Operational reporting was manual, inconsistent, or in some cases nonexistent. We couldn’t easily see team capacity, understand what was happening across projects, identify bottlenecks, or assess the costs and profitability of client work. And because the project data hadn’t been captured consistently before the ClickUp rollout, we didn’t have a reliable starting point for numerical comparisons.
Once the ClickUp implementation was in place and people were entering data more consistently, the next challenge was making that data useful. I worked with leadership to understand what they wanted visibility into, checked what the data could actually support, and built reports around those questions.
I used Google Apps Script and AI to turn ClickUp data exports into structured reports for profitability, payroll/time tracking, workload, and project cost. I added custom menu actions so the reports could run easily on a regular cadence instead of being rebuilt manually each time.
Sample report that was made from raw ClickUp data.
Custom menu buttons to run the script with one click
ClickUp, Google Apps Script, Claude
After about six months of getting better data into ClickUp and building out the reporting, we had access to operational information we hadn’t previously been able to review in a consistent way. I could look at team capacity, see where projects were getting stuck, and dig into the processes behind those bottlenecks. Leadership could also look at the costs and profitability of individual client retainers when making decisions.
Note: I can't give a credible percentage improvement over the old process because there wasn’t a reliable baseline. The outcome was a repeatable way to turn project data into information that the team and leadership could actually use.
There was still room to automate more of the reporting process, potentially using AI agents or ClickUp’s internal agents. I had identified that as a next step, but I hadn’t implemented that phase before I left.