Power BI in the Age of AI
An end-to-end curriculum for practitioners who already know Power BI — from a stakeholder’s business question to an AI-designed, decision-ready dashboard. Stop measuring success by how many dashboards you ship; start measuring whether you answered the question, well.
For experienced Power BI users • 9 modules + capstone • ~44–50 hours • Advanced
Course Description
Power BI in the Age of AI is an end-to-end curriculum for practitioners who already know core Power BI — relationships, basic DAX and basic visuals. It does not repeat the basics. It is about how to approach the work: using AI at every stage, with proper design and real business judgment.
The core promise is a shift in how you measure success — from “how many dashboards did I ship” to “did this answer the stakeholder’s actual question, well.” Every module ties back to that: a clean semantic model as the foundation of AI quality, AI-directed DAX with proper structure, stakeholder discovery, Figma wireframes and AI-generated themes, disciplined KPI design, and advanced visual craft with SVG, HTML and Deneb.
Across roughly 44 to 50 hours — nine modules and a capstone — everything is taught as decision frameworks (when to use what), not a feature tour. You finish with a portfolio-ready case study that takes one business question from discovery to a finished, AI-designed dashboard.
What You Will Learn
- ✓The semantic model as your AI foundation — Treat the model as the layer that defines vocabulary, relationships and business logic once — and run a readiness pass before you point any AI at it.
- ✓AI-directed DAX, done like a senior dev — Direct AI to write DAX properly — a repeatable prompt method, base-vs-derived measures, folders, naming and variables — then verify it.
- ✓Stakeholder discovery — Surface the real decision and its cadence, and decide explanatory vs exploratory before you wireframe anything.
- ✓Figma wireframes & AI-generated themes — Design before you build, and generate reusable Power BI theme JSON with AI instead of hand-formatting every report.
- ✓KPI design with a built-in ‘so what’ — Choose the handful of KPIs that change a decision, and design cards that carry the number, the comparison and the so-what.
- ✓SVG, HTML & Deneb — when to use what — Native-first, then SVG, HTML or Deneb only where they earn their place — a decision framework, not because you could.
Course Curriculum
Data Connectivity & the Semantic Model, Properly Understood
- Connectivity as a decision table: Import vs DirectQuery vs Direct Lake
- What a semantic model actually is — the reusable vocabulary and business-logic layer
- Why a clean model is the single biggest lever on AI output quality
- Model hygiene: hidden columns, marked date tables, default summarization, sort-by-column
- Lab: a semantic model readiness pass before you open Copilot or an AI agent
AI Tooling Setup: MCP, PBIP, Claude & Copilot
- Setting up a Model Context Protocol (MCP) server to connect an AI agent to a live Power BI or Fabric model
- Working with Power BI Project (PBIP) files and TMDL — editing your model and report as code
- Using Claude with the Power BI and Fabric skills to author measures, models and reports
- Directing an agent to batch-create DAX, audit relationships and enforce your naming conventions against a real model
- Setting up Power BI Copilot: licensing, Fabric capacity and prompt patterns that actually work
- Version control with GitHub: putting PBIP under source control with a review and pull-request workflow
- Guardrails: what to let an agent do unsupervised, and human-in-the-loop checkpoints
- Lab: connect an agent to a sample model via MCP and build a small, GitHub-tracked measures library
AI-Assisted DAX: Structure, Prompting & Best Practice
- Measure organization: display folders by business domain, base vs derived measures
- Naming conventions that scale (Base, %, YoY, _Hidden) — AI matches your patterns
- A repeatable method for prompting AI for DAX: grain, edge cases, naming and folder
- The verification habit: catching wrong context transition, missing edge cases and silent aggregation errors
- Using AI to refactor: nested IFs into SWITCH, variables, meaningful comments
- DAX best practice, AI-reinforced: VAR by default, consistent formatting, comments that state logic and grain
- Time intelligence done right: current vs prior period, variance, YTD/QTD/MTD, MoM, YoY
- Lab: AI-build a full time-intelligence measure family, then audit it against the checklist
Stakeholder Discovery & the Right Business Questions
- Requirement-gathering beyond ‘what fields’ — the actual decision, its cadence and what ‘good’ looks like
- Explanatory vs exploratory, decided up front (senior vs operational audiences)
- Translating a vague ask into a specific, buildable spec
- A discovery-call script and a requirement-capture template
- Lab: a mock stakeholder interview and a one-page brief
Design Foundations: UI/UX, Visual Hierarchy & Data-Viz Best Practice
- UI vs UX for business reports: usability, accessibility and visual consistency
- Visual hierarchy: guiding the eye with size, position, weight and contrast
- Whitespace, grid and alignment — why space is a design element, not wasted room
- Data visualization best practices: the right chart, honest encoding, less clutter
- Pre-attentive attributes and Gestalt principles for faster comprehension
- Colour and typography as an accessible, on-brand system
- Lab: critique and redesign a cluttered report page against these principles
Wireframing in Figma & AI-Generated Themes
- Figma wireframing for BI: page structure, KPI and filter placement, drill pages
- Components and a style library for repeatable wireframes, not one-off sketches
- Client sign-off on the wireframe before build, so changes are cheap
- Generating a reusable Power BI theme JSON with AI: palette, fonts, card and table styles
- A small library of theme variants (client brand, a house style)
- Colour strategy: one or two deliberate accent colours, not eight competing
- The importance of gray, and colour as encoding not decoration
- Choosing the right visual for the data relationship you are actually showing
KPI Design: What to Show and the ‘So What’
- Selecting KPIs: does it drive a decision, and does someone own the action if it moves
- The ‘so what’ structure: number → comparison → so-what statement
- How many KPIs is too many — cutting to the handful that earn a place
- Designing the KPI card: primary number, trend, comparison and a one-line insight
- Lab: cut a 15-metric candidate list to 5 KPIs, each with a so-what
Advanced Visualization Craft: SVG, HTML & Deneb — Knowing Which to Use
- Native visuals first, always — the decision tree before reaching for custom
- From slicer panels to visual filters and guided navigation
- Upgrading tables with SVG and HTML: in-cell bars, sparklines, conditional icons
- HTML KPI cards vs native cards — when each one wins
- When to reach for Deneb, and when it is not worth the spec-authoring cost
- Using AI to draft SVG measures, HTML markup and Deneb specs — with the same verification discipline
- Lab: three passes on one page (native, SVG/HTML, Deneb) and make the call on which ships
Dashboard Architecture & Storytelling
- The overview-to-detail structure and a deliberate drill path
- When a page needs detail vs stays a clean summary, tied to the audience split
- Navigation design: consistent structure, breadcrumbs and bookmarks that guide
- Turning a set of good pages into one coherent product
- The 90-second test: would a stakeholder know what they need and what to do next
Capstone: One Business Question, Start to Finish
- Discover: stakeholder brief, explanatory vs exploratory call
- Model: readiness pass and AI-assisted DAX with proper folders and time intelligence
- Set up the tooling: MCP, PBIP and GitHub for an agent-assisted build
- Design & wireframe: UI/UX principles, a Figma wireframe and an AI-generated theme JSON
- Design KPIs: cut to the right KPIs with so-what framing
- Build visuals: native-first, SVG/HTML where justified, Deneb only where it earns it
- Architect & deliver: overview-to-detail, navigation and the 90-second test, presented as a stakeholder walkthrough
How the Training Is Delivered
The course is practical, lab-heavy and built around real decisions — delivered live by a senior practitioner, in Dubai or online.
- Instructor-led, live sessions with a senior practitioner
- Hands-on labs at every stage — you build the pipeline yourself
- Around 36 to 42 hours across 8 modules plus a capstone
- Taught as decision frameworks — when to use what, not feature tours
- A portfolio-ready case study and follow-up support
Power BI in the Age of AI — FAQ
Who is this course for?
Practitioners who already know core Power BI — relationships, basic DAX and basic visuals. It does not repeat the basics; it is about how to approach the work using AI, with proper design and business judgment.
How is this different from a normal Power BI course?
It is taught as decision frameworks — when to use what — not a feature tour. Most of the weight is on AI-directed DAX and model structure, and AI-directed design (theme generation, colour, KPI cards, table upgrades), stitched together by stakeholder discovery and dashboard architecture.
Do I need to be a developer or designer?
No, but you need core Power BI — relationships, basic DAX and basic visuals. We build the AI, design and business-judgment layers on top of that.
How long is the course?
About 44 to 50 hours across nine modules plus a capstone, delivered as a corporate intensive or a part-time cohort.
Will I build something I can show?
Yes. The capstone takes one business question from discovery to a finished report — brief, wireframe, theme file, model and dashboard — a portfolio-ready case study you can use as a sales asset.
Is it available online or in person?
Both — in-person across Dubai and the UAE, and remotely for distributed teams.
Ready for Power BI in the age of AI?
Book a free consultation and we will tailor Power BI in the Age of AI to your team’s level and goals.
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