case study · 2026

Enterprise ad sales workflow prototype

I designed and prototyped connected workflows for PM and stakeholder review.

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role: Product Designer
team: Ad Sales Technology
contribution: UX design, interaction design, and AI-assisted prototyping.
tools: Figma, Claude Code, Vercel, GitHub, AI-Assisted Pipelines
status: High-fidelity prototype using mock data. No live BigQuery or Salesforce integration.

Create a Campaign: deal setup, plan type, and budget in one screen, with an Import With AI tab.

Context

the problem

Paramount’s ad sales ran across 5 disconnected legacy systems. Sales reps manually rebuilt advertiser records across tools, causing data fragmentation and corrupting revenue forecasting across a $5B business.

the solution

I designed and prototyped workflows for a proposed unified ad sales platform, addressing fragmentation across five legacy systems. The goal was to make the advertiser record a single source of truth across connected workflows.

Accelerated technical alignment

Shifted from static handoffs to production-like prototypes, collaborating directly with engineering leads in live sessions to validate technical feasibility and edge-case data flows in real time. I used working prototypes to review workflows with my PM and stakeholders.

I wrote structured design specifications covering layout, CSS tokens, states, permissions, and edge cases. I used Claude Code to build a working HTML prototype, then reviewed and refined each build against those specifications. I chose carefully where to spend design time, and I assessed each build against my own standard, because the accountability for the design stays with me.

View full journey ↗

Trade-offs & architectural decisions

Every design decision in this platform was driven by three simple questions every sales rep needs answered:

The three questions behind the decisions below: does the record exist, can we trust it, and who acts next.

Solving data quality at entry

The Decision: Block duplicate records before they are created.

How It Works: I prototyped duplicate-record checks using mock data to explore how potential matches could be surfaced during advertiser creation.

Why It Matters: The goal was to catch duplicate records at entry before they could affect downstream revenue reporting.

Progressive disclosure for deep taxonomies

The Problem: Media buying involves dozens of parameters (network restrictions, time blocks, exclusions) that usually result in massive, unreadable forms.

The Trade-off: Instead of showing 60+ options at once, I used tag-based progressive disclosure (CBS News ✕, 5AM–9AM ✕).

Campaign setup with tag-based restrictions: the default view shows the primary selections and the long tail opens on request.

Key UX Choices:

3-Step Task Chunking for setup
Inline Tags for instant visual auditing
Collapsed Tail Lists/ default views to keep the UI clean.

Predictive feedback & tiered error handling

Budget deliverability states, from Excellent to Unavailable, each with a status bar and recommended fixes when delivery drops.

Visual Health States: Uses segmented progress bars and semantic colors (Green/Yellow/Red) to show deliverability status instantly.
Prescriptive Fixes: Surfaces direct actions ("Choose Peak Placement", "Expand audience") when deliverability drops.
Tiered Warnings: Uses soft inline banners for minor risks and blocking modals only for critical budget/targeting conflicts.

The same narrow-targeting warning as a blocking modal and as a soft inline banner.

Narrow targeting rules can accidentally starve campaign delivery, causing missed revenue targets

→

Engineered live data feedback components to catch issues early.

Inline checkpoints over review pages

The campaign builder with three checkpoints (targeting, preferences, review) in one flow instead of a separate review page.

Traditional review pages are passive text dumps users blindly click through. I replaced them with a progressive checkpoint architecture.

What I learned

In next steps I would instrument flow tracking and run A/B testing to capture task completion speeds, drop-off rates, and clear ROI metrics for executive leadership.

This project helped me learn to use live code as the ultimate alignment tool.