Hey everyone — curious to learn from other teams here!

We’re building out a Case Closure Data Model as part of our Intercom + Fin AI rollout, with the goal of turning every resolved ticket into AI-ready data for automation, prevention, and knowledge training.

Our current thinking includes adding structured closure fields like:

• Technology / Sub-Technology

• Problem Code / Resolution Action / Root Cause

• Repeatability / Escalation / Customer Effort

• KB Linked

The intent is to improve data quality, reduce “Other” categories, and help Fin identify repeatable automation candidates over time.

I’d love to hear how others are approaching case hygiene at closure:

• Which closure fields have been most valuable for your AI or reporting goals?

• How do you balance structured data collection vs. agent friction?

• Have you seen any creative ways to tie closure data to Fin training or KB improvement?

Any best practices, examples, or lessons learned would be hugely appreciated.

— Lance