Power BI and Copilot: The Complete 2025 Guide to AI-Powered Data Analytics
In late 2025, the convergence of Power BI and Copilot represents a fundamental shift in how organisations approach data analytics. No longer is business intelligence a domain reserved for highly trained data scientists and analysts. With Copilot in Power BI, business users can now “chat” with their data, asking questions in plain English and receiving instant visual analyses, DAX code generation, and narrative summaries powered by Azure OpenAI’s Large Language Models.
This comprehensive guide covers everything Australian organisations need to know: what these technologies are, how they work together, their benefits, licensing structures specific to Australia, and a complete step-by-step tutorial for implementation.
Table of contents
Part 1: The Foundations
What is Power BI?
Power BI is Microsoft's comprehensive business intelligence platform, encompassing a suite of connected software services, apps, and connectors designed to transform unrelated data sources into coherent, visually immersive, and interactive insights. It has dominated the global BI market for over a decade because of its deep integration with the Microsoft ecosystem and its ability to scale from individual analysts to enterprise-wide deployments.
The Core Architecture
Power BI operates across three distinct layers:
1) Data Connection & Transformation: Power Query connects to hundreds of data sources (SQL databases, Excel, cloud services, APIs) and transforms raw data into structured formats.
2) Data Modelling: The semantic model defines relationships between tables using star-schema designs, with fact tables containing metrics and dimension tables containing descriptive attributes.
3) Visualisation & Distribution: Reports and dashboards present insights through interactive charts, matrices, and custom visuals, distributed via Power BI Service, mobile apps, or embedded into business applications.
The User Base
Power BI serves a diverse audience:
• Data Engineers: Build data pipelines and optimise semantic models for performance.
• Analysts: Create reports, write DAX measures, and conduct exploratory analysis.
• Business Users: Consume reports, filter data, and ask ad-hoc questions.
• Executives: View KPI dashboards and receive automated insights.
What is Copilot?
Copilot in Power BI is Microsoft's branding for a generative AI assistant grounded in your specific business context. Unlike a generic chatbot, it is deeply integrated into Power BI's architecture and understands your data model, your industry terminology, and your organisational logic.
The Engine
Copilot runs on Large Language Models (LLMs) hosted in Azure OpenAI Service. As of 2025, Microsoft offers multiple LLM options (such as GPT-4 and GPT-4 Turbo), with the specific model varying based on capacity tier and tenant settings.
The Critical Privacy Boundary
A common concern: “Will my data be used to train ChatGPT?” The answer is unequivocally no.
• Your data remains within your tenant and your region.
• Your prompts and data are not used to train public foundation models.
• Only your organisation can access your data through Copilot.
• Row-Level Security (RLS) is respected at all times, so users only see what they’re allowed to see.
Part 2: How They Work Together (The Technical Architecture)
The integration of Copilot into Power BI is not a simple “chat sidebar.” It is woven into multiple layers of the application.
Layer 1: The Authoring Layer (Power BI Desktop)
When a report author opens Power BI Desktop and clicks the Copilot button, they enter a conversational interface where they can:
• Generate full report pages using natural language prompts.
• Add or modify visuals on existing pages.
• Delete unwanted elements and refine layouts.
Example workflow:
Prompt: “Create an executive summary page. Include a KPI card for Total Revenue, a line chart showing Monthly Revenue Trends, and a map of Sales by State.”
Result: Copilot automatically:
1) Scans your semantic model for appropriate fields.
2) Creates the KPI card, line chart, and map visuals.
3) Arranges them on the canvas with appropriate sizing and positioning.
4) Applies default formatting.
The user then has full control to refine, adjust, and republish.
Layer 2: The Calculation Layer (DAX Code Generation)
Writing DAX (Data Analysis Expressions) is the highest barrier to entry in Power BI. Copilot removes this barrier through the DAX Query View.
Example:
Prompt: “Create a measure that calculates the rolling 3-month average of Total Sales, excluding any filters on Product Color.”
Rolling 3 Month Sales =
CALCULATE(
AVERAGEX(
DATESINPERIOD('Date'[Date], MAX('Date'[Date]), -3, MONTH),
[Total Sales]
),
ALL('Product'[Color])
)
The critical difference from a generic chatbot: Copilot understands your specific table names, relationships, and business logic.
Layer 3: The Consumption Layer (End-User Interaction)
For business users and executives, Copilot appears as:
1) The Copilot Pane: A sidebar chat interface where they can ask questions like “Show me sales by region” and receive both a chart and a narrative summary.
2) Narrative Visuals: An automatic text summary that rewrites itself as filters change. If the user clicks to view only “Sydney,” the narrative updates to focus on Sydney-specific insights.
3) The Standalone Copilot (New in 2025): A full-screen experience where users can search across all reports and semantic models they have access to, not just the one currently open.
4) Mobile Voice Integration (New in 2025): Tap the microphone icon and ask “What were sales yesterday?” Copilot returns a KPI card and mini-chart instantly.
Part 3: The Business Benefits
1. Velocity (The “Time Tax” Elimination)
The traditional report creation workflow:
1) Business user requests a report.
2) Analyst spends 2–3 hours understanding requirements.
3) Analyst spends 4–6 hours building the data model relationships and DAX measures.
4) Analyst spends 2–3 hours creating visuals and formatting.
5) 1–2 hours of refinement and feedback cycles.
Total: 9–15 hours per report.
With Copilot:
1) Analyst types: “Create a sales performance dashboard comparing actual vs. budget by region.”
2) Copilot generates a draft in about 60 seconds.
3) Analyst refines specific elements in 30 minutes.
Total: 30–45 minutes for the same output – representing a 10–15x velocity improvement.
2. Accessibility (Breaking the “BI Bottleneck”)
In traditional organisations, the BI team becomes a bottleneck. Every question requires drafting an email or ticket and waiting days. With Copilot, non-technical business users can:
• Ask their own questions: “Which customer segments have the highest churn?”
• Get instant answers from the semantic model.
• Iterate quickly without creating new BI tickets.
Result: a sales manager can run their own root-cause analysis in five minutes instead of submitting a ticket.
3. Insight Quality (Pattern Recognition at Scale)
Humans are good at interpreting individual data points. AI is good at pattern recognition across thousands of combinations.
Example: A finance manager is looking at a declining profit margin visualisation. Copilot can:
• Automatically cross-reference the decline against many potential drivers (product mix, pricing, costs, geographic shifts).
• Identify that 60% of the margin decline is due to a product mix shift toward lower-margin items in a particular region.
• Surface this insight in a single narrative.
4. Model Maintenance (Self-Healing Insights)
When your underlying data model changes (a new column is added, a table is renamed), Copilot doesn’t require retraining. It immediately understands the new schema and adapts its responses, as long as your model remains logically structured and well-documented.
Part 4: Licensing & Pricing in Australia (2025)
This is the section that determines adoption for many organisations.
The “Golden Ticket”: Fabric Capacity
To use Copilot in Power BI, your workspace must be assigned to a Fabric Capacity that supports it. This is not included in the standard Power BI Pro license.
The Capacity Tiers
| SKU Tier | Compute Units (CUs) | Copilot Enabled? | Typical Use Case |
|---|---|---|---|
| Fabric F2 | 2 CUs | No | Data pipeline testing, minimal analytics |
| Fabric F4 | 4 CUs | No | Small team analytics |
| Fabric F8 | 8 CUs | No | Medium team analytics |
| Fabric F16 | 16 CUs | No | Large team analytics |
| Fabric F32 | 32 CUs | No | Enterprise data processing |
| Fabric F64 | 64 CUs | Yes | Minimum tier for Copilot access |
| Fabric F128 | 128 CUs | Yes | High-volume Copilot usage |
Australian Pricing (Late 2025 Estimates)
Based on Azure region pricing for Australia East and current exchange rates:
| SKU | Pay-As-You-Go (Monthly) | Reserved 1-Year (Monthly) | Annual Savings (Indicative) |
|---|---|---|---|
| F64 | ≈ $12,800 AUD | ≈ $7,700 AUD | ≈ $61,200 AUD |
| F128 | ≈ $25,600 AUD | ≈ $15,400 AUD | ≈ $122,400 AUD |
Important notes:
1) This is a capacity cost, not per-user. One F64 capacity covers your entire organisation (or specific workspaces).
2) For 1,000 Power BI Pro users, cost per user can be roughly $12.80/month at F64, which is economical at scale.
3) For 50 users, cost per user jumps to around $256/month, which is expensive.
4) Reserved instances offer significant savings if you commit for one year.
Tenant Settings (The Gatekeeper)
Even with F64 capacity, Copilot must be explicitly enabled in your tenant.
1) Go to the Power BI Admin Portal or Fabric Admin settings.
2) Enable “Users can use Copilot and other features powered by Azure OpenAI.”
3) Optionally configure whether data can be processed outside your tenant’s region, based on your data residency requirements.
Regional Considerations
As of 2025, Copilot is available in major regions, including Australia East and Australia Southeast. If your capacity is provisioned in an unsupported region, Copilot will not function in that workspace.
Part 5: Step-by-Step Implementation Guide
Phase 1: Pre-Implementation (Weeks 1–2)
Step 1.1: Capacity Procurement
1) Contact your Microsoft account manager or a licensed reseller in Australia.
2) Purchase Fabric F64 capacity (or higher).
3) Expect costs between roughly $7,700–$12,800 AUD/month depending on reservation.
4) Provisioning typically completes in 24–48 hours.
Step 1.2: Tenant Configuration
1) Go to Microsoft 365 Admin Center > Power BI / Fabric Admin Portal.
2) Navigate to Tenant Settings.
3) Find the “Copilot and Azure OpenAI” section.
4) Enable the setting “Users can use Copilot and other features powered by Azure OpenAI.”
5) Decide whether to allow processing outside your tenant’s geographic region based on data policy.
Step 1.3: Workspace Assignment
1) Go to Power BI Service > Workspaces.
2) Select the workspace where you’ll pilot Copilot.
3) Open workspace Settings > Premium/Fabric Capacity.
4) Assign your F64 capacity and save.
Phase 2: Data Preparation (The Linguistic Schema) (Weeks 2–4)
Copilot is only as intelligent as your data model. Garbage in, garbage out applies to AI as well.
Step 2.1: Column Naming Hygiene
Bad naming examples:
• Tbl_Sales_Final_v2
• Amt_$
• CustID
• SDATE
Better naming examples:
• Sales Transaction
• Sales Amount
• Customer ID
• Sale Date
Step 2.2: Synonyms (Bridging Business Language)
Your CEO might say “Turnover” while your model says “Total Revenue”. Copilot needs to know these are the same idea.
• For Total Revenue, add synonyms: Turnover, Billings, Gross Sales, Fees.
• For Profit, add synonyms: Net Income, Earnings, Bottom Line.
• For Customer, add synonyms: Client, Account, Consumer.
Step 2.3: Data Categories (Semantic Labelling)
Help Copilot understand the type of data in each column:
• Set City columns to Data Category = City.
• Set Country/Region columns to Country/Region.
• Ensure dates are typed as Date, not Text.
Step 2.4: Measure Descriptions (The AI Cheat Sheet)
Write clear, business-friendly descriptions for every measure. Copilot reads these when generating responses.
Good description: “Total sales revenue across all customers and products. Excludes returns and adjustments. Calculated using SUM(Sales[Amount]).”
Step 2.5: The “Prep Data for AI” Button (New in 2025)
1) Open your model in Power BI Desktop.
2) Click Prep data for AI on the Home ribbon.
3) Use the Simplify data schema tab to hide confusing or system fields.
4) Use Verified answers and AI instructions tabs in later phases.
Phase 3: Creating Your First Report (The Tutorial)
Scenario: Sales Performance Dashboard for a Regional Manager
Requirement: Create a page showing:
• Total Revenue KPI
• Sales Trend (Monthly)
• Sales by State (Map)
• Top Products (Bar Chart)
Step 3.1: Open Power BI Desktop
1) Open Power BI Desktop.
2) Connect to your Fabric/Premium capacity workspace.
3) Select your cleaned semantic model.
4) Create a new blank page.
Step 3.2: Activate Copilot
1) Click the Copilot button in the ribbon.
2) The Copilot pane opens on the right-hand side.
Step 3.3: The “Suggest Content” Method (Automated)
1) Click “Suggest content for this report.”
2) Copilot scans your schema and proposes several layouts.
3) Choose an option and click Create.
4) Copilot builds the page automatically.
Step 3.4: The GCES Prompt Method (Controlled)
For more control, use the GCES Framework:
| Component | Description |
|---|---|
| Goal | What do you want to achieve? |
| Context | Who is the audience? |
| Expectations | Format? Theme? Specific visuals? |
| Source | Which tables/measures should be used? |
Example GCES prompt to type into Copilot:
“Create a sales performance page for the Regional Manager. Include a KPI card for Total Revenue YTD, a line chart showing Monthly Revenue for the current year, a map of Sales Amount by State, and a matrix table showing Top 10 Products by Sales. Use a dark blue theme. Filter to show only Australian data.”
Step 3.5: Refining the Output
The auto-generated page is rarely perfect. Use follow-up prompts:
| Issue | Prompt |
|---|---|
| Line chart looks wrong | “Change the line chart to show a forecast for the next 3 months.” |
| Need a different visual | “Replace the matrix table with a clustered bar chart showing top products by revenue.” |
| Too much clutter | “Delete the map visual; it's not needed.” |
| Need more context | “Add a text box at the top with the title ‘Regional Sales Performance Dashboard’.” |
Step 3.6: Save and Publish
1) Click File > Save and name your report (e.g. “Regional Sales Performance Dashboard”).
2) Click Publish and choose your Fabric capacity workspace.
3) Share the report or app with your pilot users.
Phase 4: Advanced Features (Verified Answers & AI Instructions)
Feature 1: Verified Answers (Curated Responses)
Verified answers let you pre-define answers to common questions so Copilot returns consistent, author-approved responses.
• Ensure questions like “What’s our revenue target?” always surface the same KPI card.
• Reduce confusion when multiple reports or measures exist.
Setup:
1) Open a report in Power BI Service (Edit mode).
2) Select a visual (e.g., Revenue Target KPI).
3) Use the menu to set up a verified answer.
4) Add triggers like: “Show me the revenue target”, “What’s our revenue goal?”, “Revenue forecast for next year”.
Feature 2: AI Instructions (Business Context Injection)
AI instructions let you inject business rules and definitions directly into Copilot’s understanding.
Example instructions:
- Busy season is October to February. When discussing performance, highlight these months.
- “ABCD” is an internal code for our North American division.
- Always exclude trial customers from revenue calculations.
- When calculating profit, use Gross Profit, not Operating Profit, as the standard.
1) Open Power BI Desktop.
2) Click Prep data for AI > AI Instructions.
3) Add bullet points or paragraphs with your key business rules.
4) Publish the model and reports.
Part 7: DAX Development with Copilot (Advanced)
DAX is the calculation language of Power BI. It’s powerful but can be daunting. Copilot helps generate, explain, and refine DAX.
The DAX Query View
1) Open Power BI Desktop.
2) Click Copilot in the ribbon.
3) Use the skill picker to select “Write a DAX query”.
Example – Rolling Average:
Prompt: “Create a DAX measure that calculates the rolling 3-month average of Total Sales, but ignore any filters on the Product Color dimension.”
Rolling 3 Month Avg =
CALCULATE(
AVERAGEX(
DATESINPERIOD(
'Calendar'[Date],
MAX('Calendar'[Date]),
-3,
MONTH
),
[Total Sales]
),
ALL('Product'[Color])
)
The Review Loop
Never accept DAX code blindly. Follow this process:
1) Ask Copilot to explain the code line-by-line.
2) Run the query and verify results in the grid.
3) Ask about edge cases (e.g. fewer than 3 months of data).
4) Only then add the measure to your model.
Common DAX Patterns with Copilot Prompts
| Use Case | Prompt |
|---|---|
| Year-to-date total | “Create a measure for YTD Revenue.” |
| Month-over-month growth | “Calculate month-over-month revenue growth percentage.” |
| Distinct customer count | “Create a measure for the number of unique customers.” |
| Previous month sales | “Create a measure showing sales from the previous month.” |
| Running total | “Create a running total of cumulative revenue by month.” |
Part 8: Updates, Tips & Tricks (2025 Edition)
Recent Updates (May–November 2025)
May 2025: Verified Answers & AI Instructions go mainstream.
August 2025: Standalone Copilot (Preview) – a full-screen Copilot experience across models and reports.
November 2025: Mobile Voice Integration – voice queries via the Power BI mobile app.
November 2025 also delivers enhanced forecast capabilities and anomaly detection on time-series visuals.
Expert Tips & Tricks
• Iterative refinement: Don’t expect perfection on the first prompt. Start broad, then refine.
• Use data preparation to improve accuracy: Clean naming, descriptions, and categories massively improve Copilot responses.
• Context stack: Ask follow-up questions that build on prior answers for deeper analysis.
• Prompt engineering: Be specific about measures, dimensions, time frames, and formats.
• Clear chat when switching topics: Prevent old context from confusing new questions.
Common Mistakes to Avoid
Watch out for:
• Expecting 100% accuracy without verification – Copilot can hallucinate.
• Overusing calculated columns instead of measures, bloating the model.
• Neglecting RLS and accidentally exposing sensitive data.
• Running Copilot on messy, undocumented models and then blaming Copilot for poor answers.
• Not configuring verified answers for common questions, leading to inconsistent responses.
Part 9: Real-World Use Cases (Australian Context)
Use Case 1: Sales Forecasting for a Manufacturing Export Company
Company: An Australian heavy machinery manufacturer exporting to Asia-Pacific.
Challenge: The sales team was manually updating Excel forecasts, taking around 8 hours per week.
Solution:
1) Connected CRM data to Power BI.
2) Built a semantic model with pipeline, deal stages, and win probability.
3) Enabled Copilot on F64 capacity.
4) Sales manager now asks Copilot questions like:
• “Which opportunities are at risk of stalling?”
• “Compare this month's pipeline to last month's.”
• “Forecast revenue for Q1 2026 based on current opportunities.”
Results: Forecasting cycle time drops from 8 hours to about 30 minutes per week, forecast accuracy improves, and adoption climbs across the sales team.
Use Case 2: Government Agency HR Analytics
Agency: A large Australian Public Service organisation.
Challenge: HR spends hours answering ad-hoc questions about headcount, attrition, and compliance.
Solution:
1) Integrated HR data from core HR systems into Power BI.
2) Built a semantic model with employee records, leave, performance, and attrition.
3) Configured AI instructions for how attrition is calculated and which populations to include or exclude.
4) Managers self-serve common questions using Copilot.
Use Case 3: Retail Chain Performance Management
Chain: Australian multi-store retail network (50+ locations).
Challenge: Store managers struggle to quickly analyse local performance data and depend on regional managers.
Solution:
1) Connected POS data for all stores into a central model.
2) Built a store-level semantic model with sales, inventory, and customer metrics.
3) Published a Copilot-enabled Power BI app for managers.
4) Store managers ask questions like “How did we perform this week vs. last week?” and “Which products are underperforming?”
Part 10: Roadmap for 2026 & Beyond
Microsoft has signalled several directions for Copilot in Power BI:
• Enhanced real-time streaming: richer support for streaming data sources and real-time analytics.
• Custom visuals support: Copilot working more seamlessly with third-party visualisations.
• Multilingual Copilot: better experiences in non-English languages.
• Generative analytics: Copilot proactively surfacing insights without prompts.
• Copilot agents: autonomous agents that can run scheduled analyses and alert teams to anomalies.
Part 11: Implementation Checklist for Australian Organisations
Pre-Implementation
• Secure F64 Fabric capacity budget (~$7,700–$12,800 AUD/month).
• Identify a pilot use case (e.g., sales forecasting, financial reporting, HR analytics).
• Assess current Power BI maturity (clean, documented semantic models?).
• Secure stakeholder buy-in from executives, IT, and business unit leaders.
Implementation
• Procure and provision F64 capacity (24–48 hours).
• Enable Copilot in tenant settings.
• Assign capacity to pilot workspace.
• Clean and document the semantic model (1–2 weeks).
• Build a pilot report with Copilot (2–4 hours).
• Set up verified answers for key business questions.
• Add AI instructions for business terminology and rules.
• Train the pilot group (4–8 hours total).
Post-Implementation
• Measure adoption and ROI in the first 30 days.
• Gather feedback from the pilot group.
• Refine semantic models based on real-world questions.
• Expand Copilot to additional workspaces and teams.
• Monitor capacity usage and scale up if needed.
• Conduct quarterly governance reviews (permissions, RLS, data accuracy).
Conclusion
Power BI Copilot is not a silver bullet, but it is a fundamental productivity multiplier for organisations with mature data capabilities. In late 2025, it has evolved from novelty to core operational tool.
