AI-Powered Custom Dashboards

Teams ignored the default dashboard, relying on ad-hoc report requests and spreadsheet exports instead.

I redesigned it into customizable, role-based dashboards with AI-supported summaries—shifting teams toward self-serve reporting, cutting export reliance in half, and increasing engagement by 71% during beta.

My role

Lead Product Designer & acting PM (5 months) Owned problem definition, research, and IA, while driving scope, tradeoffs, and delivery alongside 4 engineers. Led sprint planning and partnered with product leadership to align on MVP and roadmap direction.

Key outcomes

  • +71% dashboard
    engagement
  • 62% multi-dashboard
    users
  • 2.0 avg. interaction
    depth

Shifted teams away from exports and ad-hoc reporting toward self-serve dashboards

Phase 1 · Understand

Problem

Teams flooded us with ad‑hoc report requests or exported everything to spreadsheets. This slowed decision‑making, increased reporting overhead, and made the product look less "enterprise‑ready" in demos.

Objectives

  • Personalized dashboard creation
  • AI-generated executive reports

Research Insights

Research

  • Interviews: 10 users (6 power users, 4 stakeholders)
  • Core insight: Users skipped dashboards and went straight to exports—they wanted familiar KPIs surfaced as quick-scan, role-based views
  • Constraint: Sales needed immediate “wow” dashboards for demos

Design implications

  • Implication 1: Prioritize role-based templates over a single generic layout
  • Implication 2: Make customization fast and obvious so users stop exporting to spreadsheets
  • Implication 3: Design one or two “demo‑ready” views that give sales instant visual impact

Phase 2 · Decide

Design Approach & Decisions

  • Scope to core roles: Focused MVP on role-based templates instead of edge cases—pushed back on “everything in v1” to ship faster and cleaner
  • Constrain AI to build trust: Limited AI to verifiable dashboard metrics after testing showed low trust in black-box outputs

Defining Decision: Dashboards vs. AI-first

Stakeholders initially pushed for an AI-first approach—assuming users would prefer prompting for insights instead of navigating dashboards.

Based on user interviews, I pushed back. Most users didn’t know what to ask for upfront—they relied on familiar KPIs to orient themselves before exploring deeper insights.

I aligned the team around a hybrid approach:

  • dashboards as the foundation for visibility and trust
  • AI layered on top for summarization and reporting

This ensured users could see and verify their data first, while still benefiting from AI-generated insights.

Design Highlights

Role-based template selection for a new dashboard
Role‑based templates that match how teams work.
Dashboard editor showing drag, resize, and widget layout controls
Fast customization replaced spreadsheet workflows.
Rename and manage dashboards for different workflows
Multiple dashboards supported team-specific views.
Generate and export an AI summary report as a PDF
AI-generated reports enabled quick stakeholder updates.

Phase 3 · Evidence

Prototyping & Validation

I prototyped the end-to-end flow in Figma and tested it with 6 target users — from choosing a template to customizing widgets and generating an AI summary.

Iteration: more KPI cards, more control

Early concepts kept dashboards minimal. Research showed teams wanted a large set of KPI cards for the metrics they already knew we tracked—and they wanted to rearrange and edit them to match how they run their day. I expanded the design to support dense KPI cards with fast drag‑and‑drop editing and rearranging. That increased scope and timeline, but it removed the core adoption blocker: visibility and control.

  • 6/6 users said templates matched how they think about their work
  • All users created and customized dashboards without guidance

These sessions validated the core flow and allowed us to defer advanced metrics and broader AI safely.

Impact

Dashboards shifted from being ignored to becoming the primary way teams monitored their work.

They also improved demo effectiveness, giving sales teams immediate, high-impact views tailored to different audiences.

+71% dashboard engagement
62% multi-dashboard users
2.0 avg. interaction depth

Phase 4 · Forward Thinking

Next Steps

  • Depth for power users: Add advanced metrics and drill‑downs requested by power users before GA, while keeping default dashboards simple and focused.
  • AI expansion: Gradually expand AI to suggest cross‑dashboard insights once we can validate accuracy and trust with real usage data.