This user's problem is not unusual. It is the quiet, everyday crisis of legacy data, and it is far more common than most people realize. Twenty Excel files, a thousand rows, columns that were designed decades ago for a screen and a workflow that no longer exist. The data is there, but it is trapped, stacked, merged, and formatted in ways that make it impossible to query, analyze, or trust. Our opinion is straightforward: this is exactly the kind of friction that modern spreadsheet tools should eliminate, and the fact that it remains a manual headache is a failure of imagination, not of technology.
The user's request is simple in concept but brutal in execution. They need to turn four merged columns into eight clean ones, and they need to do it across hundreds of rows without copying and pasting every cell by hand. Traditional spreadsheets treat this as a formatting problem, offering split functions or text-to-columns wizards that work well for tidy data but choke on the inconsistencies of old files. The real issue is that the tool assumes the data is already structured. When it is not, the user is left to write complex formulas, learn VBA, or beg for help on forums. That is not a skill gap. It is a product gap.
What this user needs is a tool that understands the intent behind the data, not just the text in the cells. An AI-native spreadsheet can look at the stacked columns, recognize the pattern of pipe characteristics repeated across rows, and offer to restructure them into distinct fields. It can learn from the first few files and apply the same logic to the remaining nineteen. It can flag anomalies, cells where the pattern breaks, or where old manual entries introduced typos, and ask for confirmation instead of silently corrupting the dataset. The goal is not to automate a tedious task. It is to make the task disappear entirely, so the user can focus on what the data means, not on how to pry it apart.
The lesson here is that the value of a spreadsheet is not in its grid. It is in the questions you can ask of the data once it is clean. This user is not asking for a faster way to split columns. They are asking for permission to treat decades of institutional knowledge as something usable, something that can be queried, visualized, and shared. That is a reasonable request, and any tool that cannot deliver it is holding them back. The solution exists. The only question left is whether the user will find it before they give up on the data entirely.