Course Overview
This course introduces participants to the integration of Generative AI capabilities within Power BI to enhance data analysis, reporting, and decision-making. The program focuses on how AI-driven features such as Copilot, Q&A, Smart Narrative, and AI visuals can be leveraged to interpret data more effectively, automate insights, and streamline communication with stakeholders. Participants will learn how to structure and model data to support accurate AI-generated explanations, create dynamic dashboards that tell clear data stories, and explore optional integration pathways using Azure OpenAI and Power Automate for automated reporting workflows. Designed for business analysts, data professionals, and decision-makers, the course emphasizes practical, hands-on learning with real-world datasets, ensuring that participants leave with the skills to build Power BI dashboards enriched with meaningful narrative insights that can directly support business reviews and presentations.
Course Objective
By the end of this program, participants will be able to:
- Understand the principles and applications of Generative AI within Power BI.
- Differentiate between predictive and generative AI models in data analytics.
- Prepare and structure datasets to support AI-driven insights and natural language explanations.
- Utilize Power BI Copilot to generate visuals, narrative summaries, and DAX suggestions.
- Apply AI features such as Smart Narrative, Q&A, Key Influencers, and Decomposition Tree to enhance reporting.
- Automate report generation and distribution using Power Automate and AI integration.
- Connect Power BI with Azure OpenAI for advanced automated commentary and data storytelling.
- Build conversational BI assistants using Power Virtual Agents and Power BI integration.
- Implement governance, data security, and responsible AI practices in analytics workflows.
- Design and present interactive dashboards enriched with AI-generated insights and summaries.
Target Audience
This course is ideal for:
- Business Analysts and Data Analysts seeking to enhance their Power BI reporting with AI capabilities.
- Data Professionals and BI Developers integrating AI automation into analytics workflows.
- Managers and Decision-Makers looking to leverage AI-driven insights for faster, data-informed decisions.
- IT Professionals exploring AI-enabled automation using Power Automate and Azure OpenAI.
- Anyone responsible for building or maintaining business intelligence dashboards and reports.
Pre-requisite
Participants should have:
- A basic to intermediate understanding of Power BI (data modeling, report creation, and visualization).
- Familiarity with data concepts such as tables, relationships, and measures.
- An active Microsoft 365 account with Power BI Service access (recommended).
- No prior AI or programming experience is required, though basic exposure to DAX or Power Query will be helpful.
Course Duration
This course runs for 2 days, the instructor led.
Methodology
The course emphasizes hands-on, practical learning supported by real-world data scenarios. Delivery methods include:
- Instructor-led demonstrations on Power BI and Generative AI integrations.
- Guided practical exercises using Copilot, Q&A, and AI visuals.
- Step-by-step labs for connecting Power BI with Azure OpenAI and Power Automate.
- Group mini-projects to apply concepts in designing AI-enriched dashboards.
- Discussions and case studies to reinforce responsible AI and governance concepts.
- Final showcase presentations to evaluate participants’ understanding and creativity.
Course Outline
- Define Generative AI and its role in analytics workflows
- Explain the difference between predictive models vs generative models
- Example: “How GPT can summarize monthly sales performance in natural language”
- Components: Power BI Desktop, Service, Copilot in Power BI, Data Models
- Where Gen AI enhances the reporting lifecycle (ingestion → modeling → visualization → insights)
- Example: Using Copilot to suggest DAX measures
- Data cleaning and transformation using Power Query
- Ensuring semantic correctness in data models (dimensions, facts, relationships)
- Example: Creating a well-structured Date Table for automated explanations
- Copilot setup and licensing requirements
- Use Copilot to:
- Generate visuals based on prompts
- Create narrative summaries
- Suggest new measures and transformations
- Example: “Show top contributing product categories to revenue decline in Q3”
- Designing datasets optimized for Q&A usage
- Best practices for synonyms and phrasing
- Example: Ask “Total sales in Klang Valley last quarter by branch”
- Using Smart Narrative
- Using Key Influencers Visual
- Using Decomposition Tree
- Study example: Identify drivers of customer churn using Key Influencers
- Understanding Azure OpenAI endpoints
- Connecting Power BI to Azure OpenAI using Power Automate or Python script
- Example: Auto-generate monthly commentary for management dashboard
- Integrating Power Automate flows to Convert insights → Email summaries
- Integrating Power Automate flows to Convert user input → Automated queries
- Example: One-click “Generate Monthly Report PDF + Narrative”
- Create chatbot interface for business users (Power Virtual Agents + Power BI)
- Data governance and permissions considerations
- Example: Assistant that answers: “What is our top 5 products by profit in 2024 YTD?”
- Row-Level Security (RLS) and sensitivity labels
- Responsible AI guidelines (transparency, bias mitigation, data privacy)
- Example: Restrict confidential business unit performance from general staff
- Participants build a dashboard with:
- Data model
- AI visuals
- Copilot-generated insights
- Automated summary explainers
- Example: “Retail Store Sales Performance Dashboard + Auto Narrative Report”
- Group showcase
- Instructor evaluation and recommendations
Itinerary
Time | Day 1: Description |
08:30 AM – 09:00 AM | Participants attendance |
09:00 AM – 10:30 AM | Module 1: Introduction to Generative AI in Data Analytics |
10:30 AM – 10:45 AM | Morning Tea Break |
10:45 PM – 12:30 PM | Module 2: Overview of Power BI Ecosystem with Gen AI Capabilities Module 3: Preparing Data for Gen AI-Driven Insights |
12:30 PM – 01:30 PM | Lunch Break |
01:30 PM – 03:30 PM | Module 4: Copilot in Power BI – Guided Demo |
03:30 PM – 03:45 PM | Afternoon Tea Break |
03:45 PM – 05:00 PM | Module 5: Natural Language Querying (Q&A Feature) Module 6: AI Visuals and Automated Insights |
05:00 PM | Class Ends |
Time | Day 2: Description |
08:30 AM – 09:00 AM | Participants attendance |
09:00 AM – 10:30 AM | Module 7: Custom AI and GPT Integration via Azure OpenAI |
10:30 AM – 10:45 AM | Morning Tea Break |
10:45 PM – 12:30 PM | Module 8: Embedding Generative AI Workflows in Power BI Module 9: Building Conversational BI Assistants |
12:30 PM – 01:30 PM | Lunch Break |
01:30 PM – 03:30 PM | Module 10: Governance, Security and Responsible AI |
03:30 PM – 03:45 PM | Afternoon Tea Break |
03:45 PM – 05:00 PM | Module 11: Hands-On Mini Project Module 12: Final Presentation and Wrap-Up |
05:00 PM | Class Ends |
