Transcript

Transcript

Transcript

The researcher now is able to take these reasoning models and apply them not just to the web, but to the entire richness of enterprise data. We’ve done the same for data analysis. Now, one of my favorite tools of all time has been Excel, and now we go beyond even Excel to create a data analyst where you can give it any arbitrary data. In fact, the most beautiful thing is you can give it 2 Excel spreadsheets and say go analyze and come back with all the insights. Let’s say I work in product development and we’re entering a new market, so I need help developing a product strategy for our expansion. I enter my prompt, and researcher immediately gets to work. You’ll notice it’s reasoning over all of my work data, not just one file. Just look at this thorough response – it’s something in line with what I’d expect from a researcher on my team. And now I can edit this response and pages and bring in my team to collaborate. Now that we’ve got a great product development strategy in with researcher, let’s turn over to analyst. We built analysts to think like a skilled data scientist, so you can go from raw data to insights in minutes. Now I have this really messy, highly complex data set – you can see thousands of rows and multiple tabs with customers and their monthly revenue, and none of this has been cleaned or contextualized. Usually, to make sense of this data, I’d have to pull in a member of my team who knows Python, but we’re gonna give the analyst agent a try. I’ll simply ask Copilot to help me come up with a way to easily understand, learn, and visualize my data. And here I’ve got the answer I was looking for with an electric visual to share with the team.