Examining the Preparedness of Kenya’s Marine Fisheries and Maritime Transport Sectors for the Adoption of Artificial Intelligence

Brigid K. Gesami (), Jacob Nunoo (), Stephen Edward Moore () and Joshua Sebu ()
Additional contact information
Brigid K. Gesami: University of Cape Coast, Department of Applied Economics, School of Economics
Jacob Nunoo: University of Cape Coast, Department of Applied Economics, School of Economics
Stephen Edward Moore: University of Cape Coast, Department of Mathematics
Joshua Sebu: University of Cape Coast, Department of Data Science & Economic Policy, School of Economics

Chapter Chapter 9 in The Blue Economy in African Coastal Communities, Volume I, 2026, pp 219-245 from Palgrave Macmillan

Abstract: Abstract The rate of artificial intelligence (AI) innovation is accelerating in all sectors worldwide, but its penetration into Kenya’s marine fisheries and maritime transport sectors remains limited and incipient. Attaining the potential for AI benefits, including efficiency and sustainability, depends on the sectors’ capacity in infrastructure, skills, regulatory environments, and institutional preparedness. Under-preparedness has the potential to result in risks such as the depletion of resources and disruption in the supply chain. Using an evaluation analysis, the preparedness level for AI in Kenya’s maritime transport and marine fisheries sectors is analyzed to determine gaps and feed into strategies for responsible and inclusive AI adoption. The research used an exploratory mixed-methods approach, combining quantitative analysis with qualitative sentiment analysis from 56 purposively selected stakeholders in Nairobi County, Kenya, comprised of ministries, maritime agencies, ICT companies, and fisheries organizations. The preparedness was measured in three pillars adapted from the Cisco 2024 AI preparedness index. Quantitative analysis uncovered marked differences in the two sectors’ AI readiness. Weighed against AI readiness scores, the Marine Fisheries sector scored 70%, placing it in the “Chasers” (above-average readiness) category, while the Maritime Transport sector scored 54%, putting it in the “Followers” (below-average readiness) category. Sector-affiliated institutions ranked higher at 74%, still in the “Chasers” group. Statistical tests show that the duo-sectoral institutions and the Marine Fisheries sector are substantially ready for the adoption of AI than the Maritime Transport sector. Qualitative sentiment analysis gave deeper insights into the overall positive sentiment for Data and Infrastructure, moderate for Technology and Skills, and lower for Management and Governance. The study concludes that while there are incidences of advanced readiness in some institutions, Kenya’s overall AI preparedness in its maritime transport and marine fisheries sectors is uneven, with the Maritime Transport sector being the least prepared.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:psmchp:978-3-032-25364-4_9

Ordering information: This item can be ordered from
http://www.palgrave.com/9783032253644

DOI: 10.1007/978-3-032-25364-4_9

Access Statistics for this chapter

More chapters in Palgrave Studies in Maritime Economics from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().