Efficiency is the headline, but it is not the whole story. The recent piece on measuring AI value makes a necessary correction to the conversation: if you only track how many minutes a tool saves, you are missing the real returns. We agree, and we think the practical implication for anyone working with data is more significant than a simple productivity boost.
Efficiency gains are easy to count. A task that took an hour now takes ten minutes. That metric feels concrete, so it becomes the default justification for adopting AI tools. But this narrow view ignores where AI creates durable value: in enabling decisions that were previously too complex or too time-consuming to attempt. When a spreadsheet can surface patterns you did not know to look for, or when it can simulate outcomes across dozens of variables in seconds, the value is not just time saved. It is insight gained. For the user, this means the difference between finishing a task faster and fundamentally changing what you can accomplish with your data.
This distinction matters because it changes how you evaluate tools. If you measure AI value only by efficiency, you will gravitate toward tools that automate existing workflows. Those tools have a place, but they do not transform your relationship with data. The more interesting opportunity is in tools that expand what you consider possible. A traditional spreadsheet asks you to know the question before you start. An AI-native spreadsheet can help you discover the question itself. That is not a faster version of the old process. It is a different process entirely.
So our take is straightforward: stop treating AI value as a speed metric. Start asking whether the tool helps you see something you would have missed. The next time you evaluate a spreadsheet or data tool, look past the time-saved numbers. Ask what it enables you to explore that you could not before. That is the measurement that will determine whether the tool changes your work or just speeds it up.