The user who posted this question has already solved the hardest part of the problem. They have functional, consistent data across twelve identical tabs. Their leaders want a yearly total. Most people would stop there, paste a SUM formula across a thirteenth sheet, and call it done. But this user wants more. They want the file to *pop*. That instinct is not superficial. It is the difference between a tool that gets tolerated and a tool that gets used.
We see this all the time. Teams build spreadsheets that work perfectly on the logic level but fail on the human level. A plain grid of numbers communicates nothing about priority, trend, or context. When you open a file and see twelve tabs of identical gray cells, your brain has to do all the work of pattern recognition before it can do any analysis. That friction adds up. Asking for a single tab with all months is not just about aesthetics. It is about reducing cognitive load. It is about making the data tell its story at a glance.
The practical solution here is not more formatting tricks within Excel. It is a structural shift. A single tab with all twelve months requires a data model, not a copy-paste layout. One column for the month, one for the team, one for the overtime hours. That structure unlocks pivot tables, charts, conditional formatting that actually means something, and the ability to filter by any dimension instantly. The yearly totals become a simple sum of a filtered range instead of a manual cross-tab reference. The user is asking for a template, but what they really need is a framework that treats their monthly data as a single dataset rather than twelve independent islands.
This is where modern spreadsheet tools earn their keep. An AI-native spreadsheet can take those twelve identical tabs and flatten them into one view automatically. It can suggest the best visualization for a year-over-year trend without the user having to learn chart settings. It can flag months where overtime spiked and let the user click into the detail without building a dashboard. The user wants the report to pop. That happens when the tool handles the structure so the user can focus on the insight. Our take is simple: stop fighting the format. Redesign the data. The polish will follow.