The use of ML in business processes isn't considered as a debate on whether or not to incorporate it, it's a matter of how and how much.

‍

By now every business executive should know about ML and started thinking on how to incorporate it in their business, and that's putting it lightly. ML use cases have shown great results in many different business perspectives, such as customer service, business analytics, fraud provention and employee expertise support. By employee expertise support i am actually talking about cases where ML support the employee with years of experience. The positives of including ML in your business processes are so game changing that the negatives of using it the wrong way are equally worse. So how do you use ML correctly?

‍

The best cases of ML used in business practise starts with a great plan, you'll need to know in which part of your business process the ML should be applied, what problem is expected to be soleved by using ML and how, and then everything there is to know about the data input. Your ML algoritme is only as good as your knowledge about the data input.

‍

When using ML in your decision making process you should always question the output by the data input, keep in mind that the ML algorithm acts accordingly to the reliability of the data input. What does that mean? well it means that the ML algoritme produceres an ansvar to your problems but it's up to you as an executive whether to use it or not or maybe even partly. The biggest mistakes of using ML in business practises arises when executives or employees stops thinking themselves, which might by tempting in cases where the AI is suppose to do most of the thinking.

‍

- Danijel Clarke, DevOps process engineer at NordicSolutions