Статьи

DOI: https://doi.org/10.31713/ve2202624

Abstract

The research explores mechanisms for adapting personnel management systems to conditions of martial and economic instability through the implementation of the Data-driven management concept and digital transformation tools. The study is necessitated by the requirement to bridge the "productivity gap" caused by the desynchronization of traditional labor norming methods with the real requirements of Industry 4.0. In this context, digital transformation is viewed not merely as software adoption but as a fundamental shift in management philosophy, where human competencies and creative potential become the center of an intellectual ecosystem. The analysis confirms that the integration of Artificial Intelligence (AI) allows for the automation of up to 30% of managerial functions regarding the division and cooperation of labor. This shift frees resources for the "human factor"- empathy, intuition, and the resolution of ethical dilemmas that cannot currently be algorithmized. Modern labor organization is defined by three key trends: intellectualization (increased creativity), flexibilization (remote work and flexible schedules), and digital cooperation via shared cloud platforms (SaaS) to ensure business continuity. Unlike traditional experience-based norming, Data-driven management utilizes time-trackers and Big Data for the real-time adjustment of labor standards. For small enterprises, the research highlights the viability of the gig economy model, where a small managerial "core" coordinates a network of external contractors through digital registries and tools such as Jira, MS Project, and Slack. To ensure objective evaluation, a complex KPI matrix is proposed, weighting indicators such as quality (0.4), adherence to milestones (0.3), resource profitability (0.2), and digital discipline (0.1). Ultimately, the future of HRM lies in creating synergy between the manager (possessing high emotional intelligence) and AI as an "intellectual exoskeleton" capable of real-time data processing.

Author Biography