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People Analytics Manager
Based on 38 assessments
37%
Moderate risk
Average realistic automation risk across all People Analytics Manager profiles in the dataset.
Raw potential = I/O automation ceiling. Realistic risk = adjusted for informal knowledge and social context.
Score spread
Distribution across 38 profiles.
Middle half of People Analytics Managers score between 34% and 39%.
0%
50%
100%
Task breakdown by work type
Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.
Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.
Computer input, real-world output — needs someone to act on it, not just software.
No computer required. Furthest from automation — the strongest human advantage.
Typical tasks
3 synthetic profiles for a People Analytics Manager, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
Present insights to leadership, translate statistical findings into business recommendations, and propose process improvements
deep expertise
social core
20%
AD
12%
Extract HR data from multiple systems (payroll, ATS, HRIS), clean it, and load into analytics platforms or data warehouse
19%
DD
89%
Conduct ad-hoc analyses on business questions (e.g., why engineering attrition is high, which departments underutilize benefits)
deep expertise
social element
18%
AD
10%
Design and validate methodologies for measuring HR KPIs (retention rate, time-to-hire, engagement, etc.) and ensure data quality
deep expertise
social element
16%
AD
9%
Meet with HR leaders, department managers, and executives to understand their metrics needs and interpret findings
deep expertise
social core
11%
AA
3%
Build dashboards and reports (headcount trends, turnover, diversity metrics, hiring pipeline) for stakeholder review
9%
DD
50%
Maintain data governance, document data sources, manage access controls, and ensure compliance with privacy regulations
5%
DD
64%
Meet with HR leaders, department managers, and executives to understand their metrics needs and interpret findings
some context needed
social core
30%
AA
6%
Design and validate methodologies for measuring HR KPIs (retention rate, time-to-hire, engagement, etc.) and ensure data quality
deep expertise
social element
23%
AD
18%
Extract HR data from multiple systems (payroll, ATS, HRIS), clean it, and load into analytics platforms or data warehouse
19%
DD
88%
Build dashboards and reports (headcount trends, turnover, diversity metrics, hiring pipeline) for stakeholder review
14%
DD
54%
Maintain data governance, document data sources, manage access controls, and ensure compliance with privacy regulations
9%
DD
65%
Conduct ad-hoc analyses on business questions (e.g., why engineering attrition is high, which departments underutilize benefits)
deep expertise
social element
2%
AD
20%
Present insights to leadership, translate statistical findings into business recommendations, and propose process improvements
deep expertise
social core
1%
AD
5%
Extract HR data from multiple systems (payroll, ATS, HRIS), clean it, and load into analytics platforms or data warehouse
42%
DD
93%
Build dashboards and reports (headcount trends, turnover, diversity metrics, hiring pipeline) for stakeholder review
27%
DD
45%
Conduct ad-hoc analyses on business questions (e.g., why engineering attrition is high, which departments underutilize benefits)
deep expertise
social element
16%
AD
14%
Meet with HR leaders, department managers, and executives to understand their metrics needs and interpret findings
deep expertise
social core
10%
AA
0%
Maintain data governance, document data sources, manage access controls, and ensure compliance with privacy regulations
1%
DD
64%
Design and validate methodologies for measuring HR KPIs (retention rate, time-to-hire, engagement, etc.) and ensure data quality
deep expertise
social element
1%
AD
19%
Present insights to leadership, translate statistical findings into business recommendations, and propose process improvements
deep expertise
social core
1%
AD
13%
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