Start with intent, not a spreadsheet
Traditional analysis often begins with opening files, filtering columns, and deciding which source to trust before the business question is even clear. Conversational analysis reverses that order. The team starts with the decision it needs to make β for example, which burger brands expanded in Riyadh β and the interface translates that intent into a structured exploration.
The answer still needs evidence
A conversational interface should not turn market research into unsupported prose. A useful answer keeps the underlying branch, menu, price, city, and dated observation connected to the result. That makes it possible to move from a short answer into the rows that support it instead of treating AI as a replacement for the evidence.
Follow-up questions are the real advantage
The first answer is rarely the final decision. Teams naturally ask what changed, where the pattern is strongest, which branches are driving the difference, and whether the same pattern appears in another city. Keeping that context inside one conversation reduces the repeated setup work that makes ordinary research slow.
What good looks like
The goal is not to remove analysts. It is to shorten the distance between a commercial question and a defensible view of the data. The best experience lets a user ask naturally, inspect the supporting evidence, refine the scope, and save or share the result with the team.
Use the summary as the starting point, while keeping the evidence and context available behind the result.