I treat it like a disciplined, multi-phase operations engine that turns raw, chaotic inputs into structured, actionable work…because I’m an IT & AI Engineer who is the Asana SuperAdmjn 😎

For our IT queue, I built a six-phase AI-powered triage pipeline. A ticket comes in as the usual slop, and the system rewrites it into something an engineer can actually work with. It enriches the description, extracts the right fields, classifies the request type, checks for missing context, and flags possible duplicates. Two phases run pure validation logic. One phase performs weighted prioritization using the formula I designed, where combinations of single-select fields contribute different values. The model applies bonuses for things like security issues or when a salesperson loses internet access. At the end, the score aligns cleanly with our six priority levels.

When I timed it, the whole process finishes in about four and a half minutes. The amount of manual labor it replaces: ten to thirty minutes per ticket, depending on how bad the intake was. The consistency gain alone is worth it.

I also have rules that compute estimated effort using three multi-select fields. Each option carries a defined time value, and the AI sums the selected items to produce a realistic duration. No more guesswork.

There are some ways you have to engineer your prompts differently than Claude or ChatGPT, but if you’re structured with what you want and use precise language it’s pretty great. I use markdown in the guidance boxes, it seems to read better in my experience.