Maybe some devs here can help me, I was recently promoted to “head of AI” at my work despite being very outwardly ambivalent towards it. So I’m struggling to figure out what would actually create value instead of just being an expensive waste of time but still satisfy the higher ups AI lust.
My first idea that I thought would actually be useful was just setting up the architecture for an actual analytics database for us and then let them explore it with metabase (then letting them use Claude for their wow factor of exploring it with AI or whatever).
But now I’m somewhat at a loss, so any insight you all have would be really helpful!
May want to consider setting up a private, on-prem system. That way, you can reliably enforce privacy/GDPR rules. You can also tweak the system to support local training, RAG, MCPs, etc.
This way, the costs can also be controlled. It’s some capital investment in local hardware, plus reasonably fixed power/cooling/maintenaance ongoing expenses.
Another way is to use the major AI services for planning/brainstorming specific features, tell it not to implement or touch anything, but to generate a detailed plan for an implementer LLM. Review that plan manually, and when ready, feed it to your local system for implementation and debugging.
This doesn’t work if the goal is one-shot vibe-coding. But it works really well for focused feature enhancements, test coverage, and bugfixing.