# AI-assisted ticket refinement

Source: https://www.michaelnouriel.com/work/ai-assisted-ticket-refinement

## Problem

Engineering refinement sessions can begin with participants holding very different levels of context. Product, engineering and test specialists may spend a meaningful portion of synchronous meeting time reaching a shared understanding of a ticket before the useful discussion can begin.

The question was: does all context acquisition need to happen synchronously?

## System / intervention

Before a Three Amigos/refinement session, the workflow analyses the available ticket and relevant context through three perspectives: Product Owner, Developer and Tester. It produces a consistently structured preparation artefact covering scope and activities, conversation points, potential acceptance criteria, assumptions, dependencies, risks, estimates and tensions between the perspectives.

Participants still read the original material, review their own perspective and examine the others before discussing the ticket together. The preparation supports human judgement; it does not make the final decisions.

## Contribution

I designed an AI-assisted ticket-refinement workflow that moves part of the context-acquisition process upstream.

## Outcome

In the first live trial, three teams progressed seven tickets within a 60-minute session, compared with previous experience of progressing around two to three tickets in the same timeframe.

This was an observed early result, not a controlled measurement or proof that AI alone caused the difference. The historical comparison was reported experience, not an independently verified benchmark. “Progressed” does not mean the tickets were implemented, shipped or closed.

## Learning

> Remove low-value discovery. Preserve high-value discovery.

> AI prepares the conversation. Humans own the decision.

Michael's later account reports adoption across L2 and L3 engineering teams and Product Owner feedback that preparation improved access to context and helped surface relevant questions. Continued use and feedback should guide revisions and establish where the workflow helps, where it fails and how humans challenge its output.