How Rovo helps teams start work 30% faster

This post details the analytical methodology used to measure the impact of Rovo AI on user productivity within Jira. It defines 'Lead Time to Start' (time from creation to 'In Progress') and 'Number of work items moved into In Progress' as key metrics. A quasi-experimental design with control and test cohorts (regular Rovo users vs. non-users) was employed. Filters included paid Jira licenses (premium/enterprise), 50-1000 seats, and user tenure of at least 180 days. Winsorization excluded the bottom and top 10th percentile of 'Lead Time to Start' values. A normalized timeline ('Day 0' for Rovo adoption, 'Day 45') was used, with control group 'Day 0' dates randomly assigned from the test group's distribution. A two-sided t-test with significance 0.05 and power 0.8 was used for comparison. Findings indicate a statistically significant 30% improvement in work items moved to 'In Progress' and a 35% reduction in 'Lead Time to Start' for Rovo AI adopters.