Bobby Shaw

Twenty teams.One week.A shot at Nvidia.Ours made the cut.

IQVIA challenged every business unit to define how they'd leverage a new partnership with Nvidia. Our team at IQVIA Digital answered with the beginnings of a platform-wide AI assistant — and moved from concept to delivery in months.

RoleLead Product Designer
Timeline1-week sprint
ToolsFigma, FigJam, Teams
Team3 designers
AIAdTechHealthTechB2B SaaS

Hero image

Context & Challenge

Media OS is IQVIA Digital's unified healthcare marketing platform — built for pharma companies, agencies, and publishers planning and activating omnichannel campaigns across HCP and consumer audiences. It consolidates what once required multiple vendors: audience planning, activation, and real-time measurement, all in one place. The data inside it is extraordinary. What was missing was a way to make it not feel like homework.

Users were doing what anyone does when software doesn't help them think — reverting to Excel, building activation strategies outside the platform, and peppering the data team with questions it shouldn't have to field. Media OS was exceptional at delivering data. It just wasn't helping anyone understand what to do with it.

The Design Challenge

Design a platform-wide AI assistant — in a single week — that could translate the complexity of Media OS data into clear guidance, recommended actions, and smarter decisions. The work needed to be compelling enough to win a competitive internal proposal, with a Nvidia partnership and executive attention on the line.

Images coming soon

Twenty teams pitched.
Five days to design.

One shot at Nvidia.

MyRole&Approach

I owned the end-to-end design implementation for a 5-day sprint. Working from a direction my lead had begun to shape, I built a rapid component library rooted in our recent platform rebrand — giving us the speed to explore freely without starting from zero.

Visual and interaction design across both prototypes
Figma variables and micro-interactions for production-ready feel
Daily stakeholder sync and alignment throughout the sprint
Co-defined user story, script, and real-world data scenarios
Post-pitch availability as the Nvidia-partnered MVP team moved forward

Phase 1

Discovery

PoC proposal + focus groups

Phase 2

Design

Rebrand foundation, custom components

Phase 3

Feedback

Storyboards + scripted scenarios

Phase 4

Prototype

Ozempic & Asthma demos

Post-Pitch

MVP

Nvidia-led development

TheWork

Discovery

Discovery was intentionally lean — the timeline demanded it. Our primary guide was the PoC proposal document, supplemented by multiple stakeholder focus groups throughout the sprint.

01

The AI needed to lead conversations proactively — surfacing suggestions before the user had to ask.

02

The existing design system needed a lighter, faster counterpart built for rapid exploration.

03

The assistant had to work across multiple entry points and user contexts — not a single fixed flow.

Rapid Component Library

A lighter design system purpose-built for this sprint — fast enough to explore freely, coherent enough to feel like a real product.

Four concepts entered.
Two survived.

The rejects sharpened everything.

Exploration

Exploration ran parallel to scripting — early concepts were deliberately held loose until the data scenarios were real enough to design around. Four prototypes were in play at once; focus groups cut two that were covering similar territory, landing us on Ozempic and Asthma as the strongest demonstrations of distinct use cases.

Four Prototypes → Two

Four concepts entered. Two left. Focus groups identified the redundancy — cutting early gave us more time to make the remaining prototypes exceptional.

Key Design Decisions

Should the AI wait for the user, or meet them halfway?

A blank prompt field gives users full flexibility — but most healthcare marketers aren't prompt engineers. Leaving them to figure out what to ask would recreate the exact problem we were trying to solve.

Option A — Wait and respond

Traditional chatbot. Clean, open input field. User drives every interaction. Simple to build, hard to use well.

Option B ✓ — Lead and suggest

Proactive AI that surfaces conditional prompts, context clues, and suggested actions based on where the user is and what they likely need next.

The call: Designing the AI to lead set the tone for everything that followed — not just in this prototype, but in how IQVIA Digital began thinking about AI patterns platform-wide. The goal was never a smarter search bar. It was a co-pilot.

How finished does a proof of concept need to be?

Five days to design two interactive, data-populated prototypes for Nvidia and executive leadership. Full fidelity wasn't possible. But something that looked half-baked wouldn't win anything either.

Option A — Wait for perfect

Slow down, close gaps properly, present fewer but more polished screens. Risk: running out of time before the story is told.

Option B ✓ — Strategic duct tape

Move fast, connect the pieces that need to connect, accept rough edges where they don't show. Keep the data real, keep the vision clear, keep it visually credible.

The call: The realism of the scripted data scenarios carried more weight than pixel-perfect polish. Leadership and Nvidia could see what it could become — which was exactly the point of a proof of concept.

Final Designs

Prototype — Ozempic

Prototype — Ozempic

Video has no sound

Prototype — Asthma

Prototype — Asthma

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The most extensive and well-thought out presentation we were able to see
the combination of data, resources, and design prototype made moving forward a solid choice.

Outcome&Impact

Selected from 20+

Chosen to move forward out of the largest cross-company AI proposal competition in IQVIA's Nvidia partnership launch

MVP built by Nvidia

A standalone AI application developed from our prototype — capable of reading IQVIA data and delivering summaries, suggestions, and user-specific recommendations

Platform AI standard

The work became the foundation for IQVIA Digital's approach to standardizing AI patterns and guidelines across Media OS

Round 2 in progress

The MVP is returning to the design team for a full implementation sprint, starting with the Audience module before expanding platform-wide

The proposal was selected over twenty others across the entire IQVIA organization. Nvidia built the MVP as a standalone application that could parse IQVIA data and hold intelligent conversations with users. That work is now coming back home — the team is preparing a full round of design, discovery, and user testing, starting in the Audience module before scaling across the platform. The sprint also quietly established a new role: the designer leadership calls when the timeline is impossible and the stakes are high.

Limitless capability means nothing without a person worth building it for.

— Reflection