Power BI Copilot

Power BI Copilot is Microsoft's LLM-powered assistant embedded directly in Power BI Desktop and Service. It generates report pages, writes and explains DAX queries, summarizes data, and answers questions in natural language. A product manager types "Create a page showing quarterly revenue by region with year-over-year growth" and Copilot generates a complete report page - chart selections, DAX measures, layout - in seconds.

Copilot is not the same as the older Q&A feature. Q&A uses keyword matching against the data model schema - a deterministic process with no large language model involved. Copilot uses Azure OpenAI large language models to interpret intent, generate DAX code, select visualization types, and produce natural language summaries. The distinction matters for understanding both capabilities and limitations: Copilot is more flexible but less predictable, and its output quality depends directly on how well your data model is documented.

What Power BI Copilot Does

Copilot has four core capabilities, each solving a different problem in the BI workflow.

Report page generation is the headline feature. You describe what you want to see - "Show monthly sales by product category with a trend line" - and Copilot creates a report page with appropriate visuals, axes, and formatting. It selects chart types based on the data structure: a line chart for time series, a bar chart for category comparison, a card for a single KPI. The generated page is editable, so you can adjust what Copilot produces rather than starting from scratch.

DAX query writing translates business questions into calculation logic. "Calculate year-over-year growth rate" produces a query using SAMEPERIODLASTYEAR and percentage calculation. "Show year-to-date revenue" generates a query with CALCULATE and DATESYTD. In DAX query view, Copilot writes and explains DAX queries, which you can run and then keep in the query tab. It does not add measures to the model by itself. For experienced DAX developers, this accelerates routine work. For less technical users, it removes the barrier of learning DAX syntax entirely.

Data summarization generates bullet-point summaries of report pages. Copilot reads the visuals on a page and produces a natural language summary of the key findings: "Revenue was $12.4M in Q3, up 8% from Q2. The Electronics category drove most of the growth, accounting for 42% of total revenue. The Southwest region underperformed with a 5% decline." This is useful for executive summaries and automated report distribution.

Conversational data exploration lets users ask follow-up questions about their data in a chat interface. After generating an initial view, you can ask "Which product had the highest margin?" or "Break this down by customer segment" and Copilot updates the analysis. The conversation maintains context, so each question builds on the previous answer.

How Copilot Reads Your Data Model

When you type a prompt, Copilot uses the data in your semantic model combined with your prompt to respond. Its main guide to that data is the model schema: table names, column names, data types, relationships between tables, existing measure definitions (DAX formulas), and any descriptions you have added to tables and columns.

This is the critical architectural detail. Copilot works with your data, but it understands that data through your metadata. For DAX queries, Microsoft documents that Copilot relies on model metadata, may use min/max sample values in Import mode, and reads only the first 200 characters of a description. It reads that you have a table called "Sales" with columns "Amount," "Date," "ProductID," and "Region," and that "ProductID" has a relationship to a "Products" table. From this schema, it infers what queries and visualizations make sense.

The implication is straightforward: vague metadata produces vague outputs. If your column is named "Col1" or "Amount_v2," Copilot cannot determine what it represents. If you have three columns that could mean "Revenue" in different tables with no descriptions to differentiate them, Copilot picks one - and may pick wrong. If your table has no description and no clear naming convention, Copilot has to guess the business context.

Contrast this with a well-documented model: a column named "Revenue_USD" with a description "Total revenue in US dollars, net of returns, from the Sales fact table." Copilot reads that description and uses it to generate accurate DAX and select the right column when you ask about revenue. Output from a documented model is noticeably better; Microsoft warns that unprepared models can give generic or inaccurate results.

Key Capabilities with Examples

Report page generation. Prompt: "Show monthly sales by product category for the last 12 months." Copilot generates a line chart with month on the x-axis, revenue on the y-axis, and separate lines for each product category. It adds a slicer for date range and a card visual showing total revenue. The entire page layout - visual positioning, formatting, color scheme - is generated automatically.

DAX generation. In DAX query view, prompt: "Show year-over-year revenue growth by year." Copilot generates a query along these lines:

DEFINE MEASURE Sales[YoY Growth %] = VAR CurrentYear = SUM(Sales[Revenue_USD]) VAR PreviousYear = CALCULATE(SUM(Sales[Revenue_USD]), SAMEPERIODLASTYEAR('Calendar'[Date])) RETURN DIVIDE(CurrentYear - PreviousYear, PreviousYear, 0) EVALUATE SUMMARIZECOLUMNS('Calendar'[Year], "YoY Growth %", [YoY Growth %])

You run the query to check the result, then select Keep query to keep it in the query tab. Copilot does not add the measure to the model; if you want it there, you add it yourself after reviewing the DAX.

Summarization. On a report page with five visuals, Copilot generates: "This page shows Q3 2025 performance. Total revenue was $12.4M, up 8% from Q2. Electronics led with $5.2M (42% of total). Southwest region declined 5% while all other regions grew. Customer acquisition cost dropped to $42, the lowest in four quarters."

Iterative conversation. After the initial report, you can refine: "Add profit margin to this view" → Copilot adds a visual or field showing profit margin. "Filter to just Enterprise customers" → Copilot applies the filter. "Why did Southwest decline?" → Copilot breaks down the Southwest data by product and time period to surface the drivers.

Prerequisites and Licensing

Copilot is not available on every Power BI license. The requirements are specific.

A paid Fabric F2+ or Premium P1+ capacity is required. Microsoft's requirements are explicit: trial capacities and free SKUs are not supported, and a Power BI Pro or Premium Per User (PPU) license alone is not sufficient, because Copilot needs organizational capacity. The workspace containing your report must be on that capacity. This is the single biggest adoption barrier - many organizations have Pro licenses across their user base but have not invested in Premium or Fabric capacity.

Admin setting. The tenant setting that allows Copilot is enabled by default, and administrators can turn it off. The standalone, full-screen Copilot experience needs additional tenant settings. Some organizations switch Copilot off and require a formal request process.

Region availability. The Fabric capacity must be in a supported region. If your tenant or capacity is outside the US or EU data boundary, Copilot stays off until an admin enables the tenant setting that allows data sent to Azure OpenAI to be processed outside your geographic region. Sovereign clouds are not supported.

Supported content. Copilot works in Power BI Desktop and Service, but report creation has limits: it cannot create report pages for semantic models connected live to Analysis Services, for real-time streaming models, or for models with implicit measures disabled, and it does not support custom visuals. The Q&A feature switch must also be on for the model.

Preparing Your Data Model for Copilot

This is the most practical section of this article. Copilot's accuracy depends heavily on your data model's metadata quality.

Use clear column names. Rename "field_27" to "Customer_Region." Rename "amt" to "Revenue_USD." Rename "dt" to "Order_Date." Copilot reads these names as its primary signal for understanding what each column represents.

Add descriptions to tables and columns. In Power BI Desktop, select a table or column in the model view and add a description in the Properties pane. Write what a business user would need to know: "Revenue_USD: Total revenue in US dollars, net of returns and discounts, from confirmed orders only." Copilot uses these descriptions as context, but DAX Copilot reads only the first 200 characters of each one, so put the definition first.

Use Prep data for AI. Power BI's current guidance is Prep data for AI (preview): an AI data schema, AI instructions that add business context (for example, that users say "sales" or "turnover" when they mean revenue), and verified answers that map common questions to approved visuals. Q&A Setup and linguistic schema synonyms retire together with Q&A in February 2027.

Build a proper star schema. Copilot navigates relationships between tables. A clean star schema - fact tables connected to dimension tables through well-defined relationships - produces much better results than a flat, denormalized table with 200 columns. Copilot can traverse relationships to answer questions that span multiple tables.

Use business-friendly measure names. Name measures "Total Revenue" or "Customer Count" rather than "M_Rev_01." Copilot uses measure names to decide which measures to reference in generated DAX and visualizations.

Consistent naming conventions. If some columns use CamelCase, others use snake_case, and others use abbreviations, Copilot has a harder time inferring patterns. Pick a convention and apply it consistently.

Copilot vs. Q&A vs. AI Insights

Power BI now has three ways to interact with data using natural language or automated analysis. They serve different purposes and use different technologies.

Q&A maps keywords and phrases to your data model schema. It is deterministic - the same question always produces the same result. It requires no LLM and is free and available to all users. The trade-off is that it is less flexible: if your question does not match the schema vocabulary, Q&A fails. It produces single visuals, not full report pages. Q&A retires in February 2027, and Microsoft positions Copilot as its replacement.

Copilot uses Azure OpenAI large language models to interpret intent more broadly. It can handle ambiguous questions, generate multi-visual report pages, and write DAX. It is more powerful but less predictable - the same prompt may produce slightly different outputs on different runs. It requires a paid Fabric F2+ or Premium P1+ capacity; Pro or PPU alone is not enough, and trials are not supported.

AI Insights (Key Influencers, Decomposition Trees, Quick Insights) are built-in ML visuals that analyze data automatically. They are not conversational - they scan the data and present findings. They use classical ML (regression, decision trees), not LLMs. The exception is the Narrative visual (formerly Smart Narratives), whose Copilot mode does use an LLM. Quick Insights runs in the Power BI service on Import models and returns up to 32 insight cards; the AI visuals are available in Power BI Desktop for all users.

When to use which: Q&A for quick lookups by power users who know the data model, until it retires. Copilot for report creation and DAX generation. AI Insights for automated pattern discovery and driver analysis.

Why Metadata Quality Determines Copilot Accuracy

Copilot's accuracy problem is not an AI problem - it is a metadata problem.

When column descriptions are missing, Copilot infers meaning from column names alone. A column named "Amount" in a Sales table could be revenue, cost, tax, or discount. Copilot guesses. If it guesses wrong, the generated DAX measure calculates the wrong metric, the chart shows the wrong numbers, and the user either catches the error (and stops trusting Copilot) or does not catch it (and makes decisions on wrong data).

When the same concept exists in multiple tables - "Revenue" in Sales, "Revenue" in Forecast, "Revenue" in Budget - Copilot picks one. Without descriptions explaining that Sales.Revenue is actuals, Forecast.Revenue is projected, and Budget.Revenue is planned, Copilot may mix them in a single calculation. The resulting DAX compiles without error but produces meaningless numbers.

This is where data governance directly improves AI output quality. A business glossary that defines "Revenue" canonically, combined with column descriptions that identify which table holds which variant, gives Copilot the context it needs to generate accurate measures. The investment in metadata pays off every time a user types a Copilot prompt.

Copilot tends to work best on models with well-structured star schemas, consistent naming conventions, descriptions on every table and column, and a governed business glossary that defines key metrics. It struggles most with flat, wide tables with cryptic column names and no documentation. The AI cannot compensate for missing metadata - it can only work with what it is given.

How Dawiso Supports Power BI Copilot

Dawiso's business glossary provides the canonical definitions that should be reflected in Power BI column descriptions. When the glossary defines "Revenue" as "net revenue after returns and discounts, in USD, from confirmed orders," that definition should appear as a column description in Power BI. Dawiso's Power BI connector ingests column and measure descriptions and measure expressions, so stewards can compare them with the glossary definitions.

The data catalog records each dataset's descriptions, and the ownership assigned in Dawiso, alongside the reports, data sources, and gateways it is connected to. A dataset with documented columns, verified definitions, and assigned ownership is more likely to produce accurate Copilot output than one with undocumented columns and unknown data quality.

Through the Model Context Protocol (MCP), AI agents can retrieve glossary definitions and their attributes from Dawiso. If Copilot generates a revenue calculation, an MCP-connected agent can retrieve the glossary definition of revenue and compare the calculation with it. This creates a verification loop: generate with Copilot, check against the definitions in Dawiso.

Data lineage helps reviewers confirm that Copilot referenced the right source tables. When a user asks about customer revenue, lineage shows which data sources and gateways feed the dataset behind the report. This context helps the users reviewing Copilot's output understand where the numbers come from.

Conclusion

Power BI Copilot changes the economics of report building. Tasks that took hours - writing DAX, choosing visualizations, laying out report pages - now take seconds. But the quality of that output is not determined by the LLM. It is determined by the metadata: column names, descriptions, relationships, and business definitions in your data model. Organizations that treat data model documentation as a prerequisite for Copilot get accurate, trustworthy results. Those that deploy Copilot on undocumented models get fast output with unreliable quality - the worst combination for AI-powered BI adoption.

Sources

• Microsoft Learn, Copilot for Power BI overview (requirements: capacity, admin setting, region, sovereign clouds).
• Microsoft Learn, Privacy, security, and responsible use for Copilot in Power BI (what data Copilot uses).
• Microsoft Learn, Write DAX queries with Copilot (DAX query view, min/max sample values, 200-character description limit).
• Microsoft Learn, Create and edit Power BI reports with Copilot (report creation limitations).
• Microsoft Learn, Prepare your data for AI to improve Copilot results (AI data schema, AI instructions, verified answers).
• Microsoft Learn, Introduction to Q&A in Power BI (Q&A is deprecated in February 2027).
• Microsoft Learn, Generate data insights on your semantic model automatically (Quick Insights).
• Gartner, Gartner Predicts 75% of Analytics Content to Use GenAI for Enhanced Contextual Intelligence by 2027 (press release, June 18, 2025).
• Dawiso Help, Power BI connector (ingested descriptions, measure expressions and relations).