Artificial Intelligence and the Reconfiguration of Organizational Structures and Business Processes: A Literature-Based Analysis of Tesla

This dissertation explores the role artificial intelligence plays in reconfiguring organizational structures and business processes at Tesla. The research employs a literature-based approach to synthesizing information from secondary sources, which were predominantly published in the last four years (2022–2026) and encompass peer-reviewed academic papers, reliable industry reports, and Tesla corporate disclosures. Thematic analysis is used in the study, which consists of six areas of analysis: AI technologies and applications, Business processes, Organizational structure and coordination, Decision-making systems, Human-AI work arrangement, and Organizational outcomes. The results reveal a shift in how Tesla uses AI, from a standalone technology to an embedded capability that connects software, data, engineering, manufacturing, and customer-facing processes. While the power of decision-making is still largely in human hands and centralized, AI can facilitate more continuous and feedback-driven processes, enhance cross-functional integration, and help more quickly make decisions based on data. The analysis also suggests that AI will transform work by recombining tasks, not by fully replacing human workers, thus adding value to the expertise required, supervision, and collaboration with machines. Vertical integration, internal software capabilities, data access, computing investment, and strategic prioritization are key enablers, whereas cost, governance, skills, safety, and technological complexity are barriers. In sum, AI helps to shift the way that Tesla operates in a coordinated manner. The results have implications for managers in complex operations who are applying AI.