I once developed an „AI“ trying to predict when JIRA tickets in our startup get resolved. It was mainly a system based on k-nearest neighbor regression, predict

I once developed an „AI“ trying to predict when JIRA tickets in our startup get resolved. It was mainly a system based on k-nearest neighbor regression, predicting the closing date based on the average of k similar tickets (we had more than 1000 closed tickets already). It worked reasonable well. One of the most important features was the person creating the ticket. Some people wrote tickets really well and the tickets often got resolved in 48h. Others just wrote one cryptic sentence and nothing happened with these tickets for weeks. But you need a lot of high quality data for that. Since that time I realized that writing wellformed tickets and also changing their state on time is really important for such tools. Modern tools actually don‘t use KNN, but some of them use Monte-Carlo-Simulation to estimate work: https://kanbanize.com/kanban-resources/kanban-analytics/monte-carlo-simulation https://kanbanize.com/kanban-resources/kanban-analytics/mont... https://www.joelonsoftware.com/2007/10/26/evidence-based-scheduling/ https://www.joelonsoftware.com/2007/10/26/evidence-based-sch... I believe used correctly these tools work better than all those scrum-pokers and t-shirt size manual estimation.