Industry
Life Sciences / Enterprise SaaS
SAAMA
AI-Powered Life Sciences Platform
Reframing fragmented clinical research workflows into a scalable enterprise platform that improved efficiency, reduced complexity, and made AI support more usable in high-stakes environments.
Role
Director of Product Design
Scope
8 designers, 3 researchers, cross-functional leadership
Key Outcomes
50% faster design-to-development, 65% faster task completion
The situation
Clinical trial teams were working across fragmented legacy systems, overloaded interfaces, and disconnected workflows. Critical information was spread across multiple tools, making routine work slower, harder to manage, and more error-prone.
The need was not just a better interface. It was a more coherent platform—one that could reduce cognitive load, support regulatory rigor, and make AI useful in everyday decision-making.
The challenge
The challenge was to simplify highly complex, data-dense workflows without oversimplifying the reality of clinical research operations. Teams needed faster access to information, clearer prioritization, and confidence in system guidance.
This required more than usability improvements. It required aligning workflow design, information architecture, governance, and platform consistency across an enterprise environment with multiple roles and evolving requirements.
My role
As Director of Product Design, I worked across product strategy, design leadership, and systems governance. I guided designers and researchers, aligned stakeholders across functions, and helped define a platform direction that could scale across modules, teams, and operational contexts.
How I operated
Clarified the real problem
We reframed the work from “modernizing screens” to improving end-to-end clinical workflows and making decision support more usable.
Structured the system
We used information hierarchy, modular patterns, and design system thinking to create a more coherent platform foundation.
Aligned stakeholders
I worked across product, engineering, and design to build shared understanding around priorities, constraints, and platform direction.
Drove execution
We translated strategy into production-ready systems, reducing rework and increasing consistency from design through implementation.
Key decisions
Explainability-first AI interactions
We treated AI as decision support, not a black box—designing for clarity, trust, and human oversight in high-stakes workflows.
Task-centric workflow structure
We moved the experience away from data-heavy screens toward workflow paths that matched how clinical teams actually operate.
Component-first platform design
We established reusable interaction and UI patterns that reduced fragmentation and improved scalability across product lines.
Outcomes
- Cut design-to-development cycle time by 50% by introducing clearer systems, governance, and execution workflows.
- Improved user task completion time by 65% through simplified workflows and stronger information hierarchy.
- Created a more consistent enterprise platform foundation through reusable design patterns adopted across product lines.
- Enabled AI-informed decision-making with greater transparency, usability, and regulatory confidence.
Reflection
This work reflects the kind of design leadership I bring to complex environments: not just improving interfaces, but building the systems, clarity, and operating structure needed to make platforms more usable, scalable, and strategically valuable.