FROM SKILLS VERIFICATION TO WORKFORCE INTELLIGENCE A RESPONSIBLE AI FRAMEWORK FOR COMPETENCY-BASED WORKFORCE DECISION-MAKING

Abstract

Artificial intelligence (AI) is increasingly used to support recruitment, skills analysis, learning, performance management and workforce planning. Yet many workforce decisions still depend heavily on static qualifications, curriculum vitae data, self-reported experience and periodic assessments. These sources remain valuable, but they may provide an incomplete or outdated representation of an individual's current, demonstrable capability. This conceptual paper proposes the Responsible AI Workforce Competency Intelligence Framework (RAIWCIF), an applied architecture for transforming competency assessment from episodic verification into a governed source of workforce intelligence. The framework integrates five dimensions: competency evidence, workforce intelligence, AI-assisted analysis, digital trust and human governance. It positions AI primarily as decision support rather than as an autonomous authority over consequential employment outcomes. Drawing on responsible-AI governance principles, contemporary workforce research and regulatory developments, the paper develops a staged implementation model covering evidence provenance, role and skills mapping, skills-gap analysis, AI-readiness indicators, explainability, fairness, privacy, human oversight and continuous monitoring. The framework is intentionally designed to distinguish absence of evidence from evidence of absence and to preserve meaningful human review at consequential decision points. The paper contributes a practical bridge between competency-based workforce management and responsible AI governance. It argues that the value of workforce AI depends not only on predictive or generative capability, but also on evidence quality, traceability, proportionality, contestability and accountable organisational decisionmaking. The framework is conceptual and requires empirical validation across industries, occupations and labour-market contexts.