The The Application of Artificial Intelligence and Machine Learning to Enhance Results-Based Management

Bongs Lainjo

doi:10.51519/journalisi.v5i4.609

Bongs Lainjo Cybermatic International, Canada

Keywords: Artificial Intelligence, Machine Learning, Results-Based Management, Decision-Making, Accountability, Transparency.

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) technologies have revolutionized numerous industries and sectors, offering transformative potential for Results-Based Management (RBM). RBM is a management paradigm wherein organizations and government entities plan and assess the effectiveness of their projects, policies, or programs in achieving outcomes. Integrating AI and ML into RBM can significantly enhance outcomes, fostering data-driven and informed decision-making. AI and ML integration into RBM practices facilitates improved decision-making, resource optimization, accountability, and transparency. These technologies enhance RBM by enabling predictive analytics, real-time monitoring, task automation, customization, and scalability. The dynamic synergy of AI and ML extends beyond RBM into sectors like agriculture, public health, academia, and public administration. Despite their immense potential, AI and ML tools face challenges such as perpetuating inaccuracies and biases due to inherent biases or low data quality. Nevertheless, their application in RBM empowers organizations to plan better, monitor, evaluate, and refine projects and programs, optimizing resource allocation and performance. Ongoing research, ethical considerations, data quality, and accountability are essential priorities for harnessing the full benefits of AI and ML in RBM. Therefore, this research paper investigates the potential of AI and ML tools and technologies in improving results-based management. It comprehensively reviews existing literature, practical applications, and case studies to elucidate how AI and ML can enhance results-based management practices and contribute to better decision-making.

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References

Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2023). Re-thinking data strategy and integration for artificial intelligence: Concepts, opportunities, and challenges. Applied Sciences, 13(12), 1–33. https://doi.org/10.3390/app13127082

Aly, W. O. (2015). A framework for results-based management to the public sector in Egypt: Challenges and opportunities. Journal of Public Administration and Governance, 4(4), 23–45.

Beasley, K. (2021). Council post: Unlocking the power of predictive analytics with AI. Forbes.

Bester, A. (2012). Results-Based management in the United Nations Development System: Progress and challenges. In United Nations (pp. 1–53).

Choi, H.-W., Kim, H.-J., Kim, S.-K., & Na, W. S. (2023). An overview of drone applications in the construction industry. Drones, 7(8), 1–21.

Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60(102383), 1–17. ScienceDirect.

Cordova-Pozo, K., Hoopes, A. J., Cordova, F., Vega, B., Segura, Z., & Hagens, A. (2018). Applying the results-based management framework to the CERCA multi-component project in adolescent sexual and reproductive health: a retrospective analysis. Reproductive Health, 15(1), 1–13.

El Hajj, M., & Hammoud, J. (2023). Unveiling the influence of artificial intelligence and machine learning on financial markets: A comprehensive analysis of AI applications in trading, risk management, and financial operations. Journal of Risk and Financial Management, 16(10), 1–16.

Earnest & Young. (n.d.). Intelligent forecasting and scenario modelling.

Forbes Expert Panel. (2023). Council post: 15 tips to help businesses use AI and automation responsibly and effectively. Forbes.

Green, G. (2022). Five ways AI is saving wildlife – from counting chimps to locating whales. The Guardian.

Gruetzemacher, R. (2022). The power of Natural Language Processing. Harvard Business Review.

Haasdijk, E. (n.d.). A call for transparency and responsibility in artificial intelligence. Deloitte.

Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3(3), 119–132.

Haneef, R., Kab, S., Hrzic, R., Fuentes, S., Fosse-Edorh, S., Cosson, E., & Gallay, A. (2021). Use of artificial intelligence for public health surveillance: A case study to develop a machine learning-algorithm to estimate the incidence of diabetes mellitus in France. Archives of Public Health, 79(1), 1–13.

Linaza, M. T., Posada, J., Bund, J., Eisert, P., Quartulli, M., Döllner, J., Pagani, A., G. Olaizola, I., Barriguinha, A., Moysiadis, T., & Lucat, L. (2021). Data-Driven artificial intelligence applications for sustainable precision agriculture. Agronomy, 11(6), 1–14.

Lokanan, M. (2023). Predicting mobile money transaction fraud using machine learning algorithms. Applied AI Letters, 4(2), 1–15.

Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022). Defining organizational AI governance. AI and Ethics.

Mesmari, S. A. (2023). Transforming data into actionable insights with cognitive computing and AI. Journal of Software Engineering and Applications, 16(6), 211–222.

Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 1–20.

Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., Gupta, M., & Niehaves, B. (2023). Examining how AI capabilities can foster organizational performance in public organizations. Government Information Quarterly, 40(2), 1–14.

Nair, M., Andersson, J., Nygren, J. M., & Lundgren, L. E. (2023). Barriers and enablers for implementation of an artificial intelligence–based decision support tool to reduce the risk of readmission of patients with heart failure: Stakeholder interviews. JMIR Formative Research, 7(1).

Nieto-Rodriguez, A., & Vargas, R. V. (2023). How AI will transform project management. Harvard Business Review.

Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2018). Big Data and AI – A transformational shift for government: So, what next for research? Public Policy and Administration, 35(1), 25–44.

Perakakis, E., Mastorakis, G., & Kopanakis, I. (2019). Social media monitoring: An innovative intelligent approach. Designs, 3(2), 1–12.

Perifanis, N.-A., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review. Information, 14(2). MDPI.

Rojek, I., Jasiulewicz-Kaczmarek, M., Piechowski, M., & Mikołajewski, D. (2023). An artificial intelligence approach for improving maintenance to supervise machine failures and support their repair. Applied Sciences, 13(8), 1–16.

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 1–21. Springer.

Sarker, I. H. (2022). AI-Based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 1–20. springer.

Şengönül, E., Samet, R., Abu Al-Haija, Q., Alqahtani, A., Alturki, B., & Alsulami, A. A. (2023). An analysis of artificial intelligence techniques in surveillance video anomaly detection: A comprehensive survey. Applied Sciences, 13(8).

Shah, S., Ghomeshi, H., Vakaj, E., Cooper, E., & Fouad, S. (2023). A review of natural language processing in contact centre automation. 823–846.

Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial intelligence and the implementation challenge. Journal of Medical Internet Research, 21(7).

Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54–70.

Stahl, B. C. (2021). Addressing ethical issues in AI. SpringerBriefs in Research and Innovation Governance, 55–79.

Tirkolaee, E. B., Sadeghi, S., Mooseloo, F. M., Vandchali, H. R., & Aeini, S. (2021). Application of machine learning in supply chain management: A comprehensive overview of the main areas. Mathematical Problems in Engineering, 2021(1), 1–14.

UNESCO. (2023). Artificial intelligence: Examples of ethical dilemmas.

United Nations Population Fund. (2019). . results-based management principles and standards: The 3+5 framework for self-assessment (pp. 1–30).

Walch, K. (2020). How AI is finding patterns and anomalies in your data. Forbes.

Wang, W.-H., & Hsu, W.-S. (2023). Integrating artificial intelligence and wearable IoT system in long-term care environments. Sensors, 23(13), 1–13.

Younanzadeh, E. (2022). Council post: Data quality is also an AI problem. Forbes.

Zaino, J. (2021). Case study: Crown College uses predictive analytics to retain at-risk students. DATAVERSITY.

Zellner, W. (2022). UPMC to advance pioneering analytics efforts through new microsoft collaboration. UPMC & Pitt Health Sciences News Blog.