---
artifact: survey-analysis
version: "1.0"
created: 2026-05-21
status: complete
context: Project-management SaaS - validating the hypothesis that users would adopt an AI meeting-notes-to-tasks feature (N=240)
---

# Survey Analysis: AI Notes-to-Tasks Adoption Survey

> This is an illustrative survey analysis. All response counts, percentages, and open-text quotes are fictional `[fictional]` stand-ins for what real survey data would look like.

## Executive Summary

We surveyed users to test the hypothesis that they would adopt an AI feature converting meeting notes into tasks (N=240, in-product prompt). Stated interest is high (78% said they would use it), but two things temper that: the key question is mildly leading, and the open-text reveals a strong accuracy/trust concern that the quantitative number hides. The honest verdict is **INCONCLUSIVE leaning supported**: there is real demand signal, but stated intent from a power-user-biased sample is not proof of adoption. Confidence: Medium. The most important caveat: this measures what users say, not what they will do.

## Survey Methodology Summary

- **Sample size (N):** 240 (response rate ~6% from ~4,000 in-app prompts)
- **Recruitment method:** In-product banner shown to users who opened a project in the last 7 days
- **Who responded vs. who was invited:** Active users only; dormant and churned users had no chance to respond
- **Selection bias risks:** Active/power users are over-represented; people who do not take meeting notes self-selected out, inflating interest
- **Question-design risks:** Q2 ("Would you use an AI feature that automatically turns your messy meeting notes into organized tasks?") is mildly leading - it pairs a pain ("messy") with a benefit ("organized")

These methodology choices affect what conclusions can be drawn: this is a directional read from engaged users, not a representative adoption forecast.

## Per-Question Analysis

| Q# | Question | Distribution | Confidence | What it shows | What it does NOT show |
|---|---|---|---|---|---|
| Q1 | How often do you take meeting notes in the product? | Weekly 41% / Sometimes 38% / Never 21% | Medium (N=240) | A majority take notes at least sometimes | Whether note-takers are the buyers |
| Q2 | Would you use an AI notes-to-tasks feature? | Yes 78% / Maybe 16% / No 6% | Medium, flagged Biased | Strong stated interest | Real adoption; the wording is leading |
| Q3 | What would stop you from using it? (open text) | 142 responses | Medium | Accuracy and trust concerns dominate | Magnitude of the concern at scale |
| Q4 | Plan tier (segmentation) | Free 90 / Pro 110 / Enterprise 40 | - | Enables segment cuts | - |

> Q2 is reported but flagged Biased. Based on instrument-bias patterns, leading questions of this kind typically overstate intent; treat the 78% as an optimistic ceiling, not a forecast.

## Persona / Segment Breakdown

| Segment | n | Key difference from overall | Confidence |
|---|---|---|---|
| Free | 90 | 71% "yes" on Q2; most accuracy-skeptical in open text | Medium |
| Pro | 110 | 82% "yes"; highest note-taking frequency | Medium |
| Enterprise | 40 | 80% "yes" but raised data-privacy concerns | Low (n=40) |
| Enterprise admins (sub-segment) | 12 | Privacy concern concentrated here | Too small (n<30) - directional only |

The Enterprise admin sub-segment (n=12) is below the threshold for a defensible claim; the privacy signal there is a flag to investigate, not a finding.

## Open-Text Thematic Clustering

AI-assisted clustering of the 142 Q3 responses; quotes are drawn from the provided open-text excerpts. Mention counts are approximate.

| Theme | Approx. mentions | Representative quotes | Valence | Contradicts quant pattern? |
|---|---|---|---|---|
| Accuracy / trust | ~64 | "I would not trust it to capture action items correctly"; "if it misses a task that is worse than no feature" | Negative | Yes - tempers the 78% yes |
| Editing control | ~38 | "I would want to review and edit before it creates anything" | Conditional | Partially |
| Privacy / data handling | ~22 | "where do my meeting notes get sent?" | Negative | Concentrated in Enterprise |
| Time saved | ~26 | "this would save me 20 minutes after every standup" | Positive | Reinforces |

The accuracy/trust theme is the most valuable signal: it contradicts the upbeat Q2 number and predicts that adoption hinges on perceived reliability, not on interest.

## Hypothesis Validation

| Hypothesis | Status | Evidence | Confidence |
|---|---|---|---|
| Users would adopt an AI notes-to-tasks feature | INCONCLUSIVE (leaning supported) | Q2 stated interest high (but leading + biased sample); open-text shows adoption is gated on accuracy/trust | Medium |
| Users will pay more for it | NOT TESTED BY THIS SURVEY | No pricing or willingness-to-pay question was asked | - |

## What the Data Does NOT Show

- **Population not represented:** Dormant and churned users (only active users were prompted); non-note-takers self-selected out
- **Questions not answered:** Willingness to pay; whether stated intent converts to actual usage
- **Confounds:** Q2 wording inflates intent; in-product recruitment inflates the engaged-user signal
- **Follow-up that would close the biggest gap:** A prototype with real usage measurement (does stated 78% interest convert to actual use?), and a neutrally-worded re-ask of Q2

## Prioritized Recommendations

| # | Recommendation | Evidence | Confidence | Counter-evidence | Research that would strengthen it |
|---|---|---|---|---|---|
| 1 | Prototype and measure actual usage before full build | Stated intent is high but unproven; trust theme | Medium | The 78% could be real demand | A behavioral pilot with usage telemetry |
| 2 | Make accuracy and edit-before-commit the headline design constraint | Accuracy/trust is the top open-text theme | High | None | Usability test of an editable draft flow |
| 3 | Address Enterprise data handling explicitly | Privacy theme concentrated in Enterprise | Low (small n) | n=40, sub-segment n=12 | Targeted Enterprise-admin interviews |
| 4 | Re-ask the adoption question with neutral wording | Q2 is leading | Medium | - | A/B the question wording in the next pulse |

## Next Steps

- Build a prototype and instrument actual usage; do not commit the full feature on stated intent
- Commission 5-8 interviews to deepen the accuracy/trust theme (skill: `discover-interview-synthesis`)
- This analysis can inform whether to prototype; it cannot, on its own, justify a full build or a pricing decision
