We think this user's frustration is the exact reason AI-native spreadsheets exist. They want live EU funding data in their sheet, they need specific fields, and they need it to update automatically. That is not a complex request. It is a standard workflow. Yet the conventional approach, fumbling with APIs, writing custom scripts, maintaining fragile connections, turns a simple need into a technical ordeal. This is not the user's fault. It is the tool's limitation.
The gap here is not about coding ability. It is about design. A traditional spreadsheet treats data as something you manually place in cells, then manually refresh. The moment you need a live feed from an external source, you are pushed out of the spreadsheet and into a separate ecosystem of connectors, authentication tokens, and error handling. Most people do not have time for that. They have proposals to track and deadlines to meet. What they need is a spreadsheet that understands the internet is a data source, not a separate problem to solve.
An AI-native approach changes the conversation. Instead of asking a user to learn an API, the tool asks what data they want. The user types, "Pull all active Horizon Europe calls with a budget over €5 million and update weekly." The AI interprets the request, negotiates the API, and populates the sheet. When new proposals appear, the data updates automatically. The user never sees a line of code. They never debug a broken connection. They simply get the spreadsheet they needed yesterday.
This is what progressive data management looks like. It is not about adding more features to an old framework. It is about rethinking the starting point. The starting point should be the user's goal, not the tool's constraints. For anyone wrestling with live data imports, the real question is not "How do I make Excel talk to an API?" It is "Why should I have to?" Explore a solution that answers that question by removing the barrier, not by teaching you how to climb it.