Research Administrator
Track and report on research portfolio metrics
What You Do Today
Compile reports on research expenditures, proposal activity, award rates, and funding trends for institutional leadership. Identify patterns in funding success and areas for strategic investment.
AI That Applies
AI auto-generates portfolio dashboards, benchmarks research performance against peer institutions, and identifies trends in funding agency priorities that could inform institutional strategy.
Technologies
How It Works
The system aggregates data from multiple operational systems into a unified analytical layer. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The output — portfolio dashboards — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
Portfolio reporting becomes automated and more insightful. You provide strategic intelligence rather than just activity reports.
What Stays
Translating research data into strategic recommendations — and advising leadership on where to invest in research capacity — requires institutional knowledge and strategic thinking.
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 track and report on research portfolio metrics, 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 track and report on research portfolio metrics 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
“Which of our current reports are manually assembled, and how much time does that take each cycle?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“What questions do stakeholders actually ask that our current reporting doesn't answer?”
They understand the workflow dependencies that AI tools need to respect
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.