AI Pulse Survey Analyzer turns raw employee pulse survey data into themes, sentiment, and action items. This post runs one small synthetic CSV and shows the exact output structure you can expect.
Model
AI Pulse Survey Analyzer on Wiro
Input (sample CSV)
File used: pulse-survey-sample.csv
Output summary
Employees express frustration with frequent interruptions, shifting priorities, and inconsistent support across teams.
Main themes
- Meeting overload and distractions
- Inconsistent prioritization and communication
- Workload management and resource allocation
Sentiment breakdown
| Positive | 40% |
|---|---|
| Negative | 60% |
Top suggestions
- Implement time-blocking for focused work sessions
- Establish clear weekly priority frameworks
- Improve cross-functional coordination during project planning
Top requests
- Develop a standardized escalation process
- Provide additional training resources for new sales hires
- Create a more defined career development path for design roles
Verbatim output (JSON)
{
"main_themes": [
"Meeting overload and distractions",
"Inconsistent prioritization and communication",
"Workload management and resource allocation"
],
"sentiment_breakdown": {
"positive": 40,
"negative": 60
},
"suggestions": [
"Implement time-blocking for focused work sessions",
"Establish clear weekly priority frameworks",
"Improve cross-functional coordination during project planning"
],
"requests": [
"Develop a standardized escalation process",
"Provide additional training resources for new sales hires",
"Create a more defined career development path for design roles"
],
"overall_summary": "Employees express frustration with frequent interruptions, shifting priorities, and inconsistent support across teams.",
"filename": "cb09817a8b97ab3bab6e6cfbf571b8d6.csv"
}Notes
- Use anonymous IDs in real surveys. Do not upload personal data.
- Run the tool per team or per month to track trends over time.
- Turn suggestions into owners and due dates so feedback closes the loop.
