Questions and responses in an exploration

  • Release version: Yokohama
  • Updated July 31, 2025
  • 4 minutes to read
    • Summarize
    Summarized using AI
    This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.

    Summary of Questions and responses in an exploration

    AI Data Explorer in ServiceNow Yokohama enables users to ask specific data-related questions within explorations, generating responses that include data visualizations, summaries, and suggested follow-up questions. This tool helps customers interactively analyze data from configured tables, enhancing data-driven decision-making directly within the platform.

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    How to Use AI Data Explorer

    • Launch AI Data Explorer from a data visualization, list, or existing exploration.
    • Enter your question about data from tables listed in the Query Generation Semantic Table Configuration.
    • Note that AI Data Explorer cannot access data from secure scopes.
    • After submitting a question, wait for processing before asking another; you can cancel the processing if needed.
    • The AI converts questions into database queries and returns the original question (editable), a response title with a summary, a list or visualization (existing or generated), and suggested follow-up questions.
    • Visualizations can be resized or repositioned within the exploration.
    • You can view technical source details of the response by hovering or selecting “View source.”
    • Responses can be deleted to maintain a clean exploration environment.

    Tips for Effective Questioning

    • Name your table: Reference the exact or partial table names to improve query accuracy.
    • Explain your meaning: Add detail to clarify your intent and get relevant results.
    • Be specific with referenced names: Use full display names for users, groups, or services to ensure precise filtering.
    • Edit and refine queries: Manually adjust filter conditions if initial results are unsatisfactory; the AI learns from edits within the same exploration.
    • Delete bad queries: Remove unproductive questions to prevent degrading response quality.
    • Import complex filters: For complicated data sets, import visualizations or lists from dashboards with pre-applied filters to guide AI questioning effectively.

    Additional Functionalities

    • Extended analysis: Perform deeper analytical queries to discover new insights.
    • Add visualizations to dashboards: Place AI Data Explorer visualizations onto dashboards without disrupting exploration workflows.
    • Refresh responses: Regenerate answers with updated data as needed.
    • Manage responses: Duplicate, delete, reorder, or move questions and answers within or across explorations.

    Benefits for ServiceNow Customers

    This capability empowers ServiceNow customers to interactively analyze data using natural language, streamline complex query creation, and visualize results efficiently. By leveraging AI Data Explorer’s features, customers can gain faster insights, refine data queries dynamically, and integrate visualizations into dashboards, enhancing operational intelligence and decision-making without requiring deep technical expertise.

    Ask the AI specific questions in AI Data Explorer, to which it responds with data visualizations, a summary, and suggested follow-up questions.

    To ask a question in an exploration, launch AI Data Explorer from a data visualization or list or open an existing exploration. You will see a field with the placeholder "Ask Now Assist a question about data." For more information, see Launch AI Data Explorer.

    Note:
    • The question you ask has to be about data in one of the tables listed in the Query Generation Semantic Table Configuration table. These tables can include database views and Workflow Data Fabric tables. For more information, see Add a table to the semantic data layer.
    • AI Data Explorer cannot access data from secure scopes.
    • When you have submitted a question, you cannot submit another question or do other work in the exploration until your question is processed. You can cancel the processing of your question.
    When you write a question in an exploration, the AI converts the question to a database query. The AI returns the following items:
    • Area 1 Your original question. You can edit this question to generate new output.
    • Area 2 The title of the response and a summarization of the AI findings.
    • Area 3 A list or data visualization. This response can be an existing visualization instead of a generated one. For more information, see Launch AI Data Explorer. You can change the height of the list or visualization by interacting with the double arrow icon in its corner. You can also drag a visualization to other places in the exploration.
    • Area 4 Suggestions for follow-up questions.
    You can select and drag these sections of the response to change their order.
    The response returned from a question to AI Data Explorer, showing the summary, data visualization, and suggested follow-up questions.

    Viewing the response source

    After you receive a response from the ServiceNow AI Platform, hover over the response to see the technical details of the response. The source details include the following information:
    • The source table
    • The filter conditions
    • The metric
    • Any grouping criteria
    If the exploration is too narrow on the screen, you select View source instead of hovering.
    Source details for a response in an exploration.

    Deleting a response

    If you expand the rectangle of dots before the beginning of the response, you have a button to delete the response.
    Button to delete response.

    Tips for asking questions

    The goal of AI Data Explorer is to understand your prompts in your own words, delivering the analytics insights you want. However, if you do not know where to begin to formulate questions, or you are unsatisfied with the results, here are some tips:

    Name your table
    If you know the name of the table that contains the data you are interested in, add it to your prompt. Partial names or similar names are fine too.

    Example: Instead of "How many P1s were opened this week,” write "How many P1 requests were opened this week," which references the request tables. Better yet, write "How many P1 catalog requests were opened this week," which references the specific Catalog Requests table.

    Explain what you mean
    Query Generation tries to understand your terms, but you can add details to help guide it. If you get unexpected results, try being more specific about what you're looking for.

    Example: Instead of "Show me all stale incidents," write "Show me all incidents not updated in 5+ days."

    Be specific with names
    When filtering by referenced records like users, groups, or services, try to use their full display names for best results. The AI model may learn from previous queries in the same document, but using full names ensures accuracy.

    Example: Instead of "Cases with Workplace Ops," write "Cases with Workplace Operations."

    Edit and refine queries
    If the generated query isn't quite right, you can manually edit the filter conditions. The AI model will learn from your edits and apply them to future questions in the same document. For more information, see Change the question or filter conditions

    Example: You ask "Show me critical incidents from the network team" but are not satisfied with the response. Instead of asking repeated variations of the same question, hoping for a better result, edit the filter to find records where Assignment Group is ‘Network Operations’ and Priority is ‘1 - Critical’. Then ask "Show me the inflow trend for these incidents over time”.

    Don't leave bad queries in your exploration
    The AI model uses the previous document context to write the next query. Therefore, if you cannot refine a query to get a useful response, delete it. Otherwise bad queries can accumulate in your exploration, leading to ever-worsening responses.
    Import complex filters
    For complex data that's hard to describe, import data visualizations or lists into your exploration. If the visualization or list is on a dashboard, you can apply any filters on the dashboard before importing. The AI model will use imported queries to understand related questions in the same document.

    Example: Don't ask "Show me servers about to retire by location." Such a prompt is vague and complex. Instead, import a visualization from a dashboard titled "PostgreSQL servers nearing retirement,” with the desired values for the dashboard filters Lifecycle State and Days Until Retirement pre-applied. Then ask "Show me the same servers but grouped by location”.

    Once you have a productive exploration going, with a lot of context, you may find that you are able to ask more abstract questions and get useful answers. However, these tips might help you get started.