People use data in different ways.
Sometimes you have a quick question and want an answer. Sometimes you’re exploring to understand what’s happening before a meeting or to support a decision. Sometimes you’re pressure-testing an idea, building an argument, or simply checking in on performance to see what stands out. Other times, you already know you want to build a dashboard or report for ongoing monitoring.
Insights supports all of these workflows through two AI entry points:
AI Assistant for open-ended exploration and flexible analysis
AI Query Helper for structured content creation inside workbooks
Each supports a different way of working. You can use them independently or together depending on what you’re trying to accomplish.
If You Like to Think in Questions and Explore Freely, Use the AI Assistant
What it is
The AI Assistant is a conversational interface designed for flexible, open-ended work. You can ask questions naturally, follow your curiosity, and move across datasets such as Bookings, Pick-up, Pricing, STR Benchmarking, Forecasts, and more within a single conversation.
It supports multiple ways of working, from quick answers to deeper investigation, with the option to pivot into building dashboards or reports when and if that becomes useful.
How people use it
Explore and stop when you have clarity
Sometimes the goal is simply understanding.
You might use the AI Assistant to:
Answer a quick question
Sense-check assumptions
Prepare for a meeting or discussion
Explore performance to see what stands out
Build confidence in a decision
In these cases, the value is clarity in the moment. There’s no requirement to turn the analysis into saved content.
You can also upload documents directly into the conversation to support this type of exploration. For example:
Upload a marketing calendar or campaign plan to understand how promotions may have influenced demand.
Upload a departmental performance summary to provide broader context when reviewing overall results.
Upload notes or external data to help validate assumptions or frame follow-up questions.
Uploads become part of the conversation context, helping the assistant reason across both Insights data and external information.
Example questions:
“How did occupancy perform yesterday compared to last week?”
“Are we pacing ahead for the next 30 days?”
“Did demand shift after this promotion started?” (with a campaign document uploaded)
You ask, you understand, and you move on.
Explore first, then pivot into building content
Other times, exploration reveals something worth operationalizing.
You might:
Start with broad questions
Follow patterns across datasets
Narrow into specific time periods, segments, or properties
As the picture becomes clearer, you may decide the analysis should live on as a dashboard or report for ongoing use.
At that point, you can use Create dashboard from chat in the left-hand side panel to turn the conversation into a starting dashboard. This can include:
Queries built iteratively through back-and-forth conversation
Multiple analyses that together tell a cohesive story
You’re not starting over. You’re formalizing what you’ve already explored.
Start with the intent to build a dashboard or report
Some users come into the AI Assistant knowing they want to create content quickly.
In these cases, the assistant can act as an accelerator:
You can describe the analysis you want and let the assistant build multiple related queries in one conversation, or
You can upload an existing document, report, or set of visuals and ask for it to be recreated or adapted in Insights
This is especially helpful when:
Translating an external report into Insights
Rebuilding dashboards from another tool
Moving quickly from idea to first version
Because the AI Assistant can generate multiple related analyses at once, it’s often faster for assembling an initial dashboard or report structure than building each analysis individually.
As always, the resulting content should be reviewed and refined to align with your data definitions and analytical intent.
Why this might suit you
The AI Assistant works well if you:
Think in questions and dialogue
Want flexibility in how analysis unfolds
Sometimes just need answers, not saved content
Sometimes want to move quickly from exploration to a full dashboard
Prefer to assemble multiple analyses in one flow
If You Like Structure and Hands-On Control, Use the AI Query Helper
What it is
The AI Query Helper lives directly inside workbooks and helps you translate plain-language requests or uploaded references into structured analysis that becomes the foundation for dashboards and reports.
It gives you visibility into how the analysis is built and allows you to refine it precisely.
How people use it
Start a new analysis in the way that feels fastest
You can:
Describe what you want to build in natural language, or
Upload a document, screenshot, or exported visualization and ask the AI Query Helper to recreate a comparable analysis in Insights
This is especially helpful when you already have a clear idea of what you want to build or a specific example you want to reproduce.
Iterate and refine intentionally
Once the initial analysis is created, you can:
Adjust dimensions, measures, filters, and comparisons
Add calculations or additional context
Fine-tune structure and formatting
This ensures the analysis reflects your business logic and analytical intent.
Maintain durable content over time
Saved views become reusable building blocks for dashboards and reports that can be updated as your business evolves.
Why this might suit you
The AI Query Helper works well if you:
Know what you want to build
Prefer transparency and control
Regularly maintain dashboards and reports
Want precision in how analysis is structured
How the AI Assistant and AI Query Helper Differ
Both interfaces can help you explore data and build content, but they support different working styles.
AI Assistant is conversational and flexible. It supports open-ended exploration across datasets and can generate multiple related analyses in a single flow.
AI Query Helper is structured and focused. It helps you build and refine one analysis at a time with full visibility and control inside a workbook.
Many teams use the AI Assistant to shape the direction and scope of analysis, then use the AI Query Helper to refine individual analyses and maintain long-term content.
Many Teams Use These Together
There’s no single correct workflow. Teams move fluidly between these entry points depending on intent and timing.
Common patterns include:
Explore, then formalize
Use the AI Assistant to explore patterns and frame the problem, then move into the AI Query Helper to build reusable dashboard content.Translate existing materials into Insights
Upload reports or visuals into the AI Assistant to understand how they could translate into dashboards, then refine individual analyses in the AI Query Helper.Build directly when the outcome is clear
Go straight to the AI Query Helper when you already know exactly what you need.
Insights is designed to support how you prefer to work, not force a single path.
Summary
AI Assistant supports flexible exploration, cross-dataset reasoning, contextual thinking, and optional movement into building dashboards and reports.
AI Query Helper supports structured analysis, precision, and building reusable content with visibility and control.
Uploads strengthen both exploratory and creation workflows by adding external context and accelerating translation of existing materials into Insights.
These entry points can be used independently or together based on how you like to work.
The goal of AI in Insights is to reduce friction between curiosity, analysis, and action so you can move faster and make better decisions.