This is the kind of data problem that looks simple on paper and turns into a quiet nightmare the moment you try to solve it. A Reddit user had 800 CSV files, each with a dozen common header columns for budget details, followed by up to 400 possible cost element headers that appeared in different names and orders depending on which budget items each file contained. Power Query's "combine and transform" feature only returned the columns that appeared in every single file. That left them with the common headers and nothing else. The entire exercise collapsed into a single, frustrating dead end.
Our take is straightforward: this is exactly the moment when traditional spreadsheet tools stop being useful and start being obstacles. The user was not doing anything wrong. Power Query is a capable tool for many tasks, but it was built for tidy, consistent data. It assumes that your columns will match up across files. When they do not, it defaults to the lowest common denominator. That is not a bug. It is a design limitation. The user needed a tool that could handle the mess, files with different column orders, different column names, and hundreds of possible headers that appear only when a budget exists. Power Query cannot do that without extensive manual scripting, and most people do not have time to write custom M code for 800 files.
This problem is more common than most people realize. Many organizations run into it when combining departmental budgets, project reports, or survey data from multiple sources. The files are structurally similar but not identical. The human instinct is to force them into a single schema, which either drops data or requires hours of cleanup. The better approach is to let the tool accept the variability. An AI-native spreadsheet can read each file independently, recognize that "Cost Element A" in one file is the same concept as "Cost Element A" in another, and build a unified table that preserves all 400 possible columns without demanding that every file contain every column. That is not a theoretical feature. It is a practical necessity for anyone who works with real-world data.
The user's experience should serve as a clear signal. If you are spending more time wrestling with your data tool than working with the data itself, the tool is the problem. The future of data management is not about forcing your files to conform to the tool's expectations. It is about tools that adapt to the shape of your data. The next time you have 800 files that refuse to play nice, do not spend another hour trying to trick Power Query into cooperating. Look for a solution that treats your data as it is, not as you wish it were. That is where real productivity begins.