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Simulation study demonstrates context-dependent trade-offs in IT workforce configurations during AI integration, highlighting the need for balanced governance and sustainability metrics.
In contemporary Information Technology (IT) enterprises, workforce role configuration decisions sit at the intersection of leadership strategy, corporate Artificial Intelligence (AI) transformation and sustainable operational governance. This study investigates how heterogeneous workforce role configurations affect Service Level Agreement (SLA) compliance in SLA-aware Business Process Model and Notation (BPMN) IT business processes, framing this as a people management and leadership decision problem with explicit Environmental, Social and Governance (ESG)-oriented trade-offs. A validated MATLAB Simulink simulation tool is employed to conduct structured scenario testing across combinations of human role configurations and AI maturity levels, measuring their impact on key Service Level Objectives (SLOs) and Key Performance Indicators (KPIs). To support leadership decision making, a Workforce Sustainability Index (WSI) is introduced that integrates SLA compliance with workforce cost and AI dependency risk, where AI dependency risk captures the social dimension of ESG by emphasizing workforce skill development, upskilling and reskilling pathways, human oversight and responsible AI adoption. The simulation results reveal that no universally optimal configuration exists; the best-performing workforce design depends on organizational context and leadership priorities, as captured through scenario-based weight configurations representing performance-driven, cost-driven, human-centric and balanced governance orientations. These findings provide IT leaders and organizational decision-makers with actionable, evidence-based guidance for sustainable workforce design in the context of corporate AI transformation, contributing to the broader discourse on people management, merit-based organizational performance, responsible AI governance and sustainable digital operations management.
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Athanasios G. Lazaropoulos (2026) studied this question.
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