DEVELOPING A DIGITALIZATION STRATEGY BASED ON HUMAN–ARTIFICIAL INTELLIGENCE COLLABORATION AND INTEGRATING IT INTO INDUSTRIAL MANAGEMENT

Abstract

This article develops a digitalization strategy for industrial management grounded in human–artificial intelligence collaboration, treating AI not as a standalone automation layer but as a socio-technical capability embedded across planning, operations, quality, maintenance, logistics, finance, and risk management. The proposed approach frames digital transformation as an institutional redesign problem: value is created when human managerial judgment, domain expertise, and ethical accountability are systematically integrated with machine learning, optimization, and decision-support systems. The study conceptualizes collaboration through complementary roles: humans define objectives, constraints, and governance; AI provides pattern discovery, forecasting, anomaly detection, and scenario simulation; and joint workflows translate insights into controllable managerial actions. A strategy architecture is presented that links business goals to data stewardship, interoperable platforms, workforce upskilling, and process reengineering, with measurable key performance indicators for productivity, quality, energy efficiency, and resilience. The article also outlines a maturity pathway for industrial organizations, moving from digitization of records to data-driven operations and, ultimately, adaptive management supported by continuous learning loops. Special attention is given to practical implementation conditions in emerging industrial ecosystems, including legacy equipment, uneven data quality cybersecurity exposure, and skills gaps. The expected contribution is a coherent, implementable framework that enables industrial firms and public stakeholders to coordinate investments, manage risks, and scale AI-enabled productivity improvements while preserving transparency, accountability, and human oversight in managerial decision-making.