90% of BI teams have started “using AI”. But only 10% have actually integrated AI into their BI processes. If you’re just asking DAX vs. Copilot, you’re just scratching the surface.

If you want AI to really help BI become stronger and not just “less coding”, you need to shift your mindset from chatbot to agent : AI that can read wireframes, interact with repositories, call internal tools, and even deploy reports to the workspace.

Here is a brief framework for that journey.

And how you can apply it to your team right away.

5 Building Blocks to Turn AI into a Real BI Assistant

1, LLM (Model)

Each model is strong in one way: reasoning, code generation, working with long contexts...

→ Choose according to specific BI problem, not according to trend.

2, Context

LLM does not understand your semantic model, table, or measure by itself.

→ You have to “teach” AI: provide metadata, spec, wireframe, business requirements.

3, Prompt

A good prompt is not just a question, it is a project brief : roles, goals, tools used, constraints,…

4, Tools

Without tools, AI only suggests text. With tools, AI can actually act (read and write files, call CLI, use repo...).

5, Environment

AI needs controlled space: which folder, which workspace, what permissions.

→ Security and control are here.

4 levels of maturity when applying AI to BI processes

(1) Chatbot Tools: Start Simple

⤷ Use ChatGPT/Claude to ask DAX, Power Query.

⤷ Use Copilot to suggest queries, visuals, and reports.

✔ Easy to start.

✘ Results are unstable, lack context, and difficult to control.

(2) Augmented Chatbot: Add context & tools

⤷ Let AI read metadata via PBIP.

⤷ Connect GitHub/Azure Repos to manage source.

⤷ Add MCP server, CLI, DAX rules, dashboard standards.

✔ Chatbots start to “know what you are doing”.

✘ Need more careful setup.

(3) Agentic Development: AI acting in the BI process

⤷ Find the location that needs to be fixed in the report.

⤷ Automate small tasks: formatting, labels, Power Query parameters.

⤷ Read wireframes from Figma, map fields with visuals, deploy & fix minor bugs.

⚠️ Power BI metadata is complex → AI needs supervision.

(4) Asynchronous Agent: Assign work, receive results

⤷ You describe the task → AI creates new branches, edits, tests, creates PR.

⤷ You review and merge.

✔ Suitable for small tasks with clear criteria.

✘ Do not replace people in big decisions.

Before "becoming an agent", check 3 factors

1, Security & Access

→ What does the AI/agent read? What does it write? Is there logging?

2, The real cost

→ It's not just about the pre-model. It's also about the infrastructure, toolchain, setup, maintenance...

3, Maturity of BI process

→ If naming, convention, and Git flow are not standard, AI will only make everything… more confusing.

Practical advice for BI teams

⤷ Don't expect AI to build dashboards from AZ.

⤷ Use AI for repetitive, labor-intensive, easily standardized parts .

⤷ Start with chatbot → augment with context/tools → then think about agent.

Key: Invest in context and review process. No need for the “best” model.

💬 Which level are you at in the 4 levels of applying AI to BI?

🔍 Which step in this journey are you struggling with?

Please leave a comment to discuss, or share the article if you find it useful for your teammates.

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