This is exactly the kind of friction that makes people question whether their data is working for them or against them. When you import clean numbers from a website and your spreadsheet refuses to treat them as numbers, the tool has failed its primary job. The user who posted this knows what the fix is, double-click, press Enter, repeat, but that workaround is a confession of bad design. You shouldn't have to trick your own software into doing the obvious.
What makes this problem worth discussing is how ordinary it is. Anyone who has pulled data from a web source has encountered this: the numbers look right, they align to the left instead of the right, and every formula or pivot table you try to build ignores them. The standard advice, multiply by 1, use Paste Special, apply a VALUE formula, works, but it assumes you have time to learn a workaround for a problem that should not exist. The user here has a pivot table to build. They have analysis to run. They do not have time to become an expert in text-to-number conversion quirks.
The deeper issue is that traditional spreadsheets treat data types as an afterthought. They guess whether something is text or a number, and when they guess wrong, the burden falls entirely on you. An AI-native approach flips this. Instead of expecting you to diagnose why your numbers are stuck as text, the tool should recognize the pattern, imported column, numeric content, text formatting, and offer to correct it in one step. Or better yet, correct it silently before you even notice. That is not a luxury feature. It is the baseline for a tool that claims to handle data.
The practical takeaway here is simple: do not accept this friction as normal. If your spreadsheet cannot distinguish text from numbers without manual intervention, it is not serving you. Look for tools that understand context, that see what you are trying to do, and that remove the gap between importing data and using it. You have better things to do than double-clicking two hundred cells.