterday's logic to today's situation.

No alarm goes off. No error message appears. It just... gets worse 🙃

Since this issue isn't addressed that often on LinkedIn, we thought we'd do a small recap on the different types of drifts. Let's start with the first one: the feature drift.

👉 Full breakdown on our website: https://hubs.la/Q04fCnbB0

06/05/2026

𝗬𝗼𝘂𝗿 𝗽𝗿𝗼𝗼𝗳 𝗼𝗳 𝗰𝗼𝗻𝗰𝗲𝗽𝘁 𝘄𝗼𝗿𝗸𝗲𝗱. 𝗡𝗼𝘄 𝘄𝗵𝗮𝘁?

Most teams celebrate when a PoC lands well. And they should! Getting there is hard.
BUT a successful proof of concept is not a successful product.

Between "this works in the lab" and "this runs reliably in production" lies a gap that kills more projects than any technical failure ever could.

We've navigated that gap across 300+ projects. And here is what 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 makes the difference:

Optimize your data projects by seamlessly transitioning from PoC or prototype to an industrialized MVP. Ensure scalability, robustness, and long-term value beyond feasibility validation. Unlock the full potential of your data solutions with a structured industrialization approach.