A few habits make AI Analytics noticeably more useful day to day. This article covers the most common ones.
Know what time window you're looking at
AI Analytics answers over a recent rolling window of your data rather than a range you type into the question. Check the time window shown alongside an answer before comparing it to a specific period you have in mind, so you're not comparing numbers across different windows without realizing it.
Use follow-up questions instead of starting over
If an answer prompts a "why," ask that directly as a follow-up rather than framing an entirely new question from scratch. Follow-ups retain the context of the previous question, which usually produces a more directly useful answer.
Check the sourcing before acting on a surprising answer
If an answer is unexpected — a number much higher or lower than you'd guess — check the underlying metric and time window shown alongside it before assuming something is wrong operationally. Occasionally a surprising answer reflects a misunderstood question rather than a real change in the data.
Don't ask questions outside your connected data
Questions about data from a source you haven't connected to the workspace won't have an answer to draw from. If you're missing a data source relevant to a question you want to ask regularly, connecting it is usually worthwhile.
Share useful questions with your team
If a question turns out to be one you ask repeatedly, it's often worth turning into a permanent dashboard widget as well — AI Analytics is best suited for ad hoc and exploratory questions, while a recurring question you check daily may be better served by a stable dashboard view.