Artificial intelligence and machine learning adoption in automotive organizations using technology-organization-environment framework

Purpose: This research purposes to give a thorough knowledge of the factors impacting the adoption of AI/ML in demand forecast management within automotive organizations. In automotive organizations, artificial intelligence technology and machine learning algorithms (AI/ML) can increase demand forecast accuracy, time series forecasting, predictive analytics, inventory management, production planning, and supply chain efficiency. Methodology: Built on the Technology-Organization-Environment underpinning theory, this study validates the AI/ML adoption intentions. In this empirical study, primary data from 257 employees of small, medium, and big automotive organization was used to test a conceptual model using structural equation modelling. Findings: Technology factors such as AI/ML complexity and innovation, organizational factors like internal policies, organization communication, and data integrity, and environmental factors like government regulations and economic conditions were favourable for AI/ML adoption in automotive industries. The results highlight that organizational capability, AI/ML compatibility and localization were insignificant in the context of demand forecasting. Implications: This study provides theoretical and practical implications as Automotive organizations may respond swiftly to dynamic settings by using AI/ML to quickly adjust to shifting market conditions, customer preferences, and unanticipated demand variations.