Finance teams work with the most sensitive data in any organization, yet they're often stuck in manual, spreadsheet-driven processes.

The challenge isn't just technology adoption—it's about navigating steep learning curves, maintaining governance and control, overcoming IT dependencies, and translating complex finance logic that lives in people's heads or Excel files into scalable, secure solutions.

Without a clear strategy, attempting to modernize these manual processes risks creating more technical debt rather than solving the underlying problems.

Microsoft Fabric and Copilot offer a path forward—low/no-code tools that finance teams can own and operate, with AI assistance to accelerate development while maintaining full control.

Recent innovations like Model Context Protocol (MCP) servers enable AI agents to help not just design but to actually build and maintain solutions through natural language, dramatically reducing implementation time while preserving governance and quality standards.

This hands-on training day walks through building a complete month-end reporting solution from scratch. You'll learn how to securely connect to ERP systems and spreadsheets, use AI-assisted notebooks and Copilot to consolidate and transform transactional data, implement medallion architecture (bronze/silver/gold layers) with appropriate security at each stage, enrich data models with the metadata and business context that makes AI effective, and integrate Copilot and Data Agents for natural language financial insights and commentary.

Full Day Agenda

** Morning Session (09:00 - 12:30) **

- Module 1: Environment Setup and Data Connectivity (09:00 - 10:30)

- Understanding the finance reporting challenge: Why manual processes persist and what's required to replace them
Fabric workspace provisioning and capacity allocation for financial workloads

- Hands-on Lab: Connecting to ERP systems with proper credential management, setting up secure connections to sample financial data sources; configuring authentication and managing connection strings; Initial data profiling to understand source data structures

-Coffee break (10:30 - 10:45)

- Hands-on Lab: Establishing data governance from day one: Applying sensitivity labels to workspaces and datasets, Configuring Purview policies for financial data, Setting up audit logging for compliance requirements

-Module 2: Building the Silver Layer with AI Assistance (10:45 - 12:30)

- Medallion architecture overview: Bronze, silver, and gold layers for financial data

- Introduction to Fabric notebooks and AI-assisted development

- Hands-on Lab: Creating bronze lakehouse with raw data ingestion; Using Data Factory pipelines for incremental extraction; Handling schema drift and source system changes; Implementing basic error handling and retry logic

- Hands-on Lab: MCP server setup and configuration: Installing Claude Desktop and configuring Fabric MCP server; Authentication and workspace connection; Testing basic operations through natural language commands

- Introduction to using AI agents for notebook development

-Lunch (12:30 - 13:30)

** Afternoon Session (13:30 - 17:00) **

- Module 3: Data Transformation and Silver/Gold Layers (13:30 - 15:00)

- Understanding financial data transformation requirements
Hands-on Lab: Building the silver layer with business rules
- Using AI-assisted notebooks to consolidate multi-entity transactional data
-Implementing data quality checks with a framework (e.g. Great Expectations)
- Applying business logic transformations for financial calculations

-Hands-on Lab: Creating the gold layer with dimensional modeling; Designing star schemas for financial reporting
Building dimensions (time, account, cost center, entity); Creating fact tables with appropriate grain; Implementing row-level security for multi-entity access control

- Coffee break (15:00 - 15:15)

-Module 4: Semantic Models and AI Integration (15:15 - 16:30)

- Designing semantic models for both traditional BI and AI consumption

- Hands-on Lab: Building and enriching semantic models: Creating DAX measures for key financial metrics (gross margin, variance analysis); Using MCP servers to accelerate measure development through natural language; Enriching models with metadata: descriptions, synonyms, display folders; Configuring AI instructions and other metadata for financial terminology; Building calculation groups for time intelligence (YTD, QTD, Prior Year)

- Hands-on Lab: Copilot and Data Agent integration: Testing Copilot for natural language queries on financial data; Building Data Agent skills for variance commentary and insights; Creating conversational interfaces for board pack preparation; Validating AI-generated insights against known calculations

Module 5: Production Deployment and Best Practices (16:30 - 17:30)

- Security considerations: Row-level security, column masking, audit trails
-Performance optimization: Incremental refresh strategies, aggregations, query optimization
- Governance and compliance: Purview integration, lineage tracking, sensitivity labeling
- Deployment roadmap: Moving from development to production
- Monitoring and maintenance: Capacity metrics, pipeline health, data quality alerts
- Common pitfalls and troubleshooting strategies
- Resources and community support for continued learning
- Open Q&A

What attendees will take home:

Working Fabric workspace with complete month-end reporting solution

MCP server configuration templates and starter prompts for financial scenarios

Semantic model templates with financial calculation patterns

Power BI report templates for month-end board packs

Deployment checklists and governance frameworks

GitHub repository with all lab materials, sample data, and documentation

By the end of this training day, you'll have practical experience building AI-powered financial reporting in Fabric—with a clear implementation path that can be applied to your organization's specific requirements without requiring specialized data engineering skills.

Target audience: Data engineers, Power BI developers, database administrators, and BI professionals supporting finance departments or building analytical solutions for sensitive data environments.