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Executive Director

Measuring and communicating impact

Enhances✓ Available Now

What You Do Today

Define what success looks like, collect outcome data, tell the impact story to donors and stakeholders, and prove that the organization is actually making a difference.

AI That Applies

AI aggregates program outcome data, generates impact visualizations, and creates donor-facing impact reports that connect giving to results.

Technologies

How It Works

For measuring and communicating impact, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — impact visualizations — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Impact reporting goes from a painful annual exercise to a continuous, visual narrative. Donors see the connection between their giving and results.

What Stays

Choosing what to measure and how to tell the story. Impact is more than numbers — it's the lives changed, told compellingly.

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 measuring and communicating impact, understand your current state.

Map your current process: Document how measuring and communicating impact works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Choosing what to measure and how to tell the story. 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 impact measurement platforms (Apricot, Efforts to Outcomes) 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 measuring and communicating impact 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 board chair or lead independent director

What data do we already have that could improve how we handle measuring and communicating impact?

They shape expectations for how AI appears in governance

your CTO or CIO

Who on our team has the deepest experience with measuring and communicating impact, and what tools are they already using?

They own the technology infrastructure that enables AI adoption

a peer executive at a company further along on AI adoption

If we brought in AI tools for measuring and communicating impact, what would we measure before and after to know it actually helped?

Their lessons learned are worth more than any consultant's framework

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.