Research Administrator
Support faculty with grant proposal development and submission
What You Do Today
Help researchers prepare and submit grant proposals — budgets, institutional forms, compliance documentation, and subcontract agreements. Navigate sponsor-specific requirements and institutional approval chains.
AI That Applies
AI auto-populates standard proposal sections, generates compliant budgets from templates, checks submissions against sponsor requirements, and identifies common reasons for proposal rejection.
Technologies
How It Works
The system tracks learner progress, competency assessments, and engagement patterns across the learning environment. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — compliant budgets from templates — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
Proposal preparation accelerates. Budget calculations and compliance checks happen in minutes instead of hours.
What Stays
Working with a stressed PI on a tight deadline — helping them articulate their research vision in budget terms — requires patience, expertise, and relationship management.
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 support faculty with grant proposal development and submission, 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 support faculty with grant proposal development and submission 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 training programs have the highest completion rates, and which have the lowest — what's different?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“How do we currently assess whether training actually changed behavior on the job?”
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.