People.ai

AI Deal Review Dashboard

Role: Lead Product Designer, UX Researcher

Duration: 3 months

Tools: Figma

System: Web App

Empowering enterprise sales teams with predictive AI insights for better forecasting and faster pipeline decisions.

I led the end-to-end UX for a predictive, AI-powered dashboard designed to help sales teams quickly surface deal risks and take action. In a landscape where fragmented tools and unclear signals slowed decisions, the design unified key insights into a clear, trustworthy interface, enabling faster, more confident pipeline reviews.

Challenge & Users

The Problem:

There was no reliable way to assess pipeline health at scale. CRM data was fragmented, self-reported, and often misleading. As a result:

  • Sales managers and leaders couldn’t confidently predict outcomes.

  • Sales reps lacked guidance on how to advance deals.

  • Forecast meetings became inefficient, repetitive, and reactive.

The User Personas:

A graphic comparing three sales roles with their descriptions, featuring illustrated profile icons of a sales manager, sales operations manager, and sales representative.

Key Insights

Through user interviews, workflow shadowing, and cross-functional discovery workshops, I identified four key insights across the sales process:

Unclear Deal Health

Sales reps marked deals as "on track" despite no recent engagement or missing key signals.

No Guided Action

Sales reps didn’t know what to do next: no system-level nudges or insights.

Fractured Tooling

Managers had to piece together information from multiple sources during forecast review calls.

No Accountability Loop

No way to follow through on suggested actions or coach effectively.

Proposed User Journey

The redesigned experience creates a more intuitive and focused workflow. By consolidating critical deal information into one place, surfacing clear, trustworthy indicators of deal health, and embedding follow-up tasks directly into the interface, the system supports faster, more confident decision-making and stronger accountability across teams.

This directly supports the needs of each user group:

  • Sales Managers gain quick visibility into at-risk deals and stronger forecasting confidence.

  • Sales Operations Managers get centralized tools to monitor seller activities.

  • Sales Reps receive timely guidance on which deals to prioritize and what steps to take.

Flowchart outlining sales process from alert reception, deal review, to post-deal actions. Shows roles of sales ops/manager and sales rep, with steps for viewing deals, reviewing, taking action, and notifications.

Design Principles Applied

To shape a dashboard that builds confidence and accelerates decisions, I grounded the design in three guiding principles. Each was validated through user research and directly shaped interaction, content, and layout decisions.

North Star: Unify fragmented information into a single source of truth.
The goal: surface reliable, explainable AI signals that help managers coach, not chase.

Clarity Over Complexity - Don’t show the AI model. Show what matters.

Explain the Why - Provide rationale for every insight to build trust.

Simple By Design, Power On Demand - Progressively disclose details

Key Features

Deal Health Snapshot

  • Simple health status: Healthy / At Risk / Needs Attention

  • AI-driven signals summarized into one visual

  • Color-coded risk tags like "Gone Silent" or "Missing Decision-Maker"

  • Helped triage deals in 1:1s instantly

A deal health report screenshot for FY21 Acme New Business, showing engagement level, owner, amount, close date, stage, and last week. Includes notes on meetings and buyer persona, with a dark background.

Insight Cards with Rationale

Paired each AI insight with the "why"

e.g. "No engagement from decision-maker in 14 days"

Screenshot of a meeting management interface showing upcoming meetings, including details such as date, time, and participants, with internal and external attendee lists.

Validation & Iteration

I explored several key concepts in early sketches to find UX opportunities and to ensure I was showing the most important information at the right level. My learnings from testing these concepts with users led me to my final design solution.

Filtering & Prioritization of deals

Hand-drawn wireframe of a user interface for managing North American deals, featuring filters for priority, account details, updates, and notes. Includes annotations and notes on system features.
Hand-drawn user interface sketch of a project management tool with sections for priorities, updates, and opportunities, featuring handwritten notes and labeled buttons.
Hand-drawn wireframe of a project management or feedback form with sections for team, closing, dead size, and save view options. Contains checkboxes, labeled insights, and notes for updates and charges.

Insight Display & Placement

Hand-drawn sketch of a user interface for managing deals, with sections labeled 'WA Deals' and 'Insight,' featuring dropdown menus, checkboxes, and list items.
Hand-drawn sketch of a user interface with multiple screens showcasing a project management or deal tracking application. Includes wireframes for different views with color-coded elements, annotations, and notes about features such as insights, updates, filters, and saved views.
Hand-drawn sketch of a digital dashboard interface with sections for insights, connections, a line graph, and an overview summary.

Insight Explainability & Coaching Triggers

Hand-drawn sketches of a user interface with notes and annotations, including wireframes of a mobile app or website displaying engaged personas, task stages, and a progress or engagement chart.
A hand-drawn sketch of a user interface with two main sections. The left section shows a narrowed view of a dashboard with icons, a bar graph indicating 80%, a line graph, and an envelope icon. The right section displays a more detailed view with a sidebar containing icons, a highlighted insight, a table of engagement levels with an exclamation mark icon, and handwritten notes mentioning stages, engagement, and a warning.

Impact

Insight Trust:

80%+ adoption of AI recommendations within 6 weeks

Upsell Opportunities:

This feature created major upsell opportunities for the company due to the positive feedback received from customers.

What I’d Do Next

  • Add real-time collaboration features for forecast prep

  • Incorporate AI-based coaching recommendations based on rep behavior

  • Layer in confidence scores or thresholds for AI predictions