The problem posted by FurryACiD is one we see every day: multiple spreadsheets, each with data arranged in a way that makes sense for its original purpose, but impossible to combine without hours of manual work. The request is straightforward, pull data from two tables in one file, plus data from other files in a folder, and reshape it into a single clean table. The frustration is not with the data itself, but with the tools that force users to become amateur programmers just to perform a basic consolidation. Our take is simple: this should not be this hard.
What FurryACiD is describing is a classic data wrangling task that Power Query can handle, but the learning curve is steep. You have to understand how to reference tables from different sources, how to merge or append queries, and how to pivot or unpivot columns to match the desired output format. Even for someone who knows Power Query exists, the path from messy input to clean output is littered with obscure error messages and trial-and-error. The time spent figuring out the correct M code or the right sequence of transformations could have been spent actually analyzing the data. That is a productivity tax that no spreadsheet user should have to pay.
The real issue here is that the spreadsheet industry has treated data consolidation as an advanced feature, when it is a fundamental need. Every business user who works with monthly reports, departmental budgets, or sales data from multiple regions faces this exact scenario. They are not asking for something exotic, they want to take structured information that already exists and bring it together in a logical table. The fact that this requires a Reddit post, a community discussion, and a deep dive into Power Query documentation means the tools are failing the people who use them. An AI-native approach would recognize the pattern in the user's request and generate the correct transformation automatically, without requiring the user to understand the mechanics of query folding or table joins.
The solution is not to make Power Query tutorials better. It is to build spreadsheets that understand what you are trying to do. When you show a tool a folder full of similar files and describe the output you want, it should be able to infer the transformation steps and execute them. That is what AI-powered queries can deliver: not a replacement for human judgment, but a removal of the technical friction that stands between you and your data. FurryACiD's post is a perfect example of why the old way of thinking about spreadsheets is no longer acceptable. The technology exists to make this process instant, and users deserve tools that meet them where they are, not tools that demand they become experts in query languages first.