Sustainable Innovation Risk Management and AI Adoption in Textile Manufacturing Enterprises

¹ Tashkent University of Architecture and Civil Engineering, Uzbekistan
² Karakalpak State University named after Berdakh, Uzbekistan
³ Karakalpakstan Institute of Agriculture and Agrotechnology, Uzbekistan
⁴ Tashkent Institute of Textile and Light Industry, Uzbekistan
⁵ Mamun University, Uzbekistan

^* Corresponding author: r.i.nurimbetov@bk.ru

Abstract

New digital transformation in manufacturing provides an opportunity for textile enterprises and technology developers (i.e., industrial managers and AI engineers) to engage with each other in collaborative innovation ecosystems to discuss data-driven approaches to risk management and sustainable competitiveness. This research paper examines the interaction of artificial intelligence systems and hybrid analytical media in enhancing the innovation-driven textile sector in Uzbekistan through a structural–empirical framework, the SEM-regression hybrid model which is the core methodological foundation of this empirical study. It then suggests a comprehensive framework of AI-based risk profiling and proposes the concept of innovation readiness calibration. A comparative analysis of Uzbekistan’s textile enterprises (organizational adaptability, cost management efficiency, and perceived technological value) and external drivers (“investment readiness” and regulatory support) were chosen for quantitative evaluation. Drawing on data gathered during the 2023 fiscal reporting period in Uzbekistan, a hybrid SEM-regression analysis, this article shows that organizational adaptability and perceived technological value actively shape adoption intentions and change profiling performance in order to keep different financial and operational risk parameters apart. The article highlights how AI-based readiness is the result of systemic interactions among organizational, financial, and technological factors within emerging textile economies and not the consequence of a single policy directive or isolated investment effort. Results indicate that investment readiness and technological perception most often field the highest explanatory power in determining adoption potential. It then suggests a theoretical refinement of innovation risk profiling models and proposes the concept of dual-stage hybrid estimation for sustainable innovation planning.