If you are manually refreshing a Power Query dataset that takes five to ten minutes each time, you are wasting time that could be spent on higher-value work. This reader's situation is not unusual: a consolidated file with 100,000 rows today, heading toward 500,000 by year-end, shared across a team. The manual refresh is a bottleneck, and the request for an automated morning refresh is entirely reasonable. The real question is why anyone should have to ask.
The traditional approach, Power Query in Excel, scheduled refreshes via Power Automate, or VBA scripts, works, but it is fragile. It depends on the file being open, the network being stable, and the credentials not expiring. When the file is shared across many users, the risk multiplies. One person leaves it open overnight, and the automation fails. Another user has a different version of Excel, and the query breaks. The reader is solving a technical problem that should not exist in the first place.
This is where AI-native spreadsheet tools offer a cleaner path. Instead of scheduling a refresh within a desktop application, the data lives in a cloud-native environment where refresh happens automatically on a schedule or on demand, without requiring anyone to open a file. The dataset grows from 100k to 500k rows, and the system scales without manual intervention. The team accesses the same live data, not a static snapshot that someone remembered to refresh at 7 AM.
Our take is straightforward: stop treating your spreadsheet like a database with a manual pump. The reader's current workflow is a sign that the tool is no longer serving the task. The solution is not a better macro or a more reliable script. It is a fundamental shift to a platform where data refresh is a background service, not a morning chore. For anyone managing a shared file that grows by thousands of rows each month, the path forward is clear: automate the refresh by moving the data to a system that does not need you to open a file to keep it current.