Summarizing survey responses for the board
An assistant reads open-ended survey answers from clients or participants and groups them into themes with a few short example quotes. Staff review the themes before sharing with the board.
What it is
The helper reads open-ended survey answers from clients or participants. It groups them into themes with a few short example quotes.
Who it is for
Program staff who report to a board or a funder will find it helpful.
What you get
You get a themes summary that is quick for the board to read.
A real-life example
A job-training nonprofit asks participants to answer open questions at the end of a class. The director gets dozens of long answers. A helper reads them and groups them into themes. It picks a few short quotes for each theme. The board can read the summary in five minutes.
This is a made-up scenario. It does not describe a real organization.
How it works
- You: Collect survey answers with names removed.
- The AI: Reads every answer.
- The AI: Groups the answers into themes.
- The AI: Picks a few short quotes for each theme.
- You: Read the answers yourself to check the themes are fair.
- You: Share the summary with the board.
Time saved
Typical estimate
By hand: about 5 hours.
With AI: about 1 hour, plus your review.
These are rough guesses, not measured results. Your own times will vary.
Sample output
Example Survey themes summary
Survey summary: 42 answers Theme 1: Confidence grew. "I did not think I could do this, and now I can." Theme 2: Schedule was hard. "Classes at night would help." Theme 3: Want more hands-on practice. Suggestion: Consider offering one evening class next term.
Made-up names and numbers, shown only to give you an idea. Always read AI output before you use it.
What it involves
- Tools / helper type
- Survey export + AI summarizer
- Rough effort
- Medium (Low = an afternoon or less; Medium = a few days with some setup; High = a project)
Who should look it over
Remove names and identifying details before using any tool. Do not present AI themes as statistics.
Related use cases
Building a program-outcomes dashboard from case notes
The helper reads de-identified program data and case-note summaries.
The result: You get a monthly report draft for the director to check against real results.
Read more about Building a program-outcomes dashboard from case notesDrafting a first pass of a grant application from past reports
The helper reads your earlier reports and program descriptions.
The result: You get a draft to reshape, so you start with words instead of a blank page.
Read more about Drafting a first pass of a grant application from past reportsWriting donor thank-you notes with a personal touch
You give the helper a donor's giving history and notes on what that person cares about.
The result: You get drafts that a staff member edits and signs.
Read more about Writing donor thank-you notes with a personal touchExamples are for general information. Tools and features change, so check with the vendor, and keep a person in the loop for anything involving money, private information or people in your care.