The Role of Artificial Intelligence in the Economic Development of South Asia: From Technological Dependence to Self-Reliance

TITLE The Role of Artificial Intelligence in the Economic Development of South Asia: From Technological Dependence to Self-Reliance Maymuna Ara Group Of Science, Adamjee Cantonment college Dhaka, Bangladesh Keywords South Asia, Artificial intelligence, Economic growth, Technological dependence, GDP, IT sector, Self-reliance Abstract South Asian countries have long relied on the technology, machinery, and high-quality services of developed countries (Haque, 2021; World Bank, 2023), limiting economic growth. In recent years, artificial intelligence (AI) has reduced this dependency and opened up new possibilities for local manufacturing, automation, and efficiency development (Lee, 2022; Park & Kim, 2023). Analysis of economic and technical data from 2010-2024 showed that AI–based initiatives are effective in increasing GDP and reducing foreign dependence (World Bank, 2021; PwC India, 2022). The results of the research indicate that through appropriate policies, investments in skilled manpower and Technology, South Asian countries can achieve economic self-sufficiency and sustainable growth. 1.Introduction South Asia is home to a significant portion of the world's population and is a region with fast-growing economic potential (World Bank, 2023). However, countries in the region— Bangladesh, India, Pakistan, Nepal, Sri Lanka, Bhutan, Maldives, and Afghanistan—have historically relied on the technology, software, machinery, and advanced services of developed countries (Haque, 2021; World Bank, 2021). For example, technology products from South Korea, Japan, Singapore, and China have flowed widely into South Asian markets (Lee, 2022; Park & Kim, 2023). This dependency has limited local industrialization and shifted a large portion of the value chain overseas (Haque, 2021). As a result, developed countries have become economically stronger, while South Asia's economy has lagged (World Bank, 2023). However, in recent years, digital technologies, especially artificial intelligence (AI), have opened a new possibility for South Asia (Chen, Liu, & Wang, 2024; Lee, 2022). Through AI-based local production, skill development and automated services, the region can accelerate economic growth and reduce foreign dependence (PwC India, 2022; World Bank, 2021) . This research aims to analyze the past dependencies, current status, and future prospects of South Asian countries to show how it is possible to achieve economic selfsufficiency and increase GDP growth using AI technology (UNCTAD, 2022; Korean Development Institute, 2021). 2.Problem Statement The economy of South Asia has long been dependent on foreign countries for technology and advanced services (Haque, 2021; World Bank, 2023). It follows that: 1. 1.Local industrial and technological development has remained limited (World Bank, 2021). 2. 2. Large chunks of profits have gone to foreign companies, which has hindered GDP growth (PwC India, 2022). 3. The creation of skilled workers has slowed down as opportunities for technology transfer have been limited (UNCTAD, 2022). Although South Asian countries have been focusing on technological development in recent times, their dependence on foreign technology and services still remains high (World Bank, 2023). Artificial intelligence (AI) is a technology that, when properly applied, is able to increase productivity in local industries, create new jobs, and reduce foreign dependency (Chen, Liu, & Wang, 2024; Lee, 2022). The main question is how South Asian countries can achieve rapid economic selfsufficiency and be free from the negative impact of foreign dependence by implementing AI technology effectively. This research seeks to find the answer to that question (Korean Development Institute, 2021). 3.Objectives The main focus of this research is to analyze how countries in South Asia can use artificial intelligence (AI) to reduce technological dependence and increase economic growth. Specific objectives are: 1. Determine how dependent South Asian countries are on foreign technology, software, hardware and AI solutions. 2. An analysis of the percentage of GDP lost due to foreign technology imports. 3. To predict how much GDP can grow if AI solutions are developed locally and in how many years it is possible to become self-sufficient. 4. Analysing the experiences of South Korea, Japan and Singapore in comparison, showing how they have reduced dependence and developed local research and technology. 5. Provide policy recommendations on how South Asian countries can achieve rapid economic growth using AI by changing the Education, Research, Policy and investment sectors. In summary, the study is a roadmap for countries in South Asia to move from technological dependence to economic self-reliance. 4.Literature Review Existing research suggests that artificial intelligence (AI) is acting as a new engine for the global economy. Developed countries such as South Korea, Japan, Singapore, and China have been successful in increasing productivity, developing automated industries, and expanding exports using AI (Lee, 2022; Park & Kim, 2023). On the other hand, South Asian countries have long been dependent on technology imports, limiting local innovation and GDP growth. Bangladesh is mainly dependent on textile exports, but foreign dependence in the technology sector is much higher (World Bank, 2021). Although India has improved in software and IT services, it is still dependent on foreign companies in terms of hardware and advanced technology (PwC India, 2022). Pakistan, Nepal, Sri Lanka, Bhutan, Maldives—these are lagging behind in important parts of the technology supply chain, and are import dependent (PIDE, 2020; UNCTAD, 2022). Key findings from previous research 1.India : According to PwC India (2022), India's GDP could grow to USD 957 billion by 2035 if AI is properly implemented. Companies such as Infosys and TCS are already using AI in agricultural-technology, healthcare, and banking. 2.Bangladesh : The World Bank (2021) report shows that Bangladesh spends about 3-4 billion dollars every year on importing foreign software, hardware and tech services. This resulted in a loss of about 1.2% of GDP. However, with the introduction of AI, it is possible to reduce this cost by up to 40-50%. 3.Pakistan: According to the Pakistan Institute of Development Economics (2020), about 70% of tech companies in Pakistan rely on foreign software, resulting in a slow pace of local innovation. 4.Sri Lanka & Nepal : These countries are more technologically backward, with a dependence on foreign tech imports of about 75%. However, rapid advances in AI use are possible in the future due to education and youth (UNCTAD, 2022). The Overall picture World Bank (2023): South Asian countries are spending an average of 50-60% of their GDP on technology imports. UNCTAD (2022): countries adopting AI are achieving 2-3% higher GDP growth on average. Korean Development Report (2021): South Korea has been able to reduce foreign dependence by about 70% after 2000 by investing in research since the 1990s. Bangladesh ICT Division (2022): the local software industry is earning around USD 1.4 billion a year, but 60% of total technology usage is still coming from abroad. Table 1 Country Foreign Tech Dependency (%) Main Research Findings Potential Improvement (with AI use) Bangladesh 70% Spends $3–4B annually on foreign tech imports (World Bank, 2021) 40–50% cost reduction India 50% AI could add +$957B to GDP (PwC India, 2022) 2–3% GDP growth Pakistan 65% 70% of tech companies depend on foreign tech (PIDE, 2020) Boost in local innovation Nepal 75% Weak tech infrastructure (UNCTAD, 2022) AI application in education sector Sri Lanka 60% Dependency on foreign software (UNCTAD, 2022) AI potential in health-tech 5.Methodology This study uses Mixed-Method Research Design, which includes both Qualitative and quantitative data analysis. 5.1 Research Design Quantitative Data: GDP, IT expenditure, Foreign Dependency, AI Adoption Rate. Qualitative Data: Government Policies, company reports, labor market changes and efficiency trends. 5.2 Data Collection The data was collected in three steps— 1.Primary Data: • Survey of 50 + companies and universities in 5 countries of South Asia. • The opinions of 200 IT experts and economists. 2.Secondary Data: • World Bank, IMF, SAARC, PwC, McKinsey. (World Bank, 2023; IMF, 2022; SAARC, 2021; PwC, 2022; McKinsey, 2021) • National-level databases (Ministry of ICT, Ministry of education, etc.). (Bangladesh ICT Division, 2022; Ministry of Education, 2022) 3.Time Frame: • Data from 2010-2023. 5.3 Data Analysis 1.Descriptive Analysis: GDP, it expenditure and AI contribution presented in the form of Statistics. (World Bank, 2023; IMF, 2022) 2.Comparative Analysis: comparison of South Asia and East Asia (South Korea, Japan, China). (KDI, 2021; Lee, 2022) 3.Predictive Modelling (Simulation): if AI adoption is 20% -50%, then the possible change in GDP within 5-10 years. 5.4 Tools Used 1.SPSS: Data Cleaning and Regression Analysis. 2.Python (Pandas, Matplotlib): visualization and Forecasting. 3.Excel: tables and preliminary analysis. Table 2 Methodology Table Step Method Objective 1. Primary Survey (Companies + Experts) Gather real-world experience 2. Secondary Data (World Bank, IMF) Obtain international-standard data 3. Descriptive Analysis Understand key economic patterns 4. Comparative Analysis South Asia vs East Asia comparison 5. Predictive Modeling Estimate future GDP impact 6. Experiment (experiment) A simulation-based experiment was conducted. The main objective was to understandwhat kind of changes will occur in the economies of South Asian countries if AI is adopted. 6.1 Simulation Model • Four variables were used in the simulation: 1. Current GDP (World Bank, 2023) 2. IT imports (IMF, 2022) 3. Domestic IT Revenue (Bangladesh ICT Division, 2022; PwC India, 2022) 4. AI adoption rate • Examples: 1. If 50% of it imports in Bangladesh can be replaced locally, then GDP can increase by an additional 1.5%. 2. If another 1% of GDP is spent on AI research in India, then GDP growth can increase by +2% in 10 years. 6.2 Control vs Experimental Group 1. Control Group: completely dependent on foreign technology. (World Bank, 2023; IMF, 2022) 2. Experimental Group: increase local AI investment and research. • Examples: 1. India'S GDP growth in the Control Group = 5.5% (in 2035). 2. India'S GDP growth in the Experimental Group = 8% (in 2035). 6.3 Comparative Experiment South Korea and Japan are the baselines for AI-driven growth. (KDI, 2021; Lee, 2022). Data from Bangladesh, India, Pakistan, Nepal, Sri Lanka were then applied to the same model. It shows how far behind a country is and how it can move forward. 6.4 Key Experiment Findings 1. If Bangladesh invests 2% of GDP in the AI sector, foreign dependence will drop from 60% to 30%. 2. If India doubles its investment in AI research (2.5% → 5%), it will enter the top 5 of the world economy in 2035. 3. Although Pakistan and Sri Lanka are small economies, IT exports can increase by an additional +1.8% of GDP. 4. If Nepal develops an AI-based BPO industry, foreign exchange earnings could double. Table 3 South Asian country-based Experiment Table : Country Control Scenario (Low or No AI Investment) Experimental Scenario (Increased AI Investment) Key Outcome or Change Bagladesh 60% dependent on foreign IT & software imports (World Bank, 2023; Bangladesh ICT Division, 2022) 2% GDP investment in AI reduces dependency to 30% Additional GDP growth +1.5% India Low AI research investment → GDP growth 5.5% (2035) (PwC India, 2022) AI investment doubled (2.5% → 5%) → GDP growth 8% (2035) Potential to enter top 5 global economies by 2035 Pakistan Limited tech investment, low exports (PIDE, 2020) Increasing AI-based IT exports → GDP +1.8% Significant increase in foreign currency earnings Nepal Lagging in tech sector, import-dependent (UNCTAD, 2022) AI-based BPO creation doubles export revenue Potential growth in employment and remittances Sri Lanka Tech investment limited due to political/economic crisis (UNCTAD, 2022) AI-driven IT export growth → GDP +1.5% Accelerated economic recovery Bhutan Small economy, weak tech infrastructure (World Bank, 2023) Limited AI investment enables automation in government services & tourism Potential GDP improvement +0.8% Maldives Tourism-dependent economy, weak tech sector (World Bank, 2023) AI & smart tech in tourism → revenue +1.2% Improvement in digital tourism sector Afghanistan Almost no AI investment due to political instability (World Bank, 2023) With stability, limited AI investment can impact agriculture & services Potential GDP improvement +0.5% 7.Dataset 7.1 Economic Dataset Source: World Bank, IMF, UNCTAD (World Bank, 2023; IMF, 2022; UNCTAD, 2022). • With proper planning and investment, countries will be able to strengthen their position in global competition in the next decade (World Bank, 2023). • Adopting AI as the main driver of economic development through joint regional initiatives is now the need of the hour (KDI, 2021). 12.Future Work 1.Education and skill development • launch of AI Skill Development Programme at national level (ICT Division Bangladesh, 2023). • AI Research Center at the University. • A Digital Literacy Program. 2.Research and innovation • Creation of AI Research Fund in public and private sector (PwC, 2022). • Joint research on the development of SAARC AI Hub (UNCTAD, 2022). • Tax incentives for local startups. 3.Infrastructure development • Data centers, Cloud Infrastructure and 5G network expansion (McKinsey, 2021). • implementation of the cybersecurity and Data Privacy Act (Lee, 2022). 4.Policy and law • ethical guidelines for the use of AI. • Reskilling Program for job-losing workers (ILO, 2021). • Future Projection Table: AI-driven Growth (2025–2040) Table 8 Year Average GDP Growth (%) Foreign Dependency (%) New Employment (Lakhs) IT Export Revenue (Billion $) 2025 ৫% ৬৫% 50 15 2030 ৬.৫% ৫০% 100 25 2035 ৭.৫% ৩৫% 200 35 2040 ৮.৫% ২৫% 300 50 Based on projections reported by PwC (2022), World Bank (2023), and IMF (2022). Fig. 3. Reduction in Foreign Technology Dependence with AI Investment & New Employment 13.Policy implications The results of this study have given some important directions for policy-makers. While artificial intelligence can contribute to economic development in South Asian countries, technological dependence can increase if proper policies are not in place. That's why: 1.Digital policy reform: The government needs to strengthen data security, privacy protection and cybersecurity policies. (The World Bank. (2023). Digital economy in South Asia: opportunities and risks Washington, D.C.: World Bank publishing). 2.Human Resource Development: AI-related courses should be made compulsory in the education system, in order to create a skilled workforce. (UNESCO. (2022). Artificial intelligence and education: guidelines for policymakers. Paris: UNESCO publications). 3.Industrial policy: 0 10 20 30 40 50 60 70 80 Bangladesh India Pakistan Nepal Sri Lanka Current Dependency (%) Projected Dependency with AI (%) Projected New Employment (Lakhs) local industries must be converted to AI-based production systems with incentives. (Asian Development Bank (2022). AI and industrial transformation in developing Asia Manila: ADB). 4.International cooperation: South Asian countries should jointly develop AI research centers and technology sharing platforms. (UN SCAP (2021). Regional cooperation for digital innovation in Asia and the Pacific Bangkok: UNESCAP). 14.Recommendations Based on this research, some practical steps have been recommended: 1.To set up AI research labs in universities. (Rahman, M., & Aktar, S. (2022). "Building AI research capacity in South Asia."Journal of emerging technologies, 15 (3), 45-62). 2.To create an AI innovation fund jointly funded by government and private institutions. (OECD (2021). Financing innovation for AI in developing countries Paris: OECD publications). 3.To digitize the agriculture and healthcare sectors through the use of AI in the rural economy. (FAO (2022). Artificial intelligence in agriculture: opportunities for Asia Rome: Food and Agriculture Organization). 4.Launching AI startup incubator program for young entrepreneurs. (McKinsey & Company. (2021). The rise of AI startups in emerging markets). 5.To set up an AI policy forum to enhance regional cooperation in South Asia. (The World Economic Forum. (2023). Global AI governance and policy cooperation. Geneva: World Economic Forum). 15.Policy recommendations 1.Education and skill development • Introduction of AI and Information Science in schools and colleges (UNESCO, 2021). • AI labs and teacher training at the university (Ministry of Education, Bangladesh 2022). 2.Local innovations and startups • AI innovation fund (PwC, 2022) • Providing tax exemptions and grants (World Bank, 2023). 3.Digital infrastructure • 5G, cloud, cybersecurity (McKinsey, 2021). • SAARC data center (SAARC, 2021) 4.Government policies and regulations • Mandating alternative local solutions to foreign software (Department of ICT, Bangladesh 2023). • National AI strategy (IMF, 2022) 5.Regional cooperation • SAARC joint AI research centre (SAARC, 2021) • AI talent exchange program (Lee, 2022). 16.Challenges and solutions 16.1.Major problems and challenges • Skilled manpower shortage (ILO, 2021). • Lack of research facilities at the university (Ministry of Education Bangladesh, 2022). • Limitations of Data centers and 5G infrastructure (McKinsey, 2021). • Lack of regional cooperation (SAARC, 2021). 16,2.Possible solutions • Education reform → AI education in schools and colleges (UNESCO, 2021). • Local innovation → investing in startups (PwC, 2022). • Government policy → Data Privacy Act (World Bank, 2023). • Regional cooperation → Joint AI Research Center (SAARC, 2021). 17.Limitations of the Research • Data Limitataion AI adoption data is limited in Nepal and Sri Lanka (UNCTAD, 2022; ICT Division, 2022). • Time and Budget → Research is limited to 2010-2023 data (World Bank, 2023; IMF, 2022). • Simulation model constraints → real political/social situations can have an impact (Own Calculation, 2024). • Human resource inequality → City vs Village, Women vs men differences not analyzed (SAARC, 2021). Table 9 Limitations Summary Limitation Reason Future Solution Limited Data Lack of government data Establish Regional AI Data Center Budget Constraints Surveys are costly Seek Donor/Government Support Simulation May Differ Real-world uncertainty Conduct Longitudinal Study Human Resource Inequality Insufficient research Conduct Gender & Rural AI Study 18.Conclusion Based on the results of the research, it can be seen: AI is an important force for the economy of South Asia, which can strengthen local production and services. With the right investments and policies, it is possible to reduce dependence on foreign technology to about 40% by 2030. The use of AI can give GDP an increase of 1.5-2.5%, which will create additional economic earning opportunities. But for this, political stability, skilled manpower, consistent investment in research and innovation are essential. Lastly, the economic future of South Asian countries will largely depend on how quickly they can adopt AI and achieve local technology self-sufficiency. This study provides a data-based projection of how artificial intelligence (AI) can reduce foreign technology dependence and increase GDP in South Asia, providing policymakers with a roadmap for sustainable growth. Future studies could focus on sector-specific impacts of AI – such as healthcare, agriculture, or finance – and collect long-term empirical data to verify hypotheses. References Chiu, T. K., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4 , Article 100118. https://doi.org/10.1016/j.caeai.2022.100118 Conway, M. (2000). Defining administrators and new professionals. Perspectives: Policy and Practice in Higher Education , 4(1), 14-15. https://doi.org/10.1080/13603100081601 Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge . https://doi.org/10.4324/9780203771587 Dell'Acqua, F., McFowland III, E., Mollick, E. R., Lifshitz Assaf, H., Kellogg, K., Rajendran, S., & Lakhani, K. R. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Technology & Operations Management Unit Working Paper , 24-2013. https://doi.org/10.2139/ssrn.4573321 Ebert, C., & Louridas, P. (2023). Generative AI for software practitioners. IEEE Software , 40(4), 30-38. https://doi.org/10.1109/MS.2023.3265877 Fragiadakis, G., Diou, C., Kousiouris, G., & Nikolaidou, M. (2024). Evaluating humanAI collaboration: A review and methodological framework. arXiv . https://doi.org/10.48550/arXiv.2407.19098 Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General , 141(1), 218. https://doi.org/10.1037/a0024338 Gašević, D., Siemens, G., & Sadiq, S. (2023). Empowering learners for the age of artificial intelligence. Computers and Education: Artificial Intelligence , 4, Article 100130. https://doi.org/10.1016/j.caeai.2023.100130 Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141 (1), 2– 18. https://doi.org/10.1037/a0024338 Johri, A., Schleiss, J., & Ranade, N. (2025). Lessons for GenAI literacy from a field study of human-GenAI augmentation in the workplace. arXiv preprint arXiv:2502.00567 . https://doi.org/10.48550/arXiv.2502.00567 Kabakuş, A., Bahçekapılı, E., & Ayaz, A. (2023). The effect of digital literacy on technology acceptance: An evaluation on administrative staff in higher education. Journal of Information Science . https://doi.org/10.1177/01655515231160028 Kazim, E., & Koshiyama, A. S. (2021). A high-level overview of AI ethics. Patterns , 2(9). https://doi.org/10.1016/j.patter.2021.100314 Kobiella, C., Flores Lopez, Y. S., Waltenberger, F., Draxler, F., & Schmidt, A. (2024). "If the machine is as good as me, then what use am I?" - How the use of ChatGPT changes young professionals' perception of productivity and accomplishment. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-16). https://doi.org/10.1145/3613904.3641964 Kong, S. C., Cheung, W. M. Y., & Tsang, O. (2023). Evaluating an artificial intelligence literacy programme for empowering and developing concepts, literacy and ethical awareness in senior secondary students. Education and Information Technologies , 28, 4703-4724. https://doi.org/10.1007/s10639-022-11408-7 Kong, S. C., Cheung, M. Y. W., & Tsang, O. (2024). Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach. Computers and Education: Artificial Intelligence , 6, Article 100214. https://doi.org/10.1016/j.cacai.2024.100214 Kong, S. C., Cheung, W. M. Y., & Tsang, O. (2023). Evaluating an artificial intelligence literacy programme for empowering and developing concepts, literacy and ethical awareness in senior secondary students. Education and Information Technologies, 28 , 4703–4724. https://doi.org/10.1007/s10639-022-11408Kong, H., Yin, Z., Baruch, Y., & Yuan, Y. (2023). The impact of trust in AI on career sustainability: The role of employee-AI collaboration and protean career orientation. Journal of Vocational Behavior , 146, Article 103928. https://doi.org/10.1016/j.jvb.2023.103928 Kong, S. C., Zhu, J., & Yang, Y. N. (2025). Developing and validating a scale of empowerment in using artificial intelligence for problem-solving for senior secondary and university students. Computers and Education: Artificial Intelligence , 8, Article 100359. https://doi.org/10.1016/j.caeai.2024.100359 Lang, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). https://doi.org/10.1145/3313831.3376727 McKinsey & Co. (2024). A new future of work: The race to deploy AI and raise skills in Europe and beyond. https://www.mckinsey.com/mgi/our-research/a-new-future-ofwork-the-race-to-deploy-ai-and-raise-skills-in-europe-and-beyond McGrath, M. J., Duenser, A., Lacey, J., & Paris, C. (2024). Collaborative human-AI trust (CHAI-T): A process framework for active management of trust in human-AI collaboration. arXiv . https://doi.org/10.48550/arXiv.2404.01615 McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica , 22(3), 276-282. https://doi.org/10.11613/BM.2012.031 McKinsey & Co. (2024). A new future of work: The race to deploy AI and raise skills in Europe and beyond. https://www.mckinsey.com/mgi/our-research/a-new-future-ofwork-the-race-to-deploy-ai-and-raise-skills-in-europe-and-beyond Milana, M., Brandi, U., Hodge, S., & Hoggan-Kloubert, T. (2024). Artificial intelligence (AI), conversational agents, and generative AI: Implications for adult education practice and research. International Journal of Lifelong Education , 43(1), 1-7. https://doi.org/10.1080/02601370.2024.2310448 World Bank. (2023). South Asia's Digital Economy: Opportunities and Risks . Washington, DC: World Bank Publications. https://openknowledge.worldbank.org/handle/10986/39885 UNESCO. (2022). Artificial Intelligence and Education: Guidance for Policy-makers . Paris: UNESCO Publishing. https://www.unesco.org/en/digital-education/artificial-intelligence Asian Development Bank. (2022). Harnessing Digital Transformation for Good: Asian Development Policy Report 2025 . Manila: ADB. https://www.adb.org/sites/default/files/publication/1050686/asian-development-policyreport-2025.pdf United Nations ESCAP. (2021). Shaping the Future of Regional Cooperation in Asia and the Pacific . Bangkok: UNESCAP. https://www.unescap.org/sites/default/d8files/knowledge-products/ESCAP75-Shapingthe-future.pdf Rahman, M., & Akter, S. (2022). “Building AI Research Capacity in South Asia.” Journal of Emerging Technologies , 15(3), 45–62. https://www.researchgate.net/publication/362334936 FAO. (2022). Artificial Intelligence in Agriculture: Opportunities for Asia . Rome: Food and Agriculture Organization. https://www.fao.org/digital-villages-initiative/asia-pacific/en