Actualizing artificial intelligence (AI) affordances in urban mobility organizations: a hierarchically interconnected perspective

Abstract

This study examines how urban mobility organizations, characterized by different ownership, distributed capabilities and inter-organizational dependencies, actualize artificial intelligence (AI) affordances across multiple organizational layers. Drawing on affordance-actualization theory (AAT) as the primary theoretical lens, this qualitative study examines AI initiatives in Australian urban mobility. Data were collected from 20 semi-structured interviews conducted between 2021 and 2022, supplemented by documentary evidence and analyzed using an abductive logic in five iterative steps involving first-order coding, second-order categorization and aggregate conceptualization. The findings suggest four hierarchically interconnected AI affordances: smartifying mobility infrastructure and asset management, optimizing transport operations, providing customized mobility services and promoting environmentally sustainable urban mobility. These affordances are hierarchically interconnected and conditionally enabled, where their actualization is contingent on recurring organizational practices (AI sensemaking, AI leadership and digital readiness) and constrained by data, system, governance and ecosystem conditions. Managers should treat AI initiatives as interdependent rather than isolated projects by checking data, system, governance and decision conditions before pursuing service or sustainability applications. The article contextually elaborates AAT by conceptualizing AI affordances as hierarchically interconnected but nondeterministic action potentials. It shows how AI value in urban mobility emerges through conditional enablement across infrastructure, operations, services and sustainability layers, and contributes to enterprise information management by explaining how data, systems, governance and ecosystem coordination shape interconnected AI-enabled value creation.