The Role of Artificial Intelligence for Technology Managers in the Logistics Industry

Abstract

The increasing strategic importance of technology has strengthened the role of technology managers (TMs) such as Chief Technology Officers (CTOs), while advances in artificial intelligence (AI) offer new opportunities to support role-specific executive decision-making. However, existing research largely examines AI support from a general strategic management perspective and provides limited insights into TM-specific applications. This study addresses this gap by investigating how TM presence is reflected in practice, which organizational and environmental factors shape the role, which stakeholders are most relevant, and where AI can provide role-specific support. A qualitative research design was employed, involving five semi-structured expert interviews with TMs and related decision-makers from Swiss logistics companies. The interview data were analysed using qualitative content analysis, resulting in a framework linking TM responsibilities, organizational and environmental influences, stakeholder interactions, expected organizational outcomes, and AI-enabled support mechanisms. The findings show that TMs fulfil a hybrid strategic-operational role, acting as an interface between technology, business processes, and stakeholders. Major challenges include information overload, fragmented knowledge, system complexity, technology investment uncertainty, stakeholder coordination, and resistance to change. Furthermore, the findings highlight the critical importance of relationships with supervisors, peers, and employees in shaping technology-related decisions and outcomes. The study identifies several role-specific AI application areas, including knowledge management, technology scouting, decision support, investment evaluation, system transparency, process documentation, and stakeholder communication. AI is perceived not only as a tool for improving efficiency but also as a mechanism for supporting transparency, evidence-based decision-making, organizational alignment, and innovation management. The proposed framework contributes to the technology management and CTO literature by establishing a bridge between technology leadership and AI-supported management. At the same time, it provides practical guidance for organizations seeking to leverage AI to strengthen technology leadership, improve decision quality, and enhance innovation capabilities under conditions of increasing technological complexity and uncertainty.