This is a problem that should have been solved years ago, and the fact that someone with intermediate Excel skills has to ask for a workaround tells you everything about the gap between what spreadsheets offer and what people actually need. The user's table has over 100 rows, with more added routinely, and they need to verify that equivalent cells match across the grid. A brute-force approach of nested IF statements is not feasible at that scale. That is not a limitation of the user's ability, it is a limitation of the tool.
What this person is describing is a data integrity check, and it is the kind of task that legacy spreadsheets treat as an afterthought. Conditional formatting rules can highlight mismatches, but those rules break or need manual updates when rows or columns expand. Named ranges and dynamic array formulas can help, but they require a level of formula engineering that most users should not have to master just to keep their data honest. The real solution is a system that understands the structure of your data and can enforce relationships without you writing a single IF statement.
This is where AI-native spreadsheets change the conversation. Instead of asking a user to manually define every equivalence, the tool can infer the pattern from the table layout and flag discrepancies automatically. When new rows are added, the check extends without configuration. The user's focus shifts from maintaining the validation logic to acting on the results. That is the difference between managing a spreadsheet and managing your work.
The user is not asking for a fancy dashboard or a predictive model. They want a simple, reliable way to know when their data is wrong. That is a basic human need in any data workflow, and it should be as easy as clicking a button. Any tool that makes that hard is holding you back. It is time to explore a solution that treats data integrity as a built-in feature, not a DIY project.