Thermo Fisher Scientific
Enterprise UX Case Study

Thermo Fisher
Accelerator Platform

What started as a program visibility challenge became a decision-intelligence platform for tracking drug development, surfacing risk, and enabling faster action across DSD, DPD, and CTD.

Role
UX Strategy & Researcher
Scope
Program intelligence transformation
Programs
50+ drug Projects
Focus
Portfolio to task-level visibility
My role

I led the shift from a dashboard-led ask to a decision-architecture problem — reframing the challenge, aligning stakeholders, and designing a system that could support executives, program managers, and delivery teams through one connected experience.

01 — The Problem

The data existed.
The decision clarity did not.

A single drug program moved across multiple ecosystems, but tracking was fragmented across tools, spreadsheets, and manual reporting. Leadership struggled to answer a simple question: where exactly is my drug, and what risks are emerging?

Fragmented program tracking
Critical data lived across Smartsheet, mySupply, CRG, spreadsheets, and locally managed trackers.
High manual effort
Stakeholders depended on status calls and offline interpretation to understand program health.
Reactive risk visibility
Signals surfaced late, so escalations often happened after impact was already visible.
No lifecycle intelligence layer
There was no unified way to connect risk, schedule variance, dependencies, and downstream impact across the lifecycle.

"Where exactly is my drug, and what risks are emerging?"

Leadership question

"The issue wasn't reporting — it was the lack of lifecycle intelligence."

Strategic reframing

"Different teams saw different slices of reality, but nobody could see the full story in one place."

Experience insight
Decision architecture framework
From fragmented status tracking to lifecycle intelligence
The solution was designed to help users move from signal detection to diagnosis to action — without losing context across the drug development lifecycle.
01
Before
Fragmented oversight
Program data was dispersed, status understanding was manual, and risk interpretation depended on disconnected tools and meetings.
Leadership experience
Assemble the story manually
→
02
Transformation
Layered decision system
We structured the platform across portfolio, program, integrated timeline, and analytics views to support different mental models.
Design move
Design for diagnosis
→
03
After
Decision-ready visibility
Users could move from high-level oversight to task-level analysis, simulate changes, and act with greater confidence.
Business result
Faster, clearer action
↺
Key shift
We moved the conversation from "build a dashboard" to "design a system that makes complexity manageable."
02 — What We Built

A unified lifecycle intelligence platform

The solution connected portfolio oversight, timeline diagnostics, task-level action, and analytics into one experience that made program health and emerging risk easier to interpret and act on.

Cognitive design layer

One platform. Four cognitive models.

Research showed each role interprets the same system differently. We designed four interoperable views so users move from signal → diagnosis → action without losing context.

Hierarchy view
Portfolio → Program → Stage → Task
Executives drill down only as far as the decision requires.
PMO · Leadership
Gantt view
Integrated timeline intelligence
Schedule variance, critical path, and cross-functional dependencies.
PMO · CMC · PM
Kanban view
Task-level execution
Owner accountability, status signals, and escalation triggers.
PM · CMC
Analytics view
Risk & trend intelligence
Risk concentration, trend analysis, and scenario evaluation.
All stakeholders
Platform in action
drug-lifecycle-platform-walkthrough.mp4
Demo · 2024
Portfolio view Risk detection Gantt timeline Task drill-down Analytics
03 — Personas

Designed for different decision-makers
across the same program ecosystem

A key leadership move was recognising that the same information could not be presented the same way to every user. The platform had to support different cognitive models while preserving continuity.

Leadership
Portfolio oversight
KPI driven
Risk visibility
Cross-program clarity
"Show me which program needs attention, what the risk is, and what it means for the broader portfolio."
Needs
Portfolio health, risk concentration, escalations, and decision-ready KPIs.
Goals
Reduce decision latency and align leadership decisions faster.
Pain points
Fragmented reporting, inconsistent data, and lack of unified visibility.
CMC
Portfolio risk management
Multi-program view
Risk tracking
Dependency focus
"I need to see risk patterns across programs before they become escalations."
Needs
Cross-program visibility, risk signals, and dependency mapping.
Goals
Identify emerging risks early and manage portfolio stability.
Pain points
Late risk visibility and lack of pattern detection across programs.
PMO & PM
Execution
Timeline driven
Dependency tracking
Action focused
"Help me understand what slipped, why it slipped, and what gets impacted next."
Needs
Task-level visibility, dependencies, and schedule variance.
Goals
Diagnose issues quickly and take corrective action.
Pain points
Disconnected tools and hidden downstream impact.
04 — My Approach

Reframing the problem from reporting
to decision-making

Instead of designing one more reporting surface, the focus shifted to how users identify problems, diagnose them, and evaluate action across a complex program landscape.

01
Reframed the ask
Moved the conversation from building a dashboard to designing a layered decision architecture.
02
Defined experience layers
Structured the platform across portfolio, program, integrated timeline, and analytics views.
03
Designed for multiple mental models
Aligned the experience to how executives, program managers, and delivery teams each interpret the same system differently.
04
Made change legible
Introduced real-time analysis patterns so users could compare baseline vs proposed impact before approval.
05 — Execution & Leadership

Leading through ambiguity, complexity,
and multiple stakeholder needs

My contribution went beyond interface design. The work involved shaping product thinking, aligning expectations, and translating operational complexity into a design system that leadership could trust.

The real challenge wasn't design — it was changing mindset
The client initially approached this as a wireframe delivery exercise. Requirements were pre-defined and solution-driven, leaving little room for discovery.
Driving research-first thinking
I pushed for user interviews before design — making the case that we needed to understand how decisions are made, not just what screens are needed.
Fighting for synthesis time
Even after research, there was pressure to jump directly into wireframes. I ensured time was carved out for synthesis — turning raw inputs into actionable insights.
Shifting stakeholder mindset
The biggest shift was helping stakeholders understand that insight-driven design leads to better decisions — not just better screens.
06 — Impact

From reactive reporting
to proactive risk orchestration

The biggest outcome was not just better visibility — it was a more confident and more actionable way of managing program complexity.

Reduced decision latency
Teams spent less time assembling the story and more time evaluating what to do next.
Improved risk visibility
Emerging issues, downstream dependencies, and timeline implications became easier to interpret earlier.
Better cross-functional clarity
Leadership, program teams, and delivery users could operate from the same connected view of reality.
Stronger actionability
Users could move from identifying a problem to assessing proposed mitigation within one platform.
Scalable decision framework
The platform created a reusable pattern for structuring complex program intelligence at enterprise scale.
07 — Reflection
The real value came not from exposing more data, but from designing a system that helped people make better decisions faster.
This project reinforced a core leadership principle: complex enterprise products rarely fail because information is missing — they fail because information is not structured in a way that supports confidence, diagnosis, and action. The most important design move was turning complexity into something navigable.