Not very sure about the AI in competitions, as I have not taken part in any competitive competitions. On comparing AI in Academia and Industry, the biggest difference is probably freedom.
In academia, considering a research project or so, a large number of experiments and trying new things are encouraged. New learnings are heeded to, and it usually involves rigorous literature survey and studies of previous works. Even if a model performed badly, if there were new learnings one could take from it, it wouldn't be deemed a failure. There is also a lot of data available that could be used for research purposes, and open-source projects used or learned from, are always thanked and appreciated.
In industry the scene is quite different. There is more of a focus on using pre-trained models or transfer learning. Quite frequently, open-source projects are just cloned, mildly developed, and deployed under the companies name without releasing the code - basically requiring bare minimum effort towards literature. More of a focus was given (In my case at least) on reading blog posts and readme's, over the papers themselves, in order to save time. And compute efficiency is key. In industry, the effort is more directed towards scaling these models, building the data pipelines, and satisfying the clients needs. Data is also another concern in industry, with it being common practice to outsource data collection and preparation to third parties (Usually other companies that specialize in this area).
The key difference, I would say, is the amount of freedom one has in academia, as compared to a strong sense of direction towards a singular goal in industry. AI in industry pretty much mostly is in the solutions-and-services sector (mostly), making it quite similar to software engineering, broadly speaking.
So, summarizing, the domain of the AI project makes a big difference, with the main difference being what part of the project most effort and focus is put into.