We have a straightforward opinion on this: the approach to cleaning data like "John Doe (123456799)" should be as simple as the problem looks, and traditional methods have made it far harder than it needs to be. The user who posted this question knows what they want, just the name, nothing else, but the tools they have been using demand a patchwork of formulas, nested functions, and manual wrangling. That is not a skill gap. It is a tool gap.
What this means in practical terms is that the time you spend separating text from numbers in a cell is time you could spend analyzing the data itself. The old way involves LEFT, RIGHT, FIND, or even regular expressions if you are comfortable with them. Each formula is brittle. A slight change in the data format, a middle initial, a hyphenated last name, a different delimiter, and the whole thing breaks. You end up maintaining the formula instead of working with the information. That is not productive. That is maintenance work disguised as spreadsheet expertise.
AI-native spreadsheets change this equation. Instead of telling the tool *how* to extract the name, you tell it *what* you want. A simple instruction, "remove everything except the employee name", becomes a functional transformation. The AI handles the pattern recognition, the edge cases, and the formatting. You get the clean data without writing a single function. This is not about replacing your judgment. It is about removing the friction between your intent and the result.
For anyone who regularly imports employee lists, customer records, or any dataset where names and identifiers are combined, this shift is immediate. You stop debugging formulas and start exploring what the clean data reveals. The user who asked this question deserves an answer that does not require them to become a formula expert. They deserve a tool that understands the task. That is what AI-native spreadsheets deliver: not a better way to write formulas, but a way to skip them entirely.