A Triple-Intelligence Framework for Sustainable AI-Driven Workforce Analytics: Integrating Artificial Intelligence, Human Judgment, and Organizational Governance

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, SI1 (2026), pp. 43–50
Proceedings of Selected Papers from the
National Conference on Emerging Trends in Commerce and Management
NCETCM-2K26
2026-03-30 to 2026-03-31
Vijayawada, Andhra Pradesh, India
Organized by Andhra Loyola College, Vijayawada, India
eISSN: 3107-9075

This work is licensed under a Creative Commons Attribution 4.0 International License .

A Triple-Intelligence Framework for Sustainable AI-Driven Workforce Analytics: Integrating Artificial Intelligence, Human Judgment, and Organizational Governance

Praveen Kumar Guraja, Kamalamalini Nagasundaram, Manish Nalluri

Department of Applied Computing & Geomatics, Oregon Institute of Technology, Klamath Falls, Oregon, USA

Abstract

The use of Artificial Intelligence (AI) within workforce analytics represents a paradigm shift in how organizations make decisions regarding their employees. While AI-enabled workforce analytics can enable proactive and predictive decision-making, the literature identifies multiple substantive risks associated with the use of AI in workforce analytics, namely: algorithmic opacity, automation bias, proxy-based discrimination, and employee surveillance. This literature gap was addressed through developing and validating a Triple-Intelligence Framework (TIF), which integrates three interdependent components - AI intelligence for scalable pattern identification, human intelligence for contextualized interpretation of results and ethical decision-making, and organizational intelligence for governance and accountability. A systematic literature review of explainable AI, algorithmic fairness, and governance of people analytics research (between 2017-2025) produced a TIF for four high-risk decision domains related to workforce decision-making. These included: hiring/mobility, performance management, workforce planning, and remote/hybrid work analytics. The study identified that sustainable workforce analytics requires coordinated action across each of the three intelligence layers and provided a practical path forward consistent with the principles of Industry 5.0.

Keywords: Workforce Analytics, Artificial Intelligence, Algorithmic Fairness, Human Judgment, Organizational Governance.

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DOI: 10.66710/ijersem.v2si1.5

Open Access • Peer Reviewed Article

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This article is part of Volume 2, Special Issue 1 (May 2026)

2026-05-16