2025
Turning research signals into scalable insight
A self-initiated, AI-assisted insight pipeline to connect research signals faster and build shared understanding across projects.
ROLE
Lead product designer
Self-initiated individual contributor
TEAM
Solo project
Consulted with designers and product partners
IMPACT
Reduced synthesis time from hours to minutes
Improved reuse of research insights across projects
Created a scalable knowledge foundation adopted by multiple teams
THE PROBLEM
Research happens — but insights don’t scale
Research was happening across many tools and teams, but insights were fragmented, hard to retrieve, and rarely reused—leading to repeated questions, duplicated effort, and lost learning over time.
01
Volume without synthesis
Research outputs accumulated across tools, making analysis slow and inconsistent.
02
Insights were disposable
Findings lived in decks and documents that were rarely revisited or reused.
03
No system for learning
Without a shared structure, teams struggled to connect patterns across projects and time.
SOLUTION
A structured insight pipeline from capture to action
I designed a repeatable insight pipeline that standardised how research moves from capture to synthesis, storage, and reuse—using AI to accelerate analysis while keeping human judgment central.
01. A structured process for knowledge management
The core challenge wasn’t generating insights—it was making them durable, searchable, and reusable across projects.
Defined clear stages from capture → analyse → store → share
Introduced consistent structures to make insights comparable
Ensured learning persisted beyond individual projects
02. AI-powered Notion proof of concept
I used AI to reduce synthesis time and increase consistency, enabling faster pattern recognition across large volumes of qualitative data.
Automated summarisation of raw research inputs
Topic-based insight retrieval across projects
Standardised outputs to support reuse and comparison
RETROSPECTIVE
From scattered feedback to strategic advantage
This self-initiated project reinforced the value of pairing intuition with data to drive real product impact. By turning fragmented feedback into a structured knowledge system, I demonstrated how individual initiative can create momentum and influence how teams work.
01
Process impact
Reduced synthesis time from hours to minutes through a structured, AI-assisted workflow.
02
People impact
Enabled cross-functional teams to work from a shared source of truth, reducing misalignment.
03
Business impact
Created a scalable knowledge system that reduced duplicated research and supported faster, better-informed decisions.
© 2025 Sara Quagliani. All right reserved