Artificial Intelligence in Business Development and Industrial Upgrading: Environmental Impacts and Sustainability Implications

Abstract

Artificial Intelligence (AI) is a necessary technological facilitator for enterprise management, industrial digitalisation, intelligent production, coordination of the supply chains, predictive analysis and strategic decisions. The transition from the conventional information systems to the AI powered enterprise architectures has an impact on allocation of resources, management of production, prediction of demand, control of quality, product design, coordination of employees and machines. The study explores how AI can be utilized in enterprise management and how it can help with industrial upgrading from integrated technology–organisation–environment and dynamic-capability perspectives. This research is a systematic review and a literature synthesis of the research literature, and a quantitative empirical research framework on the study of AI readiness, the intensity of AI adoption, organizational capability, intelligent manufacturing integration and enterprise performance, AI readiness is divided into two parts, one is to study whether the enterprise is ready for AI applications, and the second is to study the adaptation process of AI applications. According to the literature analysis, technological infrastructure, the quality of data, organisational readiness, management support, employee capability, financial resources, regulatory conditions and strategic alignment are the factors that affect the adoption of AI. Empirical research on manufacturing companies also confirms that there is a combined impact of technological and organisational preparation and AI adoption, as well as the fact that there are differences in AI adoption among companies according to their scale and product complexity.
A sample of 420 enterprises is taken as a representative sample to demonstrate the model of analysis for quantitative analysis. The results indicate that the extent of AI use is positively associated with operational efficiency and capability to innovate, management decision quality, and AI industrial upgrading performance. Based on the results of structural equations, it can be seen that AI use has a direct positive standardised effect on enterprise performance (0.41) and industrial upgrading (0.46) and that organisational capability strengthens the relationship between AI use and enterprise performance. The business value of AI-based predictive maintenance, intelligent quality inspection and demand prediction, optimisation of supply chains is the highest. The proposed model shows that for the benefit of AI to be maximised, the model demands support from data infrastructure, organisational capacity, qualified staff, and suitable governance and process redesign as well. Based on this research, the study suggests a holistic framework which is related to the readiness and adoption of AI, enterprise management and upgrading of industries, and proposes a quantitative assessment method for the transformation by AI