The Last Mile: AI's transformation of leadership across hierarchies

Artificial Intelligence (AI) is a dynamic and evolving topic with ongoing debates within research literature and business. One area of controversy is the degree to which AI will automate and augment human work across hierarchies. To explore these undertheorized areas, this dissertation uses organizational hierarchy as a lens to investigate the socio-organizational factors and professional perspectives on how low and middle-level roles will be substituted by AI, significantly impacting management and senior leadership teams. This dissertation explores the interconnectedness between key technological factors, capabilities, and socio-organizational factors needed for AI to advance through the management and leadership levels of organizations. First, this dissertation presents a development and implementation cycle grounded in, and synthesized from, elements within the literature. Second, this dissertation introduces five key themes from the qualitative analysis that directly impact the socio-organizational factors that underpin AI implementation in organizations. Third, this dissertation empirically assesses the five qualitative themes using nine custom survey items, provides quantitative support for the socio-organizational factors identified and evidence supporting the applicability of the Attitudes towards AI At Work (AAAW) scale across organizational hierarchies, expanding its utility. Using a sequential mixed-methods design combining qualitative interviews (n=17) with a quantitative survey (n=225), this dissertation suggests that hierarchy, and not demographic factors, drives variance in perceptions towards AI at work. Operational employees are a highly distinct group and are contrasted with Strategic employees demonstrating a gradient effect. This research introduces both the Substitution Paradox in which employees see AI as a labor substitute but simultaneously feel protected against its effects, and The Last Mile, a boundary condition affecting the phenomenon of Managerial Overreliance.