Recently Lytton Advisory is seeing Artificial Intelligence (AI) being applied across a wide range of sectors of economies. Currently we are engaged in national infrastructure investment planning in Samoa and Vanuatu. This prompted us to think about some of the issues around using AI in national infrastructure investment planning. It is a promising approach that can enhance efficiency, precision, and foresight. However, implementing this technology, especially in Pacific Island nations, is not without challenges. Three big challenges we see are:
- Limited Access to Quality Data: AI thrives on large, diverse, and high-quality datasets. For AI to be effective in infrastructure planning, it needs access to data on the current state of the infrastructure, usage patterns, environmental factors, and the like. However, in many Pacific Island nations, data collection and management practices may be underdeveloped due to resource limitations, which results in poor quality or incomplete datasets. These nations may lack the digital infrastructure, like advanced sensor networks, to gather sufficient real-time data for AI to work effectively. The issue of data privacy and protection also comes into play, given the sensitive nature of certain infrastructure-related data.
- Technological Capacity and Expertise: The implementation of AI requires technical expertise and strong digital infrastructure. In many Pacific Island nations, these capacities may be lacking due to constraints in resources, education, and infrastructure. Training locals to use and manage AI systems could be difficult, and attracting or retaining AI talent may also be a challenge due to economic factors and brain drain. There’s also the task of integrating AI with existing systems, which could be outdated or incompatible.
- Environmental Vulnerability: Pacific Island nations are among the most vulnerable to climate change. Frequent natural disasters like cyclones, flooding, and sea-level rise create an unpredictable environment for infrastructure planning. While AI could potentially help manage and adapt to these issues, the volatile environment also makes data collection and analysis more challenging. Infrastructure and equipment needed for AI, such as data centers and sensor networks, could also be damaged by environmental events.
To overcome these challenges, it’s essential to adopt a strategic approach that includes improving data management practices, investing in education and digital infrastructure, promoting technological capacity building, and implementing robust measures to mitigate environmental risks.
Trying to do this at a national level may be limiting, especially for some of the very small nations of the Pacific. Developing AI on a regional Pacific basis, rather than a series of national ones, might bring some of the following benefits:
- Shared Resources: AI development requires substantial resources, including technology, data, and skilled professionals. By pooling resources at a regional level, Pacific Island nations can collectively create more robust AI systems than they might individually. They can share the costs of necessary infrastructure, the development of AI applications, and the hiring or training of experts.
- Standardization and Interoperability: A regional approach can foster standardization of data formats, protocols, and AI technologies. This makes systems more interoperable across countries, which can facilitate cross-border initiatives and collaborations. This is particularly useful for the Pacific Island nations given their geographical proximity and shared regional challenges.
- Shared Data: AI relies heavily on data for training and functioning. By pooling data at a regional level, nations can create larger and more diverse datasets, which can help improve the accuracy and reliability of AI systems. This can also compensate for the smaller population sizes and hence smaller national datasets of these nations.
- Regional Adaptation: Given that Pacific Island nations face similar environmental challenges, such as climate change and natural disasters, a regional AI system can be designed to specifically tackle these issues. AI models could be trained to predict and respond to regional weather patterns, sea-level rises, and natural disasters, aiding in preparedness and mitigation strategies.
- Collective Bargaining: A region acting as a unified entity has a stronger position when negotiating with global tech companies or other international entities. This can lead to more favorable terms in data privacy, technology transfer, and intellectual property rights.
- Capacity Building and Learning: A regional approach encourages collaboration and exchange of knowledge and best practices among nations. This can help build capacities in AI and related fields across the region, further fostering a regional tech ecosystem.
While a regional approach offers these advantages, it also presents its own challenges such as coordinating between different national interests and regulations, data privacy concerns, and managing shared resources equitably. Therefore, a balance between regional cooperation and national autonomy needs to be found.
International cooperation could play a vital role in providing the necessary resources and expertise, particularly in kick-starting a regional approach. It’s crucial to develop AI systems with an understanding of local contexts and needs, as well as appropriate safeguards for data privacy and security.
Lytton Advisory 