Social security institutions have taken significant steps toward integrating AI into their core operations, something that the ISSA has already focussed on in webinars, analysis articles and at the ICT 2024 conference. The report highlights significant strides made by social security institutions in integrating AI into their operations. AI has demonstrated its capacity to enhance service delivery, automate tasks, and significantly improve administrative efficiency as part of a broader data-driven strategy. By leveraging diverse data, AI enables organizations to create new business processes and provide value-added services, ensuring better support and reach to individuals in need.

The report Artificial Intelligence in social security organizations sheds further light on the transformative potential of AI in social security. AI has demonstrated its ability to increase capacity, enhance service delivery and support institutions in automating tasks to significantly improve administrative efficiency in social security institutions as part of an institutional data-driven strategy. By leveraging diverse and different types of data, AI can also allow organizations to create new business processes and provide value-added services to better reach and service people with adequate support. Since AI thrives on data, its integration into service and operations relies on high-quality information. It is therefore essential to ensure data availability and quality for effectively training AI systems.

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The report highlights that AI implementation also comes with its challenges and should be adopted as part of the portfolio of technologies. It shares a variety of examples, including prevalent Natural Language Processing- based chatbots. These show how AI can become a value-added alternative, by overcoming the limitations of traditional software solutions. Institutions must supervise AI solutions to ensure they align with their objectives, and establish continuous oversight to ensure accurate results and mitigate risks related to data, as well as challenges around transparency and explainability.

The authors argue that staying updated on AI solutions and experimenting with their use will prepare institutions for future advancements. As the field of AI evolves, social security institutions must prioritize capacity building and adopt guidelines to navigate challenges. A seamless transition toward a data-centric digital landscape will position institutions to fully leverage AI solutions for their specific needs.
AI implementation is not without challenges. The report stresses that AI should be adopted as part of a broader portfolio of technologies, with institutions supervising AI solutions to align them with organizational objectives. Continuous oversight is necessary to ensure accurate results, mitigate risks, and address issues related to data transparency and explainability. Examples such as Natural Language Processing-based chatbots illustrate how AI can overcome the limitations of traditional software solutions, providing value-added alternatives.
As AI evolves, prioritizing capacity building and adopting guidelines to navigate challenges will be essential. A seamless transition toward a data-centric digital landscape will position institutions to fully leverage AI solutions tailored to their specific needs.

Dr. Saidsharif Haydarov, a tuberculosis specialist in Rudaki, Tajikistan, Artificial intelligence screens for TB more accurately, leading to improved treatment for patients. The portable X-ray makes things easier for doctors and patients and plays an important role in TB response,

The automated technology helps  interpret chest X-ray images and see if people have tuberculosis (TB), a potentially fatal infectious disease that mostly affects the lungs. If anomalies are found in the images, patients undergo additional testing to confirm their diagnosis and receive appropriate treatment. 
Chest X-rays are a common method of screening and triaging people for TB and assessing their lung damage. With artificial intelligence (AI), results are more precise and ready in a couple of minutes, allowing health workers to quickly determine the next steps for TB care, even in remote areas without a radiology expert. 

Combined with the benefits of AI, these new battery-powered X-ray machines are also small and light enough to transport by hand. Traditional X-ray machines are stationary and operate on local electric grids. Like many low- and middle-income countries, where most TB cases are found, electricity is unreliable in South Sudan and Tajikistan. 

Interest in artificial intelligence (AI) as an instrument for improving efficiency in the public sector is at an all-time high. This interest is motivated by the ambition to develop neutral, scientific, and objective techniques of government decisionmaking The role of AI in achieving the Sustainable Development Goals recently drew the attention of the international development community .

Advocates argue that AI could radically improve the efficiency and quality of public service delivery in education, health care, social protection, and other sectors. In social protection, AI could be used to assess eligibility and needs, make enrollment decisions, provide benefits, and monitor and manage benefit delivery . Given these benefits and the fact that AI technology is readily available and relatively inexpensive.