TITLE:
From Prediction to Action: An Agentic AI Framework for Workforce Substitution and Risk-Aware Automation Decisions
AUTHORS:
Abdelrahman Elsharif Karrar, Eiman Salamah Aljohani
KEYWORDS:
Agentic AI, Workforce Substitution, Hybrid Ensemble Learning, Decision Intelligence, Risk-Aware Decision Making, Labor Market Analytics, Economic Forecasting, Explainable AI (XAI), Automation Policy Recommendation, Workforce Transformation
JOURNAL NAME:
Intelligent Information Management,
Vol.18 No.4,
July
6,
2026
ABSTRACT: The rapid adoption of artificial intelligence across economic and industrial sectors is reshaping labor markets, accelerating workforce transformation, and increasing the demand for intelligent systems capable of supporting automation-related decisions. However, existing approaches typically address workforce forecasting and automation policy selection as separate tasks, limiting their ability to provide integrated and actionable decision support. To address this challenge, this paper proposes an Agentic AI-inspired hybrid learning framework for workforce substitution analysis and automation decision-making. The proposed framework combines regression models to predict workforce substitution timelines with classification models that recommend automation strategies, including Monitor, Assist, and Automate. A stacking-based ensemble mechanism is employed to enhance predictive accuracy, robustness, and generalization, while a risk-aware decision intelligence layer transforms predictive outputs into actionable recommendations, enabling the framework to move beyond passive forecasting toward adaptive decision support. Experimental evaluation conducted on the 2026 Intelligence Economy: Labor vs. AI Compute dataset demonstrates the effectiveness of the proposed approach. The hybrid model achieved an R2 score of 0.857 for workforce substitution prediction and a classification accuracy of 95.4% for automation strategy selection, outperforming individual baseline models across multiple evaluation metrics. Additional validation through cross-validation, robustness analysis, scalability testing, and explainability techniques confirmed the reliability, stability, and practical applicability of the framework. The results demonstrate that integrating hybrid machine learning with agentic decision intelligence provides an effective, interpretable, and scalable solution for analyzing workforce transformation and supporting automation-related decisions in AI-driven economic environments.