This repository hosts practical, architecture-driven PDF documents focused on Copilot in Power BI.
The purpose of this workspace is to provide clear, honest, and technically grounded material for architects, BI professionals, and decision makers who want to understand how Copilot really behaves in enterprise scenarios — beyond prompts, demos, or marketing narratives.
The repository currently includes the following documents:
-
PowerBI_Copilot_Whitepaper_EN.pdf
English version of the whitepaper.
Intended for international teams, architects, and decision makers operating in enterprise and cross-country contexts. -
PowerBI_Copilot_Whitepaper_IT.pdf Italian version of the same content.
Intended for Italian-speaking professionals and local enablement scenarios.
Both documents share the same structure and technical content, adapted linguistically for their respective audiences.
The material focuses on topics such as:
- What Copilot in Power BI actually does — and what it does not
- The central role of the semantic model
- Semantic ambiguity and its impact on AI-generated answers
- DAX, metadata, relationships, and linguistic modeling
- Governance concepts such as AI instructions, verified answers, and discoverability
- Practical limits, trade-offs, and architectural implications for enterprise adoption
The perspective is intentionally architecture-first: Copilot is treated as an amplifier of the semantic model, not as a replacement for modeling, governance, or analytical responsibility.
- It is not a collection of prompts
- It is not a feature-by-feature reference
- It is not a marketing or hype-driven narrative
All content is grounded in real Power BI behavior, current platform capabilities, and hands-on experience in enterprise environments.
- Data & Analytics Architects
- BI / Analytics Engineers
- Power BI Modelers
- Technical Leads and Decision Makers
- Microsoft field and partner teams
A solid understanding of Power BI concepts (semantic models, DAX, governance) is assumed.
You are welcome to:
- Read and reference the documents for personal learning
- Use them in internal enablement or customer conversations
- Share them with colleagues and teams
- Quote or adapt sections with proper attribution
Feedback, discussion, and constructive challenges are always welcome.
Andrea Benedetti
Senior Solution Engineer, Data & AI – Microsoft
LinkedIn: https://www.linkedin.com/in/abenedetti/
These documents are shared for educational and enablement purposes.
They reflect practical experience and technical interpretation and do not represent official Microsoft documentation.