1. Design a purpose-driven AI data schema

AI performs better with fewer, clearer choices. Lean schemas reduce ambiguity, prevent incorrect field selection, and improve Copilot’s ability to map user questions to the right data. A focused schema also improves performance and makes the model easier to govern over time.

• Best practice
• Expose only the tables, columns, and measures that are meaningful to business users. Remove technical artifacts such as surrogate keys, staging tables, audit columns, and intermediate calculations.
• Why it matters
• AI performs better with fewer, clearer choices. Lean schemas reduce ambiguity, prevent incorrect field selection, and improve Copilot’s ability to map user questions to the right data. A focused schema also improves performance and makes the model easier to govern over time.

2. Use descriptions to add business context

Field names alone are not enough for AI to infer meaning.

• Best practice
• Add concise, business-oriented descriptions to tables, columns, and measures. Clearly explain what the data represents, how it should be interpreted, and—where relevant—how calculations are derived. Focus on intent, units, and assumptions rather than technical implementation.
• Why it matters
• Power BI Copilot relies heavily on descriptions to interpret metrics and generate explanations. Missing or vague descriptions often result in incorrect narratives, misapplied filters, or misleading calculations.

3. Align the model with business language using synonyms

Synonyms help Power BI Copilot understand how users naturally refer to data.

• Best practice
• Add 1-2 meaningful synonyms per field that reflect how stakeholders naturally refer to metrics and dimensions. Use business-approved terminology and avoid assigning the same synonym to multiple fields.
• Why it matters
• Effective synonyms improve prompt resolution and reduce friction between user intent and AI interpretation. This leads to more successful queries and a more intuitive Copilot experience.

4. Establish clear AI instructions

AI Instructions act as guardrails that guide Power BI Copilot behavior, especially when metrics or terminology can be interpreted in multiple ways.

• Best practice
• Use AI instructions to define business rules, clarify ambiguous terms, and set boundaries for interpretation. Be explicit and unambiguous. Include examples to reinforce intent and keep instructions up to date as definitions evolve.
• Why it matters
• Clear instructions prevent incorrect assumptions and reduce AI hallucinations. They ensure Copilot consistently applies the same logic that human analysts would use.

5. Standardize critical answers with verified responses

Not every business question should be answered dynamically by AI. Verified answers are pre-approved, fixed responses that Copilot returns when users ask specific high-value or high-risk questions. 

• Best practice
• Use verified answers for important KPIs and frequently asked questions. Associate multiple trigger phrases with each verified answer and routinely validate both the visual and its underlying logic.
• Why it matters
• Verified answers ensure everyone sees the same result for critical questions. This improves consistency and builds trust in AI-generated insights.

6. Model relationships and hierarchies intentionally

AI relies on relationships to understand how data connects and aggregates.

• Best practice
• Ensure relationships are accurate and active. Define explicit hierarchies (e.g., Year -> Quarter -> Month) to support natural drill-down behavior. Avoid ambiguous or bi-directional relationships unless they are explicitly required and well understood.
• Why it matters
• Incorrect relationships lead to incorrect insights. Well-defined hierarchies improve narrative clarity, aggregation accuracy, and Copilot’s ability to interpret phrases like “by region” or “over time.”

7. Improve security with RLS and OLS

Security settings determine what Power BI Copilot is allowed to return and should be treated as a top priority.

• Best practice
• Always implement Row-Level Security (RLS) and Object-Level Security (OLS). Never rely on hidden fields as a security mechanism. Evaluate Copilot responses across different user roles to ensure permissions are consistently enforced.
• Why it matters
• Misconfigured security can expose sensitive data at scale. Copilot-generated insights must respect the same access controls as traditional reports and adhere to governance standards.