My use case is data science, but also do analysis and often have to do my own data engineering work to get the data.
I'vs used to streamline analysis, document things so I can run it again in the future (i'm ass at this). Eventually I think I can get it to automatically write reports and push info to people. Much better than dashboards, I hate dashboards more than anything.
The DE stuff I've been using to quickly set up my API pulls, transform the data as needed, dump it to dbt etc. Pretty basic but saved me a ton of time and makes it easier to get the real DE team to put it in the actual warehouse.
Then running ml models, iterating, testing out ideas. It was fast before simply using the AI tools, but super fast now with it plugged directly into my IDE.
One of my tools I basically:
Built a python tool that could connect to a db, then execute SQL scripts and then had a few options for exporting. Easy to do, but that allowed me to tell the agent to use that tool, and then just point it to code, or have it generate code and run it. I got tired of explaining what it needed to pull, so I turned it into a skill and added another tool that every week will go into the db rebuild it's own data dictionary and track how tables connect.. Then having it save any queries it runs for additional context.
Hoping to get it to the point where it understands what tables to pull from on it's own.
I'm not building apps or shipping code, but it's been a big help for me, can also run two at once and tab between projects when i get bored.
Ive gotten some good recognition off recent work I've done with it's help.
They're encouraging us to use AI and learn all of this so I'm diving all the way in.