AI-Native Workforce Systems: Bridging Governance Gaps in Enterprise HR Transformation
Keywords:
AI-native HR Platforms, Workforce Intelligence, Data Governance, Algorithmic Bias, Skills OntologyAbstract
Purpose: The paper will outline and discuss the significant governance, data standardization, and ethical loopholes that do not allow AI-native HR products to deliver credible workforce intelligence and suggest a unified framework of responsible AI use in the field of human resource management.
Design/Method/Approach: This conceptual review is a synthesis of 31 peer-reviewed articles using Scopus, Web of Science and EBSCOhost (2022-2025) and industry case studies on algorithmic hiring failure and regulatory action in the European Union, United States, and emerging economies.
Findings: There are three underlying governance vulnerabilities that weaken AI-native HR efficacy: (1) fragmented data governance in the absence of standard skills ontology across enterprise systems, resulting in inconsistent workforce intelligence; (2) risks of algorithm bias and hallucinations without standardized validation frameworks, disproportionately affecting marginalized populations such as women, racial minorities and non-binary individuals; and (3) cross-system orchestration failures across enterprise platforms.
Theoretical implications: Expands the existing theory of dynamic capabilities by showing that governance can be a meta-capability that makes it possible to sense, seize, and transform workforce data in particular. Adds to sociotechnical systems theory by establishing AI-specific coordination mechanisms necessary when humans and algorithms work together in making decisions in high-stakes employment.
Practical implications: Offers five-component framework of governance to enterprise leaders such as data standardization protocols, requirement bias audits based on statistical parity difference and disparate impact, human-in-the-loop validation points, cross-system integration architecture with standardized APIs and training on AI constraints and oversight, and ongoing training of HR professionals.
Social implications: Resolves fairness and transparency in hiring, performance assessment, and promotion decisions made by AI, reducing risks of algorithmic discrimination that influence the workforce diversity, equal opportunity, and work justice of safeguarded populations.
Originality/Value: A first attempt to systematically frame AI-native HR platform issues as a governance gap, but not as a technology adoption issue, providing an architectural framework plus an ethical framework that is tested on empirical instances of algorithmic hiring failures.
Limitations of the research: It is based on the Western-published literature of 2022–2025; empirical verification of the offered framework is necessary, based on longitudinal case studies under different regulatory and cultural conditions.
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