Reviewed 250+ real AI implementations, a few things surprised me...

hey there, I keep seeing the same questions pop up everywhere: how are companies actually using AI? What's working, what isn't, which tools are people picking, which verticals are moving faster? I got tired of guessing so I started collecting real use cases from real companies. Not the hype stuff, just what they actually did and what came out of it. It's up to ±250 cases now, and you can filter by industry, tool, business function, etc. Some early findings: \- Engineering and Finance are moving the fastest by a pretty wide margin \- Logistics and manufacturing look like they're behind, but I think it's just that those projects take longer to implement and show results, not that nothing is happening... \- There seem to be 3 implementation patterns: a layered approach (LLMs + orchestration + apps), or full end to end solutions that abstract the LLMs from the user entirely. Advanced orgs are doing a hybrid of both. \- In terms of outcomes, speed (14%) is the most common, while workforce reduction and revenue increase are less common (less than 4% each). Link to the cases DB in the comments... Curious if any of this lines up with what you're seeing?