The algorithmic management of job loss and creation in the enterprise generative, multimodal, and agentic artificial intelligence economy
The algorithmic management of job loss and creation in the enterprise generative, multimodal, and agentic artificial intelligence economy

Summary/Abstract: Research background: Enterprise generative, multimodal, and agentic artificial intelligence (AI) technologies facilitate transformative productivity and workforce adaptation gains in innovative organizations, redesigns autonomous team and talent management for workforce and job rotation planning, skill development, and career paths, handle context-specific collaborative business processes, workflows, and decision-making, and augment multi-agent system scaling for labor productivity and operational efficiency, redefining agile and adaptive organizational performance in dynamic business environments, driving interoperable big employee data and strategic decision management, and creating strategic fluidity and synchronized digital labor for sustainable business value. Connected and interoperable agentic AI systems can carry out multistep tasks autonomously, reduce operational costs and unemployment rates, and manage big data-based organizational workflows and management pipelines, driving business value creation and productivity gains, reallocating digital labor, and redefining employee experiences and labor markets in terms of job loss and creation by upskilling and retraining. AI labor impacts predictions are based on multimodal data and labor force productivity modeling in relation to how job and skill creation can affect economic conditions and workforce development, while driving business model transformation. Purpose of the article: We aim to clarify whether enterprise generative, multimodal, and agentic AI-based task automation and machine performance complements technology-driven employment changes and algorithmic efficiency, resulting in workforce reduction and competitive pressures due to economic incentives in terms of how i) deep reinforcement learning algorithms can build digital agentic workflows for autonomous Internet of Things (IoT) sensor-based industrial robotic machines, leading to employment relation, personnel retention and recruitment, work reorganization, and labor productivity optimization, engaged produc

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