One research operations base for 85+ researchers and 2,000+ studies
A global software company runs a cross-functional research operations repository in Airtable, tracking nearly 2,000 studies across more than 85 researchers on 15 teams. Design research, UX research, research operations, product management, and engineering share one system for study intake, participant recruitment, budget tracking, and a findings repository, replacing tribal knowledge with self-serve discovery.
The problem
Research data was fragmented across tools and teams, with no way for managers or leadership to query what had already been studied. Budget tracking for studies and methods was manual, which made it hard to justify recruitment and tool spend to stakeholders.
A limited number of research-tool seats meant scheduling collisions were common, and researchers had no shared view of workload or pipeline across the organization. Findings lived in scattered documents, so tagging and linking facts to insights and recommendations was a manual, inconsistent chore.
What they built
Research operations, design research, and UX research teams built a shared Airtable base that tracks study intake, status, products, methods, and collaborators alongside participant recruitment and compensation requests. A research-tool seat and lane allocation system prevents scheduling collisions across the shared pool.
A linked findings repository connects facts to insights to recommendations, with a Miro integration for visual synthesis work. Weekly status rollups post automatically to Slack and email, and per-study and per-method budget tracking gives leadership the data to justify tool and recruitment spend.
The outcome
Managers and leadership now have a single source of truth that replaced tribal knowledge, with self-serve discovery letting stakeholders find past research without pulling a researcher off their current study. Capacity planning across 85+ researchers is now data-driven instead of guesswork.
The repository draws more than 10,000 stakeholder visits and sustains 71 active contributors, with proactive research operations support delivered through in-record comments rather than separate tickets or threads.
Inside the solution
Cross-Functional Research Operations & Insights Repository
Research & Knowledge Management
- Study intake and project tracking with status, methodology, and research questions
- Participant recruitment and compensation request tracking
- Research-tool seat and lane allocation to prevent scheduling collisions
- Findings repository linking facts to insights to recommendations
- Fragmented research data across tools and teams
- Manual budget tracking with no way to justify tool spend
- Scheduling collisions on limited research-tool seats
- Poor discoverability of past studies and tribal knowledge
- 95% researcher satisfaction
- Single source of truth replacing tribal knowledge
- Data-driven capacity planning and forecasting
- Faster secondary research and insights discovery
- Design Research
- UX Research
- Research Operations
- Product Management
- Engineering
- Design
- Miro
- Slack
Cross-Functional Research Operations & Insights Repository
Run every study (intake, method, recruitment, budget, and tool-seat bookings) in one shared repository, and trace any recommendation back through its insights to the raw facts that produced it. Automations create recruitment requests on intake, advance studies when recruiting completes, and email a weekly pipeline-and-conflicts rollup, while AI fields draft study snapshots, recruitment briefs, and stakeholder-ready insight summaries.
From tribal knowledge to a self-serve research repository
Copy the templates
Link studies to findings
Automate the rollups
FAQ
Frequently asked questions
Each study is a linked record with its own status, method, and collaborators, so views can filter to a team, a method, or an active pipeline instead of scrolling a flat list. Researchers only see what is relevant to them.
A self-serve findings repository. Facts link to insights and insights link to recommendations, so a stakeholder can search past research and trace the reasoning instead of asking a researcher to recall it.
Research-tool seats are tracked as a limited resource with linked bookings, so two studies cannot claim the same lane at the same time. The base surfaces conflicts before they happen instead of after.
Per-study and per-method tracking gives leadership the granularity to justify tool and recruitment spend with data, rather than an aggregate number nobody can defend line by line.
Not for the core repository workflows. The structured study and findings data built here is exactly the kind of foundation that would support AI-assisted synthesis in the future.