From Generative to Agentic Intelligence: How AI Derivatives Reshape Strategic Decision-Making in Textile Enterprises

Abstract

This study intends to investigate the role of derivatives of artificial intelligence including generative AI, agentic AI, predictive analysis, computer vision technologies, and decision-making structures enabled by AI in shifting strategic orientations in textile firms. In other words, while AI will not be considered solely as a tool for operational excellence, but rather as a strategy that shapes competitive strategies, innovation management, agility in the supply chain, sustainability initiatives, customer interaction, and decision-making in organizations. This study is intended as a conceptual and literature review study. It relies on recent academic papers from databases such as Web of Science and DergiPark related to AI, textiles/fashion industry, digitalization, strategic management, and sustainability.

Accordingly, the paper puts forth an integrated model of AI derivatives that can account for the evolution of strategic orientation of textile businesses from responsive and efficiency-driven to more proactive and adaptive strategic orientations. Specifically, the article posits that generative AI allows increasing the strategic agility of textile businesses through speeding up processes of designing, customization, digital modeling, and branding. On the other hand, agentic AI allows broadening the scope of strategic applications of artificial intelligence by making it capable of carrying out goal-directed activities, autonomous collaboration, supplier surveillance, scenario analysis, and multi-stage decision-making.