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Agentic AI, a form of artificial intelligence capable of acting autonomously in decision-making and task execution without continuous human instruction, has begun transforming the socio-economic landscape of the Global South. Several countries have already started adopting this technology across various sectors. According to a Gartner report, it is estimated that 33% of enterprise software will utilize Agentic AI by 2028. This figure indicates rapid growth, considering that current usage remains minimal. This prediction reflects expectations that Agentic AI will become integral to future business operations. Additionally, the adoption of Agentic AI is expected to automate approximately 15% of daily work decisions without human intervention, highlighting its potential to enhance efficiency and productivity across sectors significantly. However, without the right strategies, Agentic AI could exacerbate economic inequality, threaten job security, and pose complex ethical challenges. This article explores the economic and social impact of Agentic AI and how stakeholders can mitigate its risks.
Socio-Economic Impact
1. Workforce Transformation
Agentic AI has accelerated automation in various industries, including manufacturing, customer service, and finance. While AI enhances efficiency and reduces operational costs, it also threatens jobs in sectors reliant on routine tasks. According to the International Labour Organization (ILO), administrative and customer service sectors in developing countries face a high risk of automation. Conversely, jobs requiring creativity, complex decision-making, and human interaction will remain relevant. Without adequate reskilling and upskilling strategies, the skills gap among workers may widen further.
2. Financial Inclusion and Digital Economy
The adoption of Agentic AI has expanded access to financial services for communities previously excluded from traditional banking systems. AI-driven fintech enables individuals without a credit history to obtain loans based on alternative data. In Indonesia, for instance, digital financial services have expanded rapidly, with AI improving the accuracy of creditworthiness assessments. However, without strict regulations, algorithmic bias in credit assessment processes may negatively impact vulnerable groups.
3. Education and Public Services
In education, AI enhances personalized learning and identifies students at risk of dropping out. In healthcare, Agentic AI has improved diagnostic efficiency and treatment accessibility in remote areas through AI-powered telemedicine. For example, in Zambia, AI is used to autonomously interpret ultrasound results to support healthcare professionals in underserved regions. These positive impacts highlight AI’s potential as an effective tool for expanding access to essential services for marginalized populations.
Challenges and Risks
1. Algorithmic Bias and Social Inequality
Many AI systems are developed using biased data, leading to unfair outcomes in financial services, healthcare, and employment. For example, AI-driven credit scoring models may discriminate against minority groups if trained on historically biased datasets. Ethical audits and community involvement in AI development are necessary to mitigate these biases.
2. Data Privacy and Cybersecurity
AI’s reliance on big data presents challenges in data privacy and cybersecurity. Many Global South countries lack strong data protection regulations, increasing the risk of personal data misuse by technology companies or governments. As a result, individuals may face data exploitation, more frequent cyberattacks, and a decline in public trust in AI technology. Additionally, the absence of robust data protection measures could deepen the digital divide, as data from the Global South is exploited by foreign companies without delivering proportional economic benefits to these nations. Strict regulations are required to ensure that AI is used in a manner that respects individual privacy rights and safeguards national economic interests.
3. Digital Divide and Technological Infrastructure
Although AI offers significant benefits, the digital divide remains a major challenge in the Global South. Limited access to the internet, electricity, and technological infrastructure leads to unequal AI benefits. Consequently, remote regions risk falling further behind in digital transformation, exacerbating economic and social disparities. Without investments in digital infrastructure and technology literacy, marginalized communities may lose access to AI-driven services that could enhance their well-being, such as AI-based education, remote healthcare, and digital economic opportunities. This situation could worsen inequality and hinder inclusive growth in developing nations.
4. Dependence on Foreign Technology and Data Control by the Global North
One of the primary risks faced by Global South countries is their reliance on AI technology developed by foreign companies, particularly from the Global North. Cloud infrastructure, AI models, and major digital platforms are predominantly controlled by corporations from developed nations. This raises the risk of digital colonialism, where data from the Global South is exploited without equitable benefits for these countries. If this dependence is not reduced, developing nations will remain passive users without full control over the AI technologies they rely on.
However, some countries have begun taking steps to reduce this reliance. India, for example, has invested in local AI development by establishing national AI research centers and creating AI models based on local languages. Brazil has promoted independent AI initiatives by supporting domestic AI startups and fostering collaboration with universities to accelerate domestic innovation. Meanwhile, South Africa is working to build a more self-sufficient AI ecosystem by promoting local data production and strengthening data protection policies to ensure that data collected within its borders is not solely exploited by foreign companies. Therefore, investment in local AI development and regulatory measures are necessary to ensure that data generated in developing countries benefits national interests rather than exclusively enriching foreign corporations.
Risk Mitigation Strategies
1. Adaptive and Ethical Regulations
Governments in the Global South must develop regulations that ensure fair, transparent, and accountable AI usage. Some countries, such as Brazil and India, have begun formulating national AI policies emphasizing inclusivity and human rights protection. Adopting global frameworks like UNESCO’s AI Ethics Framework can be an initial step in building a responsible AI ecosystem.
2. Investment in Education and Workforce Reskilling
To address labor market disruptions caused by AI automation, governments and the private sector must invest in reskilling and upskilling programs. Collaboration with universities and technology training institutions can help prepare workers for the AI era. Additionally, sending students and professionals to study in developed countries and establishing joint research projects with world-renowned AI research centers and universities can accelerate technology transfer and enhance local innovation capacity.
3. Digital Infrastructure Development
Governments and international organizations must accelerate digital infrastructure development, including expanded internet access and stable electricity in remote areas. Given the substantial investment required, governments must offer attractive incentives to both foreign and domestic investors. This is crucial because the scale of investment needed often exceeds the financial capacity of governments and the domestic private sector. With the right incentive schemes, such as tax relief, subsidies, or public-private partnerships, more investors can be encouraged to contribute to the development of a more inclusive digital ecosystem.
Several countries have successfully attracted major AI investments with effective strategies. For instance, the United Arab Emirates has attracted global tech firms by building AI innovation hubs and offering investment-friendly regulations. Singapore provides tax incentives and research funding to encourage AI companies to establish operations within its territory. Meanwhile, Brazil focuses on partnerships with international academic institutions to create a competitive AI research ecosystem. Global South countries can learn from these experiences to design more attractive and sustainable investment policies. These steps will ensure that AI benefits are distributed more widely and inclusively across the population.
4. Public-Private Partnerships for Inclusive AI Innovation
Local startups and global tech companies must collaborate with governments and NGOs to develop AI solutions relevant to local needs. AI development should focus on addressing specific challenges and serving communities rather than merely replacing existing jobs. To ensure AI benefits society without eliminating employment, concrete strategies may include incentive policies for companies that use AI to augment rather than replace workers, retraining programs for employees affected by automation, and implementing AI as a collaborative tool to enhance human productivity rather than entirely replacing roles.
Additionally, governments can encourage AI adoption in labor-intensive sectors, such as sustainable agriculture, community-based healthcare services, and interactive education that retains the role of human teachers. For example, in agriculture, AI can be used to improve crop yields through more accurate weather forecasting and early pest detection while supporting farmers in making smarter decisions without eliminating their role in the agricultural ecosystem.
5. Strengthening Local AI Ecosystems and Data Sovereignty
To reduce reliance on foreign technology, Global South countries must invest in local AI research and development. Establishing AI innovation hubs, funding local AI startups, and building domestic data centers are essential steps toward technological independence. Additionally, strengthening data protection regulations ensures that data collected from Global South users benefits national digital economic development rather than being exploited exclusively by foreign companies.
Conclusion
Agentic AI presents significant opportunities to enhance economic efficiency and inclusivity in the Global South, but it also poses risks of inequality and bias if not properly managed. With appropriate regulations, investments in education, and digital infrastructure development, developing nations can ensure that AI contributes to socio-economic well-being without exacerbating existing disparities. Stakeholders—including governments, the private sector, academia, and civil society—must collaborate to create a more fair, ethical, and inclusive AI ecosystem for all.