We see this question often enough that it signals a real gap in how people use traditional spreadsheets. The user here is trying to filter by investment name, then surface the highest weight in each of four categories across quarterly updates. That sounds straightforward, but the spreadsheet tools most people rely on are not built for this kind of temporal, multi-category comparison. You end up writing nested formulas, manually checking dates, or maintaining separate sheets for each quarter. None of that is productive work. It is maintenance.
What this user is describing is a data-modeling problem, not a formula problem. The data has a clear structure: an investment, a date, four categories, and a percentage weight for each. The question is always "what was the primary exposure at this point in time?" That is a query, not a calculation. Traditional spreadsheets ask you to reshape your data into a grid and then write logic to extract the answer. AI-native tools can treat the data as it is, ask the question in plain language, and return the result without the intermediate step of building a lookup table.
The practical takeaway here is that the complexity you feel is not your fault. Spreadsheets were designed for static tables and manual updates. When your data changes quarterly and you need to compare across time, you are fighting the tool instead of using it. The solution is not to learn more arcane functions. It is to use a tool that understands time as a dimension, not a column. Filtering by investment name and asking for the highest weight per category per quarter should be a single step, not a project.
Our view is straightforward: if you are spending more time maintaining your spreadsheet than analyzing your data, the tool is the bottleneck. This user's problem is common, but it is solvable without more formulas or manual work. The answer is to adopt a system that treats your data as a living set of relationships, not a static grid. Stop building workarounds. Start asking questions directly.