Lessons Learnt from Operational Deployment of Automated Machine Learning

It has been a while now since we have integrated intelligent features deep in some SAP applications such as ‘Opportunity Scoring’ and ‘Lead Scoring’ in C/4 sales, or ‘forecast delays in Stock in Transit’ for S/4 Sourcing and Procurement, just to mention two out of dozens.

Now, we have live customers leveraging these smart processes, and thanks to C/4 professional services teams, we can begin to draw some experience from these first operational deployments of automated machine learning. This demonstrates truly the Intelligent Enterprise in motion and corresponds to the ‘optimize’ way of delivering SAP Leonardo innovations through deep embedding within the SAP applications.

So, in a nutshell, here are the findings:

We will dive into each of these aspects in the following blog.

Let’s face it, we’re still in the time when business deciders need to build their own trust into machine learning systems where the machines will take the responsibility of making the operational or tactical decisions. In our example, opportunity scoring, the main decision to be made is: “Which opportunity should my team focus on to transform them as deals/customers and what should be done next for each of them?”

Most sales manager will not trust at first sight a machine-generated score that will rank their opportunities. So, we need to help these sales managers to take the decision to use these systems in operations. We are entering what is called ‘Explainable Artificial Intelligence or Explainable AI.’ What can we do?

Why are we doing this? Because the training process of these automated machine learning techniques are purely automated, and the more insights you show in a way that can be consumed by a business user, the more chances you will find unexpected results or insights that will trigger the right questions on how this data is filled or used during the business process. When the business stake holders have asked all their questions, they are ready to go live, but they need help to ask the proper questions and therefore it is crucial to show them as many findings as possible that have been extracted from mathematics.

Very often, when I talk about embedded machine learning automation, the first question that is asked is, “What algorithms are you using?” This is the wrong FIRST question.