Build Dashboards with AI
Intermediate · Save 3-4 hours of design
Overview
Figure out what to actually put on an operational dashboard—the metrics that drive decisions, not vanity numbers—and how to structure it so people use it. This workflow helps you design the dashboard before you build it.
Best For
- Operations leaders
- Analysts
- Team leads
- Founders
Problem
Most dashboards fail by showing everything measurable rather than what matters. They become wallpaper—full of numbers no one acts on—because no one thought hard about which metrics actually drive decisions and how to lay them out for the people using them. The design thinking gets skipped in the rush to build.
Solution
AI helps with the design thinking before you touch a BI tool. Given what the dashboard is for and who uses it, it can propose the handful of metrics that genuinely support decisions, how to group them, and what to leave off. It can also help translate those into the right chart types and even draft formulas or queries. You provide the business context; AI structures a dashboard people will actually use.
Workflow Steps
- 1
Define the dashboard's purpose: what decisions should it support, and for whom?
- 2
Ask AI to propose the key metrics that serve those decisions—and what to exclude.
- 3
Have it group the metrics logically and suggest chart types for each.
- 4
Pressure-test against vanity metrics: does each number change a decision? If not, cut it.
- 5
Get help drafting the formulas, queries, or setup for your specific tool.
- 6
Build it, then check after a few weeks whether people actually use it—and prune what they don't.
Recommended Tools
Example Prompt
Help me design an operational dashboard before I build it. Purpose: [what decisions it should support] Who uses it: [audience and how often] Data available: [what you can measure] Produce: 1. The key metrics that genuinely support these decisions (and why each earns a place) 2. Metrics to deliberately leave OFF (vanity or distracting numbers) 3. A logical grouping/layout for the dashboard 4. Suggested chart type for each metric 5. Any derived metrics or calculations worth adding Bias toward fewer, decision-driving metrics over comprehensiveness. For each metric, be able to answer "what would someone do differently based on this?"