Real Machine Learning Solutions for Business: A1xCROZ

Overview

At A1, we wanted more than just another tech workshop. Our goal was to explore how and create real value in our day-to-day work, not in theory, but in practical, applicable ways using machine learning solutions. Over two days, colleagues from across telecommunications, network planning, and ICT services gathered to deepen their understanding of machine learning systems and how to identify where these technologies can make a difference in our business context. We left with a stronger technical intuition, hands-on experience in framing machine learning problems, and a clearer view of how data-driven solutions can support smarter, more efficient operations.

real people not animations, in a full steam working office situation

Challenge

Machine learning solutions offer huge potential, but at A1, like in many organizations; we experienced the friction that comes with applying them in real business settings. The concepts often felt too abstract, and it wasn’t always easy to link them to actual business needs. Even though we work with rich datasets, they’re often siloed across teams and systems, making it harder to unlock the full value of machine learning solutions.

serious person that made a breakthrough in the work office environment and are really happy

Solution

To make meaningful progress, we knew that theory alone wouldn’t be enough. We needed an experience that would push us to think differently, try things out, make mistakes, compare approaches, and see for ourselves where machine learning truly fits and where it doesn’t.

In response to A1’s needs, CROZ created a custom workshop designed specifically with their challenges in mind.

Day 1 was all about setting the stage. We explored the core building blocks of machine learning: what it is (and isn’t), how typical machine learning projects are structured, and what it takes to go from a problem statement to a functioning machine learning solution. We also looked at real-world examples from the telecom industry, what worked, what didn’t, and why, to spark ideas and bring concepts closer to our domain. The focus was on building shared language and understanding, so that everyone, from technical to business roles, could join the conversation on equal footing.

Day 2 was where the shift happened, from understanding to doing. Working in teams, participants tackled concrete Use Cases based on real challenges from A1’s operations.

They debated, decided, and designed potential machine learning solutions, or, in some cases, recognized where traditional logic still made more sense. These weren’t abstract problems; they were rooted in familiar, day-to-day operational challenges.

They explored possible data signals, considered the feasibility of applying machine learning, and identified the expected business impact. Each team then presented their developed Use Case, sharing not just solutions, but the thinking behind them. These takeaways weren’t theoretical; they were tailored, actionable ideas that participants took back to their teams.

This experience opened up a fresh perspective on how we can approach our challenges with data, creativity, and more confidence. It didn’t solve everything overnight, but it gave us a shared foundation to build on and the motivation to keep exploring.