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Impact & Evaluation Manager

Produce funder reports and impact summaries

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What You Do Today

Write evaluation reports for funders, board members, and stakeholders. Translate data into compelling narratives that demonstrate impact while being honest about challenges and learning.

AI That Applies

AI generates report drafts from data, creates infographics and visualizations, and ensures reports address specific funder requirements.

Technologies

How It Works

The system aggregates data from multiple operational systems into a unified analytical layer. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — report drafts from data — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Report generation accelerates with AI drafting data-heavy sections and creating visualizations.

What Stays

Crafting impact narratives that are both compelling and truthful, and presenting challenges as learning rather than failure, require communication skill and professional integrity.

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.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for produce funder reports and impact summaries, understand your current state.

Map your current process: Document how produce funder reports and impact summaries works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Crafting impact narratives that are both compelling and truthful, and presenting challenges as learning rather than failure, require communication skill and professional integrity. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Canva tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long produce funder reports and impact summaries 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.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

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

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.