Day 5: HR Analytics: Synthesizing Data, Surveys, and Reports | AI for HR

Engagement Survey Analysis

Annual engagement surveys produce hundreds or thousands of open-text responses. Reading them all takes days. AI can identify themes in minutes.

Survey Analysis Prompt

Analyze the following employee engagement survey
responses and identify key themes.

[PASTE ANONYMIZED SURVEY RESPONSES]

Please provide:
1. Top 5 positive themes (what employees are saying
   is going well)
2. Top 5 concern themes (what employees want improved)
3. Any urgent issues that appear repeatedly
4. Quotes that best illustrate each theme (anonymized)
5. 3-5 recommended actions based on the feedback

Do not identify individual respondents. Group themes
by frequency and importance.

Turnover Analysis

Turnover Analysis Prompt

Analyze this employee turnover data and provide insight.

Data: [paste or describe your turnover data — by department,
tenure, role, reason for leaving, etc.]

Analyze:
1. Which departments or roles have the highest turnover?
2. What patterns exist in tenure at time of departure?
3. What are the most common stated reasons for leaving?
4. What hypotheses do you suggest for the root causes?
5. What 3 interventions would most likely reduce turnover
   based on this data?

Caveat: flag where the data is insufficient to draw
conclusions vs. where patterns are clear.

HR Dashboard Narrative

HR metrics are most useful when accompanied by a narrative that explains what the numbers mean. AI can write that narrative from your data.

HR Report Narrative Prompt

Write an HR metrics narrative for [period: Q1/Q2/annual].

Key metrics to cover:
- Headcount: [current vs. prior period]
- Turnover rate: [%]
- Time-to-fill: [average days]
- Engagement score: [% favorable]
- Training completion: [% complete]
- Open requisitions: [number]

Write a 1-page executive summary that:
- States the most important trend up front
- Explains what is driving the key metrics
- Flags any areas of concern
- Recommends 2-3 specific actions

Audience: senior leadership. No HR jargon. Plain English.

💡

Data quality matters: AI can only analyze the data you give it. Before drawing conclusions from AI analysis, ask: is this data complete? Is it accurate? Are there obvious gaps? Garbage in, garbage out — AI makes this faster but does not fix underlying data quality issues.

Day 5 Exercise

Analyze Real HR Data

Pull your most recent engagement survey results or turnover data.
Anonymize it appropriately (remove names and identifiable details).
Run the analysis prompt. Read the themes it identifies — do they match your intuition?
Note where AI identified something you had missed and where it got something wrong.
Use the HR report narrative prompt to draft an executive summary from your metrics.

Course Complete — What You've Built

A framework for using AI across all four core HR functions: recruiting, performance, communication, and analytics.
Templates for job descriptions, interview question banks, performance reviews, PIPs, policies, and announcements.
Survey analysis and turnover analysis workflows that surface insights from large datasets quickly.
Clear understanding of where AI helps and where human judgment remains essential in HR.

Final Challenge

Pick the single most time-consuming recurring HR task you do. Build a complete AI workflow for it: the prompt, the editing checklist, the review step. Then use it for 90 days and measure the time savings. That number is the ROI of this course applied to your specific role.

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