If you defined "AI skills" as being able to take some cleaned data and plug it into some python library to solve some pre-defined objective a la kaggle, then su

If you defined "AI skills" as being able to take some cleaned data and plug it into some python library to solve some pre-defined objective a la kaggle, then sure, thats no more or less easy than any other kind of data transformation. The difficult part is coming up with said data and objectives, being able to ask the right questions and integrate it into an actual monetizable product. There is still a huge long tail of applications that these classes of algorithms promise, even if the number of PhDs optimizing these algorithms against ImageNet or MNIST plateau. I see "AI skills" as more of a form of literacy as opposed to python-notebook-jockeying. That is, having a general knowledge of what these algorithms are good for, being able to recognize when and how a classifier or a regressor could help, and compiling the data necessary to accomplish it. I work at an "AI" startup as an engineer, mostly on the applications side. We have a human analyst team that annotates our data for us, with the idea that we will eventually supplement that team with a classifier that will help us scale the operation with a similar team size. Data quality is certainly a challenge, and a lot of that is because the in-house app that our analysts use isn't very flexible, and they have to keep track of much of their work with excel spreadsheets, which adds a lot of mental overhead to their jobs. Additionally, a lot of the work is necessarily subjective, and the analysts have to move quickly through the data to annotate it, which leaves room for error. I realized that by improving the UX of our internal apps with good ol' application design/engineering, we can improve our data quality, and thereby improve our model. I consider that an "AI skill" even if I never have to touch a python notebook to accomplish it.