A couple a weeks ago I was kindly invited to take part of a guest lecture on the behalf of Implema for the masters program of Industrial engineering and management at LIU. In their fourth year, every student gets the opportunity to become “student consultants” for a company and come up with solutions for real life problems that their case company face. Now I was back sharing some of the experiences that I’ve made since I graduated.

One of the insights I shared with the students is one of decision making. As of now, digitalisation is accelerating and this creates opportunities and risk as never before. To act conclusively and utilising the possibilities that arises is of dire importance for every company. But in their eager to act some companies jump to a solution before they focus on the challenge. This can lead to resource waste and a misguided focus. It’s often as hard or harder to understand the challenge than to come up with a solution. This is particularly true with AI and machine learning.

Having worked as a Data Scientist, I understand many of the possibilities that machine learning can bring. There is a wide range of tasks and problems that can be solved with AI and Machine learning and the problem is seldom if machine learning can be the solution. It generally can but finding a good real life case is often more tricky. This is because you as a Data Scientist are asked to find problems that suite the solution and not the other way around. Focusing first on the challenge and, if the solution requires it, add an AI-application on top is a more sound way of solving a problem. I think this is one of key reasons why many companies struggle with leveraging the possibilities of AI in everyday business. They want an AI-application and bend the challenge to fit the solution. Hence, the advice I shared with the students was to generally take more time and focus on the challenge than you think, before deciding on the solution. Specifically when it comes to AI, be sure to frame the challenge in such a way that Machine Learning could be the answer but don’t jump to the conclusion that machine learning is the solution.

AI and decision making is one of the areas that Implema and LIU will focus on in the IE3 project during the next couple of months which I think is great. Not every student needs to become an expert on machine learning but all students will benefit from understanding when and why machine learning can be applied to make better decision based on that knowledge.

Eskil Rehme