Abstract
The utilisation of artificial intelligence (AI) is progressively becoming imperative for port managers. However, the extent of its implementation remains challenging to ascertain due to the absence of a universally adopted instrument within extant literature. The objective of the present article is to address the extant research gap by proposing a composite indicator with the capacity to determine the extent of AI implementation in ports. The conceptual framework of the article is predicated on an analysis of extant research, which indicates that the measurement of the degree of AI adoption should be a complex process, i.e. it should encompass multiple elements. The AI Adoption Index for Seaports (AAIS), as outlined in the article, has the potential to serve as a foundation for comparative analyses and the identification of implementation gaps in seaports.
Paper Type
Poster
DOI
10.62036/ISD.2026.146
Measuring the adoption of artificial intelligence in seaports: a proposal for the AAIS indicator
The utilisation of artificial intelligence (AI) is progressively becoming imperative for port managers. However, the extent of its implementation remains challenging to ascertain due to the absence of a universally adopted instrument within extant literature. The objective of the present article is to address the extant research gap by proposing a composite indicator with the capacity to determine the extent of AI implementation in ports. The conceptual framework of the article is predicated on an analysis of extant research, which indicates that the measurement of the degree of AI adoption should be a complex process, i.e. it should encompass multiple elements. The AI Adoption Index for Seaports (AAIS), as outlined in the article, has the potential to serve as a foundation for comparative analyses and the identification of implementation gaps in seaports.
Recommended Citation
Goniszewski, M.(2026). Measuring the adoption of artificial intelligence in seaports: a proposal for the AAIS indicator. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.146