Uptake of new-age technology Artificial Intelligence (AI) in Kenya is concentrated in the agricultural sector with the involvement of machine learning to equip local farmers with data-driven advice that seeks to aid them optimise productivity, a new report now shows.
The ‘AI for Africa’ report published by the Global System for Mobile Communications Association (GSMA), an association of mobile network operators, shows that agriculture and food security takes up 49 percent of all AI deployments followed by climate action and energy use cases at 26 percent and 24 percent, respectively.
According to the report unveiled on Tuesday, an overwhelming majority of use case applications fall under predictive AI in what GSMA attributes to a range of factors including the availability of historical datasets, ease of applicability as well as lower computational requirements compared to generative AI models.
“The agritech sector is seeing most of the AI innovation in Kenya where agriculture continues to play a significant role in the economy.
“AI is already being used for agricultural advisory and for financial services with companies like Apollo Agriculture developing alternative credit assessment methods,” reads the report.
Microsoft’s AI for Good Lab has, for instance, developed a spatiotemporal machine learning model to help in the detection of malnutrition hotspots, enabling timely interventions and targeted assistance and ultimately mitigating the impact of malnutrition on vulnerable populations.
Increasing investments in data centres from large tech firms and Mobile Network Operators (MNOs) in the country has been cited in the report as a key factor driving momentum by bringing critical storage and computing capacity to the local level.
In climate action, AI use has been pronounced in biodiversity monitoring and wildlife protection, driven chiefly by large tech companies like AI for Good Lab and non-profit organisations such as Rainforest Connection.
GSMA has, however, flagged critical infrastructure gaps and regular power outages as setbacks that entrench the digital divide and disproportionately affect low-income groups, the less educated as well as rural populations, noting that AI risks aggravating the existing socioeconomic inequalities.
Another major barrier to AI deployment and adoption as cited in the report is the high cost of hardware such as Graphic Processing Units (GPUs) and cloud computing especially for local entrepreneurs and researchers with limited financial resources.
GPUs and cloud computing systems are enablers that allow for the storage capacity and computing power required for processing complex algorithms, analysing vast datasets, as well as executing advanced AI applications and models.
According to the report, the price of a GPU in Kenya represents 75 percent of GDP per capita making it 31 times more expensive than in high-income countries.
“A significant skills gap also undermines the development of the AI ecosystem and use cases.
“ While universities offer AI-related courses, they often fail to keep pace with industry needs, and students have limited opportunities for practical learning and hands-on experiences,” the association writes.
“There is also a disproportionate focus on core AI skills, such as machine learning and data science, with less emphasis on building the multidisciplinary skill sets needed to leverage AI to address pressing socioeconomic challenges.”
Locally, deep tech startup Fastagger is developing a software infrastructure that allows machine learning and AI models to run directly on edge devices, including on lower-end smartphones.
