I have build a lot of PowerPoint decks over the years. Many of presentations, some for explaining a process, and once in awhile, I use it to create an image that's simple, but beyond my skills. Even putting some geometric shapes together in a clean, aligned manner, is something I've found easier to do in PowerPoint than Paint.Net.
Some of you may have noticed the PowerPoint Design Ideas that pop up when you create a new slide. At first I didn't pay much attention, but I've started to take advantage of the ideas at times. The way that slides are put together is often much more visually appealing than any I'd think of by myself. Sometimes I take an idea and modify it slightly. At first I thought most of these were just standard templates, but Microsoft is starting to use more ML in the examples that pop up on the side.
There's a good AI Show episode at the bottom of this post that explains some of what they do. From image recognition to intelligent cropping to natural language processing, they are finding ways to make better design ideas available. the episode is only minutes and worth watching if you want to know more about how they use technology in their recommendations.
To me, this is a good example of how technology and humans work together. Someone needs to come up with some good designs, based on certain situations. Users then provide them feedback on if they're useful or not, and they can modify things. I even learned how to change the icons for the designs if I don't quite like them. My changes (and yours) get fed back to new models as training for the future.
Would this make all presentations look alike? I think there is some danger here that we start to coalesce into certain patterns. We might see lots of slides with dates become timelines, but they won't be exactly the same, and there are different ideas based on content. Even the words in the titles. I made two slides, same content with a different title and I got slightly different recommendations.
I think AI and ML have good futures, but not as the sole decision makers for how to react to data. Instead, I think humans constantly providing feedback, input into models, changing weights, and yes, helping with more complex things like design, will improve the results that more of us get from our tools.