Power BI Copilot is a generative AI assistant integrated into Microsoft Power BI that streamlines both the report creation (developer perspective) and report consumption (viewer perspective) experiences[7][6]. It allows users to create and interact with reports using simple natural language, lowering the barrier to entry for data analysis.

This lesson provides an interactive tutorial on using Power BI Copilot from both perspectives, including practical examples, and demonstrates how Copilot can facilitate dynamic report generation, Q&A on data, and even assist with predictive and prescriptive analytics. We also highlight how Copilot makes Power BI easier to use for citizen developers and knowledge workers with no BI or coding background.

Overview: Key Features of Power BI Copilot

Power BI Copilot offers a range of AI-driven features that enhance data analysis and reporting. Some of its key capabilities include:

• Natural Language Interface for Q&A: Users can ask questions about their data in plain English and get immediate answers in context. Copilot understands conversational prompts and generates responses as visuals or text insights[3][2]. For example, a user can ask “What were our total sales last month by region?” and Copilot will respond with the figure or a chart, referencing the data source.

• AI-Assisted Report Generation: Copilot can create report pages and visuals based on a user’s description. By providing a high-level prompt, users get a generated report page complete with charts and tables, saving significant time in the report building process[4][5]. This automated report creation acts like a “starter kit” for dashboards – you describe what you need, and Copilot builds it.

• IntelligentSuggestions and DAX Support: For developers, Copilot can help write or optimize DAX formulas and queries. It can generate complex measures or calculations when the user describes the requirement, eliminating the need to write code from scratch[6][3]. This boosts productivity by automating formula creation – for instance, a developer can say “create a measure for year-over-year sales growth” and Copilot will produce the DAX expression. It also suggests relevant fields or visuals to include based on the data context[5].

• Semantic Model Understanding: Copilot is aware of the Power BI semantic model (datasets, tables, relationships) behind the report. It can generate summaries of your data model or add natural language synonyms for fields to improve the Q&A experience[5]. This means Copilot not only uses your data, but also helps document and enrich it – for example, by automatically describing a complex dataset’s contents or suggesting alternative terms so that business users can ask questions more naturally.

• Report Summarization and Narratives: With Copilot, you can quickly generate a summary of an entire report or a specific page in seconds[5][5]. It can produce a written synopsis of key insights, either as an on-screen narrative visual or in the Copilot chat pane. This helps users interpret complex dashboards. For instance, Copilot can be prompted with “Summarize this sales performance dashboard” to produce a paragraph of insights, highlighting trends and outliers from the charts.

• Advanced Analytics Insights (Predictive Analytics): Beyond descriptive questions, Copilot leverages AI to identify trends and anomalies. It can highlight outliers in data and even perform basic trend forecasting based on historical patterns[3]. In practice, a user might ask “Is there a forecast for next quarter’s sales?” – Copilot can analyze the data trend and provide an estimated projection along with a rationale. While Copilot is not a full predictive model builder, it uses underlying data to offer forward-looking insights (e.g., detecting that sales have grown 5% monthly and projecting a similar increase)[3].

• Contextual Answers with Source References: When Copilot answers a data question, it often references the source visual or data it used. For example, if you ask for a specific figure from a report, Copilot will not only give the answer but also indicate which chart or table that answer came from[4]. This transparency builds trust in the AI’s responses and helps users verify information by seeing the source in the report.

These features empower different user personas in Power BI, from technical report authors to casual business report readers. Next, we’ll dive into how to use Copilot as a developer (to create reports) versus how to use it as a report consumer (to explore and understand data), through step-by-step scenarios and examples.

Using Copilot from a Developer’s Perspective (Report Creation)

From the developer or report author perspective, Copilot acts as a smart assistant in building and enhancing Power BI reports. It can suggest content, create visuals, and even generate code, all through natural language interactions. This allows developers (or even “citizen developers”) to accelerate the report development process. In this section, we walk through how a Power BI developer can use Copilot to create a report and leverage its capabilities during development.

Creating a Report Page with Copilot (Step-by-Step)

Let’s say you are a developer tasked with creating a sales report. Using Copilot, you can generate a draft report page interactively. Follow these steps to create a report with Copilot:

1. Select a Dataset and Open Copilot: In Power BI Service or Power BI Desktop, navigate to a workspace that has Copilot enabled (Premium or Fabric capacity) and choose the dataset or semantic model you want to use for your report[1]. Start a new report on that dataset, and click the Copilot icon in the report toolbar to open the Copilot pane[1]. Tip: If you don’t see the Copilot button, ensure your admin has enabled Copilot in Fabric and that you’ve selected a supported dataset[1].

2. Ask Copilot for Report Suggestions: If you’re not sure where to start, you can use the “Suggest content for this report” feature[1]. Copilot will analyze your dataset and propose an outline of report pages. For example, it might suggest pages like “Sales by Region,” “Sales Over Time,” and “Product Performance” based on the data it finds[5][5]. Each suggested page comes with a brief description of what it would contain. Review the suggestions in the Copilot pane.

3. Generate a Report Page: Choose one of the suggested pages and click Create next to it[1]. Copilot will automatically build that report page for you, populating it with relevant visuals (e.g., charts, tables, cards). For instance, if you selected “Sales by Region,” Copilot might create a bar chart of total sales by country and a map visual showing regional sales distribution. Within seconds, you’ll see a new page with these visuals added to your report[4]. Behind the scenes: Copilot identified the appropriate tables (e.g., Sales data, Geography) and fields (e.g., Sales Amount, Region) and chose suitable visualizations (bar chart, map) to present the data[5][5].

4. Use a Natural Language Prompt (Optional): Alternatively, or in addition to suggestion picking, you can directly tell Copilot what you want on a report page. For example, in the Copilot chat you might type: “Create a line chart of monthly sales and a table of top 5 products by revenue.” Copilot will interpret this request and generate those visuals on the report canvas if possible. This is useful when you have a specific design in mind. High-level prompts like “show trends over time and top performers” are enough – Copilot will determine the fields and chart types to use[5][5]. You can iterate by refining your prompt if the initial result isn’t what you expected.

5. Review and Refine the Output: Once Copilot creates the page, review the visuals and layout. Copilot speeds up work but may not always follow best design practices automatically[4]. Check if the charts make sense, adjust titles or formatting, and validate the numbers. Power BI’s normal editing tools are available for you to tweak the report. For example, you might resize a chart, change a color scheme, or replace a visual if needed. Recommendation: Treat the Copilot-generated page as a first draft – a starting point that you can improve upon[4].

6. Ask Copilot to Adjust if Needed: You can also have Copilot modify what it just created. For instance, you could ask in the Copilot pane, “Add a pie chart showing sales by product category.” If your dataset supports that, Copilot will add a new visual (or possibly create a new page with those changes, depending on the system’s current capabilities)[4]. This interactive back-and-forth helps you quickly try out different report compositions. (Note: Currently, if Copilot needs to significantly change a page’s visuals or layout, it might generate a new page version rather than altering the existing one, so don’t be alarmed if you see multiple page iterations)[4].

7. Save the Report: When you are satisfied with the Copilot-generated pages, save your report. You have now created a multi-visual Power BI report with just a few clicks and some natural language guidance, instead of manually dragging fields and writing formulas.

Practical Example – Building a Sales Report:
Imagine you have a dataset “SalesData” with fields for Date, Region, Product, SalesAmount, etc. As a report author, you type to Copilot: “Create a page to analyze sales performance by region and month, highlighting top products.” Copilot evaluates SalesData and generates a new “Sales Performance” page. It may include a line chart of monthly sales (date on X-axis, sum of SalesAmount on Y-axis) and a bar chart comparing sales by region[5]. It might also add a table listing the top 5 products by total sales, since you mentioned “top products.” You quickly get a dashboard-like page with these visuals assembled automatically. After reviewing, you notice all regions are shown but you only care about the top 3 regions – you then ask Copilot: “Filter the bar chart to show only the top 3 regions by sales.” Copilot applies that filter and updates the chart accordingly. In a matter of minutes, you’ve prototyped a sales report without writing any code or manually creating visuals.

Additional Copilot Capabilities for Developers

Beyond creating basic visuals, Copilot assists developers with more advanced or tedious tasks in report development:

• Writing DAX Measures and Queries: If you need a new calculation (measure) in your report, Copilot can help. As a developer, you can prompt Copilot with something like “Create a measure for Year-to-Date Sales growth percentage.” Copilot will generate a DAX formula for the measure based on your data model[5][3]. This is incredibly helpful if you are not an expert in DAX or want to save time. It can also explain or document existing DAX – you might ask “Explain this measure formula” to get a plain-language description of what a complex calculation does. By assisting with DAX, Copilot democratizes advanced analytics creation, allowing even those with limited coding skills to derive complex insights[2].

• Semantic Model Summarization: When working with a large dataset or model, you might use Copilot to “summarize the model.” Copilot will generate a summary of the semantic model – describing the tables, key measures, and how data is structured[5][5]. This is useful for quickly understanding a new dataset or for documentation purposes. It’s like having an instant data dictionary. For example, Copilot might output: “This model contains a Sales fact table linked to Products and Regions dimension tables. It includes measures like Total Sales, Total Profit, and calculated measures for Year-over-Year Growth.” Such summaries help developers and analysts ensure they have a solid grasp of the data before building complex reports.

• Adding Synonyms for Q&A and Descriptions: Power BI has a Q&A feature that lets users ask questions using colloquial terms. Copilot can enhance this by generating synonyms for fields in your model[5]. For instance, if your data field is named “Revenue”, Copilot might suggest synonyms like “sales” or “income” so that a user’s question “What are the sales this month?” maps correctly to the Revenue field. Copilot can also create or improve measure descriptions (the explanatory text for metrics) to make your model self-explanatory to end users[5]. These enhancements improve the report’s usability for consumers later.

• Creating Narrative Summaries on Report Pages: As a developer, you can insert a Smart Narrative visual and have Copilot populate it with a summary of the page data[4]. This turns your charts into a written storyline. For example, after creating the sales report, you could add a narrative that reads: “Overall sales have been increasing month-over-month, with North America contributing the highest revenue. Top-selling products are X, Y, and Z, which together account for 45% of total sales.” Copilot helps generate this narrative, which you can fine-tune before publishing. This feature ensures that even without your direct explanation, a report viewer can read key insights in text form on the report.

Copilot in the Developer Workflow – Key Takeaway: For report authors, Copilot acts like a co-creator. It speeds up development by suggesting visuals and writing code, and it enhances quality by documenting the model and ensuring Q&A works well. Developers still apply their expertise to refine and verify the output (Copilot doesn’t replace human judgment[7]), but it significantly reduces the grunt work. In short, Copilot lets you focus on what you want to show, while it handles how to technically implement it.

Using Copilot from a Report Viewer’s Perspective (Data Consumption)

From the report consumer or business user perspective, Power BI Copilot provides a conversational way to explore and understand data without editing the report directly. In viewing mode (whether in the Power BI service app, desktop reading view, or even mobile), Copilot becomes an intelligent assistant that can answer questions about the report’s data and generate explanations or summaries. This capability is revolutionary for knowledge workers who might not be proficient in BI tools – they can interact with the data on their own terms, by simply asking or instructing in natural language. Let’s explore how a report viewer can use Copilot.

Asking Questions of Your Data (Q&A in Natural Language)

One of the most powerful features for report viewers is the ability to ask Copilot questions about the data in a report. Instead of hunting through visuals or filters, viewers can just ask what they want to know. Here’s how it works and an example:

• How to Ask a Question: When viewing a report, the user can open the Copilot chat pane (usually via an icon or menu in the report interface). Without needing edit permissions, they can type a question in plain language into Copilot[4][5]. The question can be about anything contained in the report’s data model. For example, a sales manager looking at a dashboard might ask: “What was the profit margin for Australia in 2022?”.

• Copilot Retrieves the Answer: Upon submitting the question, Copilot will first search the report’s existing visuals and data for the answer[4]. If the information is already displayed in a visual (even if the user didn’t notice it), Copilot will use that. If not, Copilot will query the underlying dataset (semantic model) directly to find the answer[4]. In our example, Copilot might find that there is a card or table in the report showing profit margin by country and year, and specifically pull out Australia 2022: 35% (as an illustrative figure).

• Answer with Reference: Copilot then responds in the chat with the answer, and importantly, it will reference the source of that answer[4]. For instance, it might say: “The profit margin for Australia in 2022 was 35%, as shown on the ‘Yearly Profitability’ chart on page 2 of the report.” The answer might be accompanied by a snapshot or mention of the visual that contains that data[4]. This way, the viewer not only gets the number but also knows where it came from (adding credibility and context).

• Follow-up Questions: The user can continue the conversation to dig deeper. They might next ask: “How does that compare to other countries?” – Copilot understands this follow-up in context and could generate a quick comparison, perhaps noting that Australia’s 35% margin is higher than the overall average and second only to Canada’s 37% (pulling those figures accordingly). This conversational exploration allows non-technical users to analyze data without writing queries or wading through the entire report manually.

Practical Example – Q&A:
A report consumer opens a Sales & Profit Report in the Power BI app. They have a question that isn’t immediately obvious from the visuals: “Which product category had the lowest profit margin last quarter?” Instead of trying to find the right filter or visual, the user clicks the Copilot pane and asks exactly that. Copilot processes the question. Suppose in the underlying data model, there’s a calculation for profit margin by product category and by quarter. Copilot finds no direct visual in the report showing last quarter’s margins, so it queries the data model. It then replies: “In Q4 2024, the Accessories category had the lowest profit margin at 12%. This is based on the data from the report’s dataset.” Along with the answer, Copilot might highlight that this info isn’t on an existing chart but was derived from the data model (letting the user know it looked deeper)[4]. The user can then ask a follow-up, like “What was the profit margin for Accessories in the previous quarter?” to see if it’s improving or worsening. Copilot, keeping context, will answer that as well. This kind of natural Q&A turns a static report into an interactive analysis session for the viewer.

Summarizing and Explaining Report Insights

Another capability Copilot provides to report viewers is the ability to summarize complex reports or explain what’s on the page in narrative form. This is especially helpful for users who might be overwhelmed by a busy dashboard or who prefer reading insights in text:

• On-Demand Report Summaries: A viewer can ask Copilot something like “Summarize this report” or even more specifically “Summarize the key insights on this page in two paragraphs.” Copilot will then generate a natural language summary of the report or page[4][5]. It looks at the visuals, their trends, and notable data points to compose a high-level summary. For example, if a page has a trend chart and a breakdown by categories, the summary might say: “Sales have been steadily increasing over the past 12 months, with a peak in December. Among all product categories, Electronics leads in total sales, while Accessories shows the highest growth rate quarter-over-quarter. The Americas region outperforms EMEA and APAC in revenue, contributing approximately 50% of the total sales.” This gives the viewer a quick narrative insight without having to interpret each visual manually.

• Uses of Summarization: Such summaries are useful in several scenarios[4]:

  • If the report is very complex or dense, a summary distills the information into an accessible form (maybe the viewer is short on time or not deeply familiar with the data).
  • If the viewer has limited data literacy or is uncomfortable interpreting charts, the written summary translates visuals into plain language[4].
  • If the viewer wants a starting point before diving in, the summary can act like an executive overview of the page.

  The user can even guide the style of the summary by prompting specifics (e.g., “summarize this page using bullet points” or “focus the summary on financial metrics”)[5]. Copilot will adjust the output accordingly, giving the user control over how the information is presented.

• Smart Narrative Visuals vs. Copilot Pane: There are two ways these summaries might appear:

  1. In the Copilot pane/chat (on-the-fly, not part of the report permanently). Any user with view access can do this at any time to get an AI-generated explanation[5].
  2. As a Smart Narrative visual on the report (if the report author added one). In this case, the summary is embedded in the report itself. The report creator may have used Copilot to generate it initially. The smart narrative will update dynamically if the data changes or if filters are applied[4]. As a viewer, you can read this text on the report just like any other visual, but you can also further ask Copilot questions about it or for more details if needed.

• Interactive Explanations: Viewers can also ask **“Why?” or “How?” questions about the data. For example: *“Why did sales spike in December?”* Copilot will examine the data for potential reasons – maybe a particular product launch or a seasonal holiday effect – and attempt to explain the spike in a conversational answer, perhaps referencing which category or region contributed most to that spike. Similarly, a question like “How was this total calculated?” directed at a number on the report could prompt Copilot to explain the measure (e.g., “This is total sales, which is the sum of SalesAmount for all transactions in the selected period.”). This turns Copilot into a tutor that teaches the user about the report.

Practical Example – Getting a Report Summary:
A marketing manager opens a complex Market Trends Dashboard that covers multiple pages (overview, by region, by product line, over time, etc.). To quickly digest it, they ask Copilot: “Give me the key insights from this dashboard in a few sentences.” Copilot generates a response in the pane: “Overall demand is growing, with a 15% increase in total sales year-over-year. North America remains the largest market, but Asia-Pacific shows the fastest growth at 20%. Product X is the top seller globally, while Product Y sales are declining and need attention. Customer satisfaction is above 90%, which correlates with the introduction of the new loyalty program.” The manager gets a succinct overview touching on each page’s theme, complete with the important numbers, all without reading any charts[4]. Armed with this, the manager can click into specific pages for details or ask follow-up questions like “Why are Product Y’s sales declining?” to get more explanation. Copilot might then look at the data and answer that perhaps a competitor launched a rival product, or that Product Y had supply issues – if such insights can be gleaned from the data or metadata.

Summary for Report Viewers – Key Takeaway: Copilot transforms a passive report consumption experience into an interactive dialogue with your data. Business users with minimal technical knowledge can get the facts they need just by asking, and they can gain understanding through AI-driven explanations. This empowers everyone – not just analysts – to make better use of data. As always, users should verify critical insights (Copilot’s answers are only as good as the underlying data/model), but it dramatically reduces the effort to get those insights in the first place.

Why Copilot is Great for Citizen Developers and Non-BI Experts

A major promise of Power BI Copilot is ease of use – making advanced analytics accessible to people who are not BI professionals or who have no background in programming. Here we discuss how Copilot benefits citizen developers (power users who build reports on their own) and knowledge workers in general.

• Lowered Technical Barriers: With Copilot’s natural language interface, users don’t need to know DAX formulas, data modeling, or visualization design principles to create useful reports and get insights[2]. This dramatically lowers the learning curve. For example, a HR specialist can build an attrition report by simply describing what they want to see (e.g., “show me attrition rate by department over the last 12 months”) without ever writing a single formula or manually configuring a chart. Copilot handles the technical translation from request to result[2]. In essence, if you can articulate your question or idea, you can produce something valuable in Power BI.

• Faster Time to Insight: Citizen developers often juggle multiple roles and can’t spend days or weeks developing a report. Copilot accelerates the process by providing immediate suggestions and automations. As noted earlier, it can generate a fully populated report page in seconds or draft a complex measure instantly. This means even a novice can go from data to insights in a single sitting, focusing on interpreting results rather than wrestling with tool features. Businesses benefit because employees can rapidly prototype analyses for decision-making. One user in the community described this as quick time to insight and high ROI for business users leveraging Copilot’s Q&A features[6].

• Empowering Self-Service and Exploration: Knowledge workers no longer have to always ask a data analyst for help when they have a question. Copilot encourages a do-it-yourself approach in a safe, guided manner. A manager can explore “what-if” scenarios (e.g., “What happens if our sales grow 10% next quarter?” – Copilot might not run a simulation, but it could quickly show last quarter’s number and what 10% more would be) or quickly pull a figure needed for a meeting, just by asking Copilot. This self-service capability means more people in an organization can use data in their day-to-day work, even if they aren’t formally trained in BI. It democratizes data analysis, spreading the ability to glean insights beyond the analytics team[2].

• Bridging Skill Gaps: Citizen developers often have domain knowledge (e.g., finance, marketing) but lack advanced BI skills. Copilot bridges this gap by handling the BI mechanics while the user applies their domain perspective. For instance, a finance manager knows what they want to analyze (budget vs actuals, variance causes) – Copilot can take their natural explanation and produce the analysis visuals. The user can then interpret those with their expertise. This collaboration between human insight and AI assistance means even complex analyses (like identifying drivers for budget variance) become attainable to non-experts, guided by Copilot’s prompts and suggestions.

• Learning and Upskilling: Interestingly, using Copilot can also help users learn BI concepts gradually. As Copilot produces DAX formulas or summaries, an observant user might pick up on how things are done. Over time, a curious citizen developer could examine the DAX Copilot writes, for example, and learn from it (almost like having a tutor). Copilot also provides explanations, so when a user asks for something and Copilot responds, the user is exposed to terminology and logic that can build their knowledge. It’s a gentle introduction to Power BI’s powerful features, in plain language.

In summary, Copilot makes Power BI far more approachable. It offers a safety net for those unsure how to start, guides those who aren’t well-versed in data, and speeds up work for everyone. This ease of use is exactly why it’s seen as a game-changer for broadening the use of analytics in organizations – more people can engage with data without fear or frustration, leading to a more data-driven culture.

Copilot for Predictive and Prescriptive Analytics

Beyond interacting with existing data, users are naturally interested in future-looking insights (predictive analytics) and guidance on decisions (prescriptive analytics). While Power BI Copilot is not a full-fledged data science tool, it does offer features and possibilities that touch on these advanced analytics aspects:

• Identifying Trends and Forecasting (Predictive): Copilot can help users spot trends over time and even extrapolate them. For instance, if you have time-series data, a user might ask Copilot, “What is the forecast for sales next quarter?” Copilot will look at historical trends in the data. If the data model or visuals include a forecast or if a trend is clear, Copilot can provide an answer like, “Sales have been growing ~5% each quarter; if this trend continues, next quarter’s sales could be around X amount.” In fact, Copilot has built-in advanced analytics capabilities such as highlighting anomalies (outliers) in data and performing trend analysis[3]. It can automatically point out if a current metric is unusually high or low compared to the norm (anomaly detection), which is a key part of diagnostic and predictive analytics. For example, “This month’s expense is an outlier, 30% higher than usual for the first time in 2 years” – a hint that something changed. Moreover, by analyzing historical patterns, Copilot might suggest what future values could be, essentially doing a light form of forecasting[3]. These AI-driven insights help users anticipate future performance without needing a separate predictive model.

• Scenario Exploration: While Copilot doesn’t run complex simulations, a creative user can still leverage it for “what-if” analysis in a conversational way. For example, a user might ask: “If we increase our marketing spend by 10%, how could that affect sales?” Copilot could respond with insights based on past data relationships (if known) or at least identify the factors involved (e.g., it might note that marketing spend and sales have a positive correlation historically if that pattern exists in the data). If the data model has scenario parameters or what-if variables, Copilot might even help adjust them. In essence, Copilot can assist in exploring hypothetical questions by quickly retrieving relevant data points (like last time marketing spend increased, what happened to sales) to inform the prediction. This guides business users in thinking through scenarios, which is an element of prescriptive analytics (deciding on an action by understanding its potential outcome).

• Root Cause and Influencer Analysis: Another aspect of prescriptive analytics is understanding why something happened so you can decide what to do. Power BI has features like the Key Influencers visual that uses machine learning to find factors that impact a certain outcome. Copilot can complement this by allowing users to ask “Why” questions. For example: “Why did our customer satisfaction drop in Q3?” Copilot may sift through the data to find relevant factors – perhaps it notices that in Q3 the support tickets spiked or a product delay occurred that correlates with satisfaction dips. It will then answer with a hypothesis like “Customer satisfaction dropped, possibly due to a significant increase in support ticket volume in that period, indicating more customer issues.” While Copilot’s answer is limited by the data at hand (and it’s not guaranteeing causation), it effectively performs a simplified root cause analysis via natural language. This insight is valuable for prescriptive action because it points business users to areas they might address (in this case, improving customer support processes in Q3).

• Guiding Decisions (Prescriptive): Ultimately, prescriptive analytics is about what actions to take. Copilot can’t outright decide for you, but it facilitates decision-making by providing the right information in a timely manner. Throughout this lesson, we saw how Copilot simplifies getting insights and understanding data. This leads to better decisions: when managers and analysts have a clearer picture of current trends and potential future outcomes, they can formulate strategies proactively. For example, Copilot’s trend analysis might show a steady decline in a particular product’s sales. A user could then ask, “What can we do to improve Product X sales?” While Copilot may not have an opinion, it could respond by summarizing related data such as “Product X sales are declining, especially in the West region where competitor Y introduced a similar product. Our prices for X are also higher than the competitor’s.” This kind of answer (drawing on internal data and possibly market data if available) provides clues for action: maybe adjust pricing or marketing in the West region. In this way, Copilot’s ability to synthesize information hints at prescriptive solutions. It gives users the insight needed to take actions like budget reallocation, process changes, or strategy shifts.

• Example – Planning with Copilot:
  Imagine a planning meeting where a team is using a Power BI report to decide next quarter’s strategy. They ask Copilot, “Which two regions should we focus our additional marketing budget on?” Copilot analyzes sales growth rates and market potential data in the model. It finds that, e.g., Asia-Pacific and Europe have the highest growth rates but lower marketing spend compared to North America. It answers: “Asia-Pacific and Europe show high growth potential with relatively lower current marketing investment. Increasing budget in these regions could yield strong returns, as they contributed a combined 30% of growth last quarter.” This data-driven suggestion (backed by the numbers from the report) directly informs a prescriptive decision – allocate more marketing budget to APAC and Europe. The team, armed with this insight, can confidently plan their strategy. Copilot essentially served as an analyst in the room, surfacing the key data points to guide the decision.

Important Note: While Copilot offers features aligned with predictive and prescriptive analytics, it should be used with understanding of its limits. The insights it provides are based on existing data and patterns; it does not perform complex statistical forecasting or optimization on its own (beyond what Power BI’s built-in analytics can do)[7]. Always validate AI-generated forecasts or suggestions against expert knowledge and consider using specialized tools for rigorous predictive modeling if needed. Copilot’s role is to make advanced insights more accessible and faster to obtain, which it does effectively – but human oversight and expertise remain crucial, especially for high-stakes decisions.

Conclusion

In this tutorial, we explored Microsoft Power BI Copilot from both the developer and consumer perspectives. We saw that for report creators, Copilot can dramatically speed up the creation of reports by handling much of the heavy lifting (from building visuals to writing DAX), and for report viewers, it transforms interacting with data into a simple Q&A experience, delivering insights on demand. We also discussed how Copilot lowers the barrier for non-technical users – enabling citizen developers and knowledge workers to engage with data in ways that were previously too complex – and how it can assist with forward-looking analysis by identifying trends and suggesting insights that inform decisions.

Key takeaways include:

• Copilot is an AI assistant in Power BI that understands natural language. This means you can talk to Power BI in plain English to create or explore reports, without deep technical know-how[2].

• Both developers and business users benefit: Developers get help with building content (like an AI pair-programmer for BI), including writing calculations and generating report elements[6][5]. Business users get a conversational analytic tool to query data and receive explanations, making complex dashboards easier to digest[4].

• Practical usage involves simple steps: selecting a dataset, asking Copilot for what you need (a visual, a summary, an insight), and refining as necessary. The interactive examples we walked through illustrate how quickly a report can come together or how easily a question can be answered with Copilot’s help.

• Copilot enhances ease of use and productivity: It bridges the gap for those with little BI experience, allowing anyone to create prototypes or glean insights. This democratization of BI leads to faster decision cycles and empowers more team members to be data-driven[2].

• Advanced insights are more accessible: While not a substitute for full-scale predictive models or expert analysis, Copilot can point out trends, anomalies, and provide forecasts or recommendations based on data patterns[3][2]. This helps organizations to anticipate and plan rather than just react.

We recommend new users of Copilot to experiment with interactive prompts in a safe environment – try asking for different visuals, summaries, or explanations. The more you use it, the better you’ll understand how to phrase requests to get the desired outcome (for guidance on writing effective prompts, Microsoft provides tips in the documentation[1]). Additionally, always combine Copilot’s convenience with your own critical thinking: use the AI’s output as a starting point or assistant, and then apply your business knowledge to refine and act on those insights.

Power BI Copilot represents a significant leap in making data analytics more approachable. By following this tutorial and practicing with your own data, you can harness Copilot to create impactful reports and uncover insights with ease – whether you’re a seasoned developer looking to boost productivity or a business user eager to answer data questions independently. Embrace this new way of working with data, and enjoy the journey toward more efficient, AI-powered analytics in Power BI!