Impact & Evaluation Manager
Design and implement program evaluation frameworks
What You Do Today
Develop logic models, theories of change, and evaluation plans for each program. Define measurable outcomes, select appropriate indicators, and design data collection methods that are rigorous but practical.
AI That Applies
AI suggests evaluation indicators based on program type and funder requirements, identifies validated measurement tools from research databases, and generates logic model templates.
Technologies
How It Works
The system ingests program type and funder requirements 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 output — logic model templates — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
Evaluation design becomes more informed by AI surfacing relevant indicators and validated tools from research literature.
What Stays
Designing evaluation frameworks that are both rigorous and feasible within program constraints, and that measure what genuinely matters for the people served, require evaluation expertise and deep program understanding.
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 design and implement program evaluation frameworks, 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 design and implement program evaluation frameworks 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 design and implement program evaluation frameworks?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Who on our team has the deepest experience with design and implement program evaluation frameworks, 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 design and implement program evaluation frameworks, 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.