Managing the risks of integrating artificial intelligence into HR software

Zornitsa Ivanova (), Magdalena Garvanova () and Ivan Garvanov ()
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Zornitsa Ivanova: University of Library Studies and Information Technologies, Sofia, Bulgaria
Magdalena Garvanova: University of Library Studies and Information Technologies, Sofia, Bulgaria
Ivan Garvanov: University of Library Studies and Information Technologies, Sofia, Bulgaria

Access Journal, 2026, vol. 7, issue 3, 643-657

Abstract: Background: This paper explores the effects of implementing AI in HR software, analyzing the critical balance between augmentation of human capabilities (augmentation) and substitution of administrative roles (substitution). The aim is to define the main organizational risks and propose a conceptual framework for their management. Objectives: This paper explores the effects of AI implementation in HR software, analyzing the critical balance between augmentation and substitution, and aims to define the main organizational risks and propose a conceptual framework for their management. Methodology: The study is based on a conceptual analysis and review of scientific literature, institutional reports and market research for the period 2023–2025. The analysis integrates the task-based framework for technological change and the current regulatory requirements of the EU AI Act. Results: Three key challenges are identified: the high risk of automation of basic HR roles, algorithmic bias in processes, and lack of trust in automated systems. As an original contribution, the paper develops and implements a four-stage model for managing AI integration. The model filters tasks by their impact type and suggests specific management interventions (proactive retraining, algorithm audit, and explainable AI). Conclusions: Successful implementation of AI in HR is not purely a technological issue, but primarily a management one. An augmentation-first strategy ensures sustainability, regulatory compliance, and maximum return on investment, while maintaining a sense of procedural fairness among employees.

Keywords: artificial intelligence; HR software; HR automation; risk management; algorithmic bias; reskilling; augmentation (search for similar items in EconPapers)
JEL-codes: J24 J81 M15 O33 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:aip:access:v:7:y:2026:i:3:p:643-657

DOI: 10.46656/access.2026.7.3(8)

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