Paper Number
ICIS2025-1851
Paper Type
Complete
Abstract
Artificial intelligence (AI) is transforming Human Resource Management (HRM) practices across organizations. HRM represents a promising AI application domain due to its data-intensive nature and combination of routine and complex processes. However, existing research remains fragmented, lacking systematic understanding of current AI applications across HRM functions. We develop a comprehensive taxonomy by analyzing 115 AI-enabled HRM startups from Crunchbase, applying Nickerson et al.'s (2013) established methodology. The resulting multi-layer taxonomy encompasses 3 layers, 13 dimensions, and 40 characteristics, structured around the HR Life Cycle framework, technological capabilities, and business model configurations. Our analysis identifies two dominant archetypes: Applicant Recommendation Chatbots and Employee Insights Solutions. Findings reveal that AI applications concentrate predominantly in recruitment and deployment functions, while strategic HR areas remain underexplored. This research contributes a structured framework for understanding AI in HRM and provides practitioners with actionable insights into market configurations.
Recommended Citation
Stahl, Hendrik, "From Black Box to Blueprint: A Multi-Layer Taxonomy for AI-Driven Human Resource Management" (2025). ICIS 2025 Proceedings. 12.
https://aisel.aisnet.org/icis2025/is_transformwork/is_transformwork/12
From Black Box to Blueprint: A Multi-Layer Taxonomy for AI-Driven Human Resource Management
Artificial intelligence (AI) is transforming Human Resource Management (HRM) practices across organizations. HRM represents a promising AI application domain due to its data-intensive nature and combination of routine and complex processes. However, existing research remains fragmented, lacking systematic understanding of current AI applications across HRM functions. We develop a comprehensive taxonomy by analyzing 115 AI-enabled HRM startups from Crunchbase, applying Nickerson et al.'s (2013) established methodology. The resulting multi-layer taxonomy encompasses 3 layers, 13 dimensions, and 40 characteristics, structured around the HR Life Cycle framework, technological capabilities, and business model configurations. Our analysis identifies two dominant archetypes: Applicant Recommendation Chatbots and Employee Insights Solutions. Findings reveal that AI applications concentrate predominantly in recruitment and deployment functions, while strategic HR areas remain underexplored. This research contributes a structured framework for understanding AI in HRM and provides practitioners with actionable insights into market configurations.
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03-Transformation