1. Why do design standards matter for adoption?

Consistency and intuitive design reduce cognitive load, build trust, and make dashboards usable without extensive training.

2. How should documentation be written?

Use clear, concise, user-friendly documentation. Avoid writing that is too sparse to help or too detailed to read.

3. How should documentation be maintained over time?

Use iterative feedback loops by monitoring where users ask questions or report confusion, then improve documentation accordingly.

4. Why did GenBI emerge?

Because self-service BI created many unconnected dashboards, conflicting metrics, and quality issues. GenBI aims to make data interaction more accessible through natural language and assisted engineering.

5. What can LLM-based analytics do?

Conversational interfaces, automated insights, context-aware answers, and assisted BI engineering.

6. What are the main guardrails for AI-powered analytics?

Strong metric foundations, training with structured Q&A, security and privacy controls, fairness guardrails, and reliable testing through pilots.

7. How do we avoid “chat becomes the new support desk”?

Make the bot answer from owned knowledge:

• Only certified metrics and documented datasets
• Explicit “I don’t know” with routing
• Logging + monthly improvements to semantic layer metadata

Otherwise you just move the support load from analysts to prompt engineers.