AI-Powered Predictive Analytics for Multi-Sector Economic Growth in Saudi Arabia

References

[1] Alahmari, A., & Duncan, B. (2021). Cybersecurity risk management in digital transformation: A Saudi perspective. Journal of Information Security and Applications, 58, 102789.

[2] Alanzi, T. (2021). A review of mobile applications available in the app and Google Play stores used during the COVID-19 outbreak. Journal of Multidisciplinary Healthcare, 14, 45–57.

[3] Alghamdi, A. (2022). Digital transformation in Saudi Arabia: Opportunities and challenges for Vision 2030. International Journal of Advanced Computer Science and Applications, 13(3), 120–129.

[4] Alharbi, A. (2023). Artificial intelligence adoption in Saudi public sector organizations. Government Information Quarterly, 40(4), 101864.

[5] Alotaibi, Y. (2023). Artificial intelligence and smart city development in Saudi Arabia. Smart Cities, 6(5), 2341–2359.

[6] Alqahtani, S., Li, S., & Din, Q. M. (2025). Saudi Vision 2030: Government investment in AI and its impact on job creation. Advances in Economics, Business and Management Research.

[7] Alshehri, M. (2020). Digital government transformation in Saudi Arabia: Challenges and opportunities. International Journal of Public Administration in the Digital Age, 7(2), 1–18.

[8] Alshuwaikhat, H., & Mohammed, I. (2021). Sustainability and urban transformation in Saudi Arabia. Sustainability, 13(12), 6782.

[9] Baryannis, G., Dani, S., & Antoniou, G. (2021). Predicting supply chain risks using machine learning. International Journal of Production Research, 59(7), 2174–2192.

[10] Davenport, T. H., & Mittal, N. (2022). How generative AI is changing creative work. Harvard Business Review.

[11] Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., & Williams,

[12] European Commission. (2021). Artificial Intelligence Act proposal. European Commission.

[13] Hassounah, M., Raheel, H., & Alhefzi, M. (2020). Digital response during COVID-19 in Saudi Arabia. Journal of Medical Internet Research, 22(9), e19338.

[14] International Monetary Fund. (2024). Saudi Arabia: Article IV consultation report. IMF.

[15] Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling the blockchain enabled traceability in agriculture supply chain. International Journal of Information Management, 52, 101967.

[16] Kshetri, N. (2021). The economics of artificial intelligence in emerging economies. Computer, 54(8), 86–90.

[17] Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M5 accuracy competition: Results, findings and conclusions. International Journal of Forecasting, 36(1), 54–74.

[18] McKinsey Global Institute. (2022). The state of AI in 2022. McKinsey & Company.

[19] Ministry of Communications and Information Technology. (2023). Digital economy report: Kingdom of Saudi Arabia. MCIT.

[20] OECD. (2021). State of implementation of the OECD AI Principles. OECD Publishing.

[21] PwC Middle East. (2020). The potential impact of AI in the Middle East. PwC.

[22] Saudi Data and Artificial Intelligence Authority. (2020). National Strategy for Data and AI: Realizing our best tomorrow. SDAIA.

[23] Saudi Data and Artificial Intelligence Authority. (2023). Data and AI national capabilities report. SDAIA.

[24] Saudi Vision 2030. (2021). Vision 2030 realization programs overview. Government of Saudi Arabia.

[25] Sharma, R., Mithas, S., & Kankanhalli, A. (2021). Transforming decision-making processes through AI. MIS Quarterly Executive, 20(3), 167–184.

[26] Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2021). Organizational decision-making structures in the age of AI. California Management Review, 63(4), 66–83.

[27] Topol, E. (2020). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25, 44–56.

[28] UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO.

[29] World Bank. (2022). GovTech maturity index: The state of public sector digital transformation. World Bank.

[30] World Economic Forum. (2023). AI governance alliance briefing paper series. WEF.

@article{1717702,
      author = {Abdul Faheem},
      title = {AI-Powered Predictive Analytics for Multi-Sector Economic Growth in Saudi Arabia},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {2021-2032},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717702.pdf},
      abstract = {Purpose: This study examines the role of AI-powered predictive analytics in supporting multi-sector economic growth in Saudi Arabia under the strategic direction of Vision 2030. The paper focuses on how predictive modelling, machine learning, big data integration, and AI-based decision intelligence can improve productivity, investment planning, risk management, service delivery, and non-oil sector competitiveness.
Design/Methodology/Approach: The paper adopts a structured review methodology based on recent academic and institutional literature from 2020 to 2025. It synthesizes evidence from studies on artificial intelligence, digital transformation, predictive analytics, Saudi Vision 2030, and sector-specific innovation. The review covers energy, healthcare, logistics, finance, tourism, smart cities, manufacturing, and public administration. The structure follows the attached reference paper model by combining policy context, literature synthesis, sectoral analysis, conceptual framework, practical tables, and visual representation.
Findings: The review finds that predictive analytics can strengthen Saudi Arabia’s economic diversification by enabling proactive planning, demand forecasting, preventive maintenance, early risk detection, smart resource allocation, and real-time decision support. Its strongest contribution appears in sectors where large data flows already exist, such as energy, finance, logistics, healthcare, and smart cities. However, the success of predictive analytics depends on data quality, interoperability, cyber security, ethical AI governance, cloud readiness, institutional coordination, and advanced human capital.
Conclusion: AI-powered predictive analytics should be treated as a national economic capability rather than a narrow technical tool. When supported by strong governance and sector-specific implementation, it can help Saudi Arabia move from reactive decision-making to proactive, data-driven economic planning.
Practical Implications: The paper proposes a multi-sector predictive analytics framework aligned with Vision 2030. The framework emphasizes national data infrastructure, AI governance, sector integration, talent development, and measurable economic impact.
Originality/Value: This review contributes a Saudi-focused conceptual model showing how AI-powered predictive analytics can support sustainable economic diversification and knowledge-based growth across multiple sectors.},
      keywords = {Artificial intelligence, predictive analytics, Saudi Arabia, Vision 2030, economic diversification, machine learning, big data, smart cities, digital transformation.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717702}
  }