AI-DRIVEN HR, GIG ECONOMY, AND QUICK COMMERCE: REDEFINING WORKFORCE STRATEGY AND BUSINESS SUSTAINABILITY IN THE DIGITAL AGE

Abstract

The convergence of artificial intelligence (AI), gig economy labor models, and the rapid expansion of quick commerce platforms has fundamentally transformed workforce management and business sustainability strategies in the digital age. Organizations are increasingly adopting AI-driven human resource (HR) systems to optimize talent acquisition, workforce allocation, performance monitoring, and decision-making processes. Simultaneously, the gig economy has redefined traditional employment structures by enabling flexible, on-demand labor models, particularly in sectors such as logistics and quick commerce, where speed and scalability are critical. This study develops a comprehensive analytical framework to examine the interaction between AI-driven HR systems, gig-based workforce structures, and business sustainability outcomes. The research adopts a hybrid methodological approach integrating workforce analytics modeling, platform-based labor analysis, and strategic HR evaluation to assess the implications of these transformations. The findings indicate that while AI-driven HR enhances efficiency, scalability, and data-driven decision-making, it also introduces challenges related to worker precarity, algorithmic bias, and regulatory compliance. Furthermore, the gig economy, while enabling operational flexibility, raises concerns regarding labor rights, job security, and long-term sustainability. The study highlights the need for integrated workforce strategies that balance technological efficiency with ethical considerations and regulatory frameworks. The research contributes to the evolving discourse on digital labor transformation by providing a structured framework for aligning AI-driven HR practices with sustainable business models in the context of platform-based economies.