AI Adoption in Automotive R&D: A Case Study Method for Prioritization of Inhibitors

Drawing on a case study method, 12 key inhibitors of artificial intelligence (AI) initiatives in the automotive research and development (R&D) unit are identified. The key inhibitors to AI adoption and success are analyzed with industry experts in a semi-structured interview format, helping practitioners formulate suitable strategies. The inter-relationship between each pair of inhibitors is obtained using a survey instrument, and the ISM and MICMAC analyses explored the interconnectedness and categorization. A fuzzy pair-wise matrix of key inhibitors is obtained via focus group discussions (FGD) and prioritized with the fuzzy AHP technique, providing action-oriented insights for the firm. The findings reveal that ‘lack of data acumen’, ‘data-related challenges’, and ‘ambiguity in vendor services’ have higher driving power, and ‘lack of employee commitment and ownership’ and ‘managerial skepticism’ have higher dependence. The study finds that ‘lack of sustained commitment from the leadership team’, and ‘insufficient collaboration and coordination within and across the business functions’ are the most prominent inhibitors, followed by ‘limited experimentation scope’, and ‘lack of data acumen’. These inhibitors essentially indicate necessary cultural changes towards trust, commitment and ownership, complexity and failure tolerance, AI fitment awareness, and alignments.