Design Researcher
Build and maintain a research repository
What You Do Today
Tag and organize past findings, make them searchable, ensure institutional knowledge persists beyond individual researchers
AI That Applies
AI automatically tags findings by theme/product/user segment, surfaces relevant past research for new projects
Technologies
How It Works
For build and maintain a research repository, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — relevant past research for new projects — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
Repository stays organized without manual curation. AI connects new research to past findings automatically
What Stays
Deciding what's worth preserving vs. what's outdated, maintaining research quality standards
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for build and maintain a research repository, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long build and maintain a research repository takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Operations or COO
“What data do we already have that could improve how we handle build and maintain a research repository?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Who on our team has the deepest experience with build and maintain a research repository, and what tools are they already using?”
They understand the workflow dependencies that AI tools need to respect
a frontline supervisor
“If we brought in AI tools for build and maintain a research repository, what would we measure before and after to know it actually helped?”
They see the daily reality that AI tools need to fit into
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.