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Business Intelligence Transformed by AI

5 min read
Data Sci

Data Sci

AI answers ad-hoc questions. You design what questions are possible and which are trustworthy.

Business Intelligence Transformed by AI

TL;DR

  • AI can generate charts, answer "what's our top product by region?", and draft insights from data.
  • AI doesn't define your metrics, your semantic layer, or which questions are worth asking.
  • Use AI for ad-hoc exploration. You own the structure that makes exploration reliable.

BI used to be: build a dashboard, hope users look at it. AI shifts it: users ask in plain language, AI queries and visualizes. Faster. But only if the data is structured, the metrics are defined, and someone's validated that the AI's answers are right. That someone is you.

What AI BI Tools Do

Natural language to query:

  • "Show me revenue by product last quarter." — AI translates to SQL or semantic layer query. Returns a chart.
  • Great for ad-hoc. Depends on how well the data model is set up.

Auto-visualization:

  • Pick a chart type or let AI suggest. Faster than clicking through a BI tool.
  • AI doesn't know your conventions (e.g., red = bad, green = good). You may need to fix.

Insight generation:

  • "Sales dropped 10% this week." — AI detects and surfaces. Useful. Can also surface noise.
  • You tune: what's an insight vs. a fluctuation?

Report drafting:

  • "Summarize this dashboard for leadership." — AI drafts. You edit for accuracy and tone.

What You Still Own

Semantic layer:

  • "Revenue" — Gross or net? Which products? Which time zone? You define it.
  • AI uses what you've modeled. Garbage in, garbage out.

Metric definitions:

  • DAU, retention, LTV — Every org defines differently. AI will assume something. You make it explicit.

Governance:

  • Who can see what? What's published vs. exploratory? AI doesn't enforce policy. You do.

Asking the right questions:

  • Users will ask "what's our churn?" AI will answer. Is that the right question for the decision they're making? You help steer.

The Hybrid Model

  • Self-serve for simple queries — AI + semantic layer. Users ask. Get answers. You maintain the layer.
  • Curated for critical metrics — Executive dashboards, board reports. Human-built. AI might draft the narrative; you sign off.
  • Exploration for analysts — AI accelerates. Analysts validate. Findings get promoted to curated when proven.

Build dashboards. Hope users find them. Answer ad-hoc questions one by one. Backlog grows.

Click "BI With AI" to see the difference →

Quick Check

A stakeholder asks 'what's our revenue?' and the AI BI tool returns a number. What's your job?

Do This Next

  1. Audit your semantic layer — Can AI answer "revenue by product" correctly? What's missing? Fix one gap.
  2. Run a natural language query — Ask your AI BI tool something you know the answer to. Did it get it right? Document where it fails.
  3. Define "AI vs. human" for reports — What's auto-generated? What requires human review? Communicate it to stakeholders.