Context & Problem

Pricing managers, analysts, and sales teams often struggled to:

  • Navigate complex dashboards just to find a single answer.
  • Translate insights into next steps across multiple tools.
  • Ensure actions were compliant with company rules and permissions.

This slowed decision-making, reduced trust in insights, and created extra dependency on technical roles.

AI Copilot Interface

Goals

  • Boost productivity: Reduce time from insight to action by at least 20%.
  • Increase adoption: Make insights universally accessible via natural language, not buried in dashboards.
  • Ensure trust: Provide clear confirmations, guardrails, and permissions.
  • Empower scale: Allow creation of reusable agents and workflows for recurring needs.

Hypothesis: If we embed a conversational AI assistant directly into the dashboard, users will move from "question" → "insight" → "action" in a single flow, cutting manual effort and enabling faster, more confident pricing decisions.

Design Process

1. Research

  • Conducted interviews with pricing managers and GTM teams.
  • Found users wanted conversational access to data but worried about accuracy and compliance.
User Research Discovery Survey

2. Ideation & Prototyping

  • Explored interaction models: persistent chat vs modal assistant.
  • Mapped user journeys: "Ask → Validate → Act → Confirm."
  • Designed early concepts balancing simplicity for casual users with depth for power users.

3. Testing & Iteration

  • Ran usability tests with internal stakeholders.
  • Key finding: trust depended heavily on clear confirmations before committing changes.
  • Iterated on error handling, permission visibility, and context retention between sessions.

Solution

Copilot introduces a seamless flow from question to action, embedded across dashboards.

  • Universal Access: Always available from the dashboard, reducing navigation overhead.
  • Natural Language Queries: Users ask questions in plain English; Copilot translates to queries and outputs results.
  • Context Passing: Copilot carries context across dashboards, avoiding the need to "re-ask" after switching views.
  • Action Execution: Suggested actions appear alongside insights; users confirm before execution.
  • Agent Creation: Users can save repeat queries or actions as reusable "agents," automating recurring tasks.
Conversational UI Sales Insights
Agent Workflow Builder Canvas

Trust & Guardrails

  • Role-based permissions: Actions limited by user role.
  • Confirmation flows: Every action requires explicit user confirmation.
  • Transparent messaging: Copilot explains what it will do before execution.
  • Error handling: Friendly fallback messages with options to refine queries.

Outcomes & Impact

~20% Productivity Lift GTM messaging workflows during pilot phase showed significant time savings.
Faster Time-to-Insight Questions answered instantly, without switching dashboards.
Improved Adoption Non-technical users reported higher confidence in using the tool daily.
"Now I can ask Copilot directly instead of waiting for an analyst to pull numbers." — Sales Manager

Reflection & Next Steps

  • What I learned: Designing for trust was as important as designing for speed. Clear confirmations and permissions built confidence in adoption.
  • Challenges: Balancing flexibility (natural language) with governance (role-based limits) required iterative testing.
  • Next steps: Expand Copilot's capabilities with AI-driven proactive suggestions and tighter integration with external CRM tools.

My Role

As Lead Designer, I was responsible for UI components, interaction entry points, and signature flows. I collaborated closely with PMs, engineers, and AI researchers to define capabilities and guardrails, creating prototypes in Figma and testing with internal users before release.