The short comparisons of the last sections between waterfall project management methods and SCRUM could theoretically be related to all IT outsourcing projects in general, but due to the proximity between software development projects and A.I. projects, it is important to look at this topic additionally.
Something that is very peculiar to A.I. projects, however, is the additional level of processes, because data is often generated, modified, deleted and stored in a chain of process steps. Moreover, these process steps are also often linked to business processes, where machines and humans generate data together.
Neither SCRUM nor “normal project management” pay special attention to processes in their frameworks, so there are generally no tools or methods available to adequately capture them. Anyone who has ever tried to capture living processes in a company in a formalized way knows that it is not easy. On the one hand, because it feels like every employee carries out the process differently and, on the other hand, people find it very difficult to express their work in formalized process terms. But since the cooperation of the employees is incredibly important in order to represent processes correctly, and to understand where which data is being collected (or not), the process manager needs special tools and skills to get the right information from the employee in all its fuzziness.
Furthermore, it is important to ensure that, in addition to a process definition that has been validated several times, each process step can also be expressed in numbers/data, because an A.I. project thrives on working with (good) data. This may sound banal, but quite often terms are not defined from the business side and then silently interpreted by the data scientist in the fourth link. This, as you can imagine, quickly leads to distortions when never questioned in later live operations these terms. Let me explain this in a little more detail. Imagine that you as a business owner have not defined the term customer satisfaction or have not defined how low customer satisfaction differs in numbers from high and medium customer satisfaction. Imagine that the Data Scientist, as a frugal person, assumes anything above 65% as high customer satisfaction, while in your heart you actually assume above 90%. In the later dashboard, everything would then be green, while it is actually on fire. A classic melon problem: green on the outside – red on the inside.
In the next section we will deal with a special but important form of process management – process optimization.