Common internal use cases for Generative AI?

Great question -- given how much perceived (and real) impact genAI is having on the workplace, this is an area where every company will change dramatically.

I don't have a great answer beyond what Alex said, but I'd start my exploration by simply interviewing engineers at other companies.

ie. Don't do first-principles thinking when you're starting out, do "2nd-principles thinking" and just copy the roadmap of other respectable companies.

Using GenAI to supercharge documentation is a common use case.

You can audit all of the internal tools inside of your company to see whether they can be improved with GenAI.

GitHub copilot is the big one for coding. ChatGPT is solid for coding as well (but it's not as integrated as copilot obviously), but I think it has a lot of value for folks who have English as a 2nd language (i.e. most software engineers) to compile written communication (project updates, emails, etc).

My recommendation for folks using ChatGPT to write English is to specify a compressed length. By default, ChatGPT is very verbose with a lot of "I need to hit the page requirement for my college essay" energy.

Check out this other thread too: "What are the top 3 generative AI tools/use cases that can boost productivity?"

Is your directive to use GenAI more or to improve efficiency overall? A lot of companies get over-excited about GenAI as the end-all solution to their problems. But, it's important to use the right tool for the job.

I would start by focusing on the problems. What friction do the company and its developers currently have? What are the highest-impact areas for improvement?

Then, find the best way to make those improvements. Maybe it's a process update, maybe it's building automation tools and maybe it's something else. GenAI may be helpful in some cases but not others.