Impact & Evaluation Manager
Support continuous program improvement
What You Do Today
Work with program teams to use evaluation findings for improvement. Facilitate learning conversations, help teams interpret data, and develop action plans based on evidence.
AI That Applies
AI identifies specific program components most strongly associated with outcomes, suggests evidence-based improvements, and tracks whether changes lead to improved results.
Technologies
How It Works
The system ingests whether changes lead to improved results as its primary data source. 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 results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Improvement recommendations become more targeted with AI identifying specific factors driving outcomes.
What Stays
Facilitating honest conversations about program effectiveness, building staff capacity to use data, and creating a culture of learning rather than blame require human facilitation and organizational development 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 support continuous program improvement, 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 continuous program improvement 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 support continuous program improvement?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Who on our team has the deepest experience with support continuous program improvement, 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 support continuous program improvement, 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.