From static baselines to dynamic interplay : Navigating AI adoption in the construction waste management industry

Sammanfattning

Artificial intelligence (AI) has recently gained a lot of attention as promises of significant opportunities for organizational development have emerged. Yet, the technology also entails several risks and its adoption remains a complex, context-dependent process. This is also evident in the low-digitalized construction waste management industry, where practical implementations often fail to match the theoretical potential. This study consequently explores how the comprehension of relevant organizational aspects, and their interrelation, can guide strategic choices when navigating AI adoption in this industry. Through a qualitative single case study at a leading firm within the industry, empirical data was collected via semi-structured interviews and observations. To avoid treating organizational conditions as isolated aspects, the study combines a static mapping of the organization’s baseline with a dynamic process model to serve as an analytical lens. The short-term potential primarily lies in augmenting back office processes, although larger processes such as transport planning and customer interfaces are also relevant to consider. Physical waste applications, on the other hand, are currently distant priorities. However, risks such as hallucinations and data security, along with the customer relationships emerge as significant concerns. Successfully navigating this landscape is also contingent on enablers and impeders such as organizational culture, competence and strategic governance. While a static analysis is important, the study concludes that comprehending the interplay between relevant aspects is beneficial when approaching AI. More specifically, this allows organizations to initialize a structured, iterative learning process, effectively supporting the navigation of future strategic choices.