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

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 productive staff flexibility and autonomy, and job performance and satisfaction, ii) how task automation and augmentation disrupt labor markets and reshape workforce for either more layoffs or more new hires, predicting both increased or lower wages, high or decreased unemployment, and job creation or elimination, and iii) how computer vision-based task automation and augmentation technologies redesign business-critical workflows and workforce upskilling processes across collaborative enterprise IoT and sluggish hiring environments for task automation and augmentation, streamlining personalized human resource support, resource efficiency, and enterprise productivity, driving economic growth.

Methods: A quantitative literature review of ProQuest, Scopus, and the Web of Science databases was carried out and the most relevant research published between 2024 and 2025 was identified and analyzed. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) and the web-based Shiny app were harnessed for search results and screening. Dimensions (for bibliometric mapping) and VOSviewer (for layout algorithms) were the deployed data visualization tools. Evidence synthesis screening software and reference and review management tools leveraged included AMSTAR, CADIMA, DistillerSR, JBI SUMARI, MMAT, Nested Knowledge, PICO Portal, and SRDR+.

Findings & value added: The main value added derived from the systematic literature review is that enterprise generative, multimodal, and agentic AI system applicability correlates with occupational task operation completion, wage, employment prospects, and education, driving business choices and transformation, labor markets, and economic growth. The benefits for theory and current state of the art are that enterprise generative, multimodal, and agentic AI-based flexible work arrangements and increased employee tracking for organizational and workforce performance can improve job quality while reducing pay inequity, staff absenteeism, job turnover, and widespread unemployment, affecting labor markets and resulting in long-term business values and outcomes. Occupational AI and computer vision technologies impact predictions with regard to work activity automation and augmentation in terms of job loss, labor productivity, and wage raising or lowering. Policy implications reveal that employee productivity and performance tools entail job displacement and creation, requiring emerging workforce reskilling or upskilling for talent attraction, retention, progression, and promotion across structural labor market transformation.

Keywords

job creation, business cycle approach, artificial intelligence, generative, China dual-carbon , agentic

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