Impact & Evaluation Manager
Collect and manage program data
What You Do Today
Oversee data collection systems—client intake forms, service tracking, outcome surveys, and follow-up assessments. Ensure data quality, train staff on data entry, and manage databases.
AI That Applies
AI validates data quality in real-time, flags missing or inconsistent entries, and automates data cleaning processes. NLP extracts structured data from narrative case notes.
Technologies
How It Works
The system ingests narrative case notes as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Data quality monitoring becomes continuous and automated, catching issues at the point of entry rather than during analysis.
What Stays
Getting frontline staff to embrace data collection as part of their work, designing forms that capture meaningful information without burdening service delivery, and maintaining data systems require organizational change management skills.
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 collect and manage program data, 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 collect and manage program data 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 collect and manage program data?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Who on our team has the deepest experience with collect and manage program data, 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 collect and manage program data, 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.