Critical Success Factors for Machine Learning Implementation in Organizations: An Analysis of Readiness, Data Governance, and Organizational Capabilities Through a Systematic Literature Review from 2020 to 2026
Keywords:
Artificial Intelligence Adoption, Data Governance, Machine Learning Implementation, Organizational Capability, Organizational ReadinessAbstract
Advances in digital transformation and Artificial Intelligence (AI) have driven organizations to adopt Machine Learning (ML) to improve operational efficiency, decision-making quality, and competitiveness. However, the success of ML implementation is still influenced by various organizational, data, and technological factors. This study aims to identify and synthesize the critical success factors for Machine Learning implementation in organizations through a Systematic Literature Review (SLR) approach. The study was conducted in accordance with the PRISMA 2020 guidelines, which cover the stages of identification, screening, eligibility assessment, and article selection. A total of 78 articles that met the inclusion criteria during the 2020–2026 period were analyzed using descriptive, thematic, and narrative approaches. The study identified five key factors influencing the successful implementation of machine learning: organizational readiness, data governance, organizational capability, technological capability, and environmental factors. Among these factors, organizational readiness, data governance, and organizational capability were the most dominant. This study produced a conceptual model that explains the relationships among the critical success factors for Machine Learning implementation and provides theoretical and practical contributions to organizations in designing effective and sustainable ML implementation strategies.
References
Anggreacia, W. C., & Ghazali, A. (2025). AI readiness and maturity assessment for ethical and responsible AI adoption: A case study of PT Sarana Multi Infrastruktur. Multidisciplinary Output Research for Actual and International Issue, 6(1), 1507–1517.
Baker, J. (2022). The technology–organization–environment framework. In Information Systems Theory: Explaining and Predicting Our Digital Society. Springer.
Bholat, D., Hansen, S., Santos, P., & Schonhardt-Bailey, C. (2023). Machine learning applications in finance: A review. Journal of Financial Innovation, 12(2), 45–63.
Booth, A., Sutton, A., Clowes, M., & Martyn-St James, M. (2022). Systematic approaches to a successful literature review (3rd ed.). Sage Publications.
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. Sage Publications.
Brynjolfsson, E., Li, D., & Raymond, L. (2024). Generative AI and organizational transformation. Management Science, 70(4), 2011–2030.
Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., Conboy, K., Doyle, R., Dubey, R., Dutot, V., Felix, R., Goyal, D., Gustafsson, A., Hinsch, C., Jebabli, I., ... Wamba, S. F. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative AI for research, practice and policy. International Journal of Information Management, 71, 102642.
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI and machine learning in organizations: Emerging trends and research directions. Business & Information Systems Engineering, 66(1), 1–18.
Floridi, L., Luetge, C., Pagallo, U., Schafer, B., & Valcke, P. (2022). AI governance and ethical frameworks. Minds and Machines, 32(4), 567–589.
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2024). Industry 4.0 technologies and machine learning adoption in manufacturing. Technological Forecasting and Social Change, 199, 123229.
Gill, N., Mathur, A., & Conde, M. V. (2022). A brief overview of AI governance for responsible machine learning systems. arXiv.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2022). Partial least squares structural equation modeling (PLS-SEM) using R. Springer.
Kitchenham, B., Brereton, P., Niazi, M., Linkman, S., Pretorius, R., & Budgen, D. (2020). Systematic literature reviews in software engineering: A tertiary study. Information and Software Technology, 104, 1–20.
McClure, J., & Gerdau, G. (2026). Why AI readiness is an organizational learning problem, not a technology purchase. arXiv.
Muhyi, H. A., Chan, A., Herawaty, T., Sukamadewi, R., & Kahfi, A. A. (2024). Organizational readiness for artificial intelligence adoption in public and private sectors. Sosiohumaniora, 26(3), 421–435.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.
Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., Britten, N., Roen, K., & Duffy, S. (2022). Guidance on the conduct of narrative synthesis in systematic reviews. ESRC Methods Programme.
Pratiwi, A., Rahmawyanet, M. E., Putra, P. A. W., & Sensuse, D. I. (2025). Systematic literature review on artificial intelligence in Indonesia’s public sector: Reimagining digital government. Jurnal Informatika dan Teknologi Komputer, 11(2), 304–318.
Qasabandiyah, M. K., et al. (2025). Data quality and machine learning performance in organizational environments. Journal of Information Systems and Software Research.
Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., & Crespo, J. (2023). Hidden technical debt in machine learning systems revisited. Communications of the ACM, 66(7), 78–87.
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.
Topol, E. (2024). Artificial intelligence in healthcare: Past, present, and future. Nature Medicine, 30(1), 14–22.
Tortorella, G. L., Fogliatto, F. S., Sunder, M. V., Cauchick-Miguel, P. A., & McFarlane, D. (2023). Organizational learning and digital transformation capability. Technological Forecasting and Social Change, 189, 122330.
UNESCO. (2024). Indonesia Artificial Intelligence Readiness Assessment Report. UNESCO Jakarta Office.
Übellacker, T. (2025). Making sense of AI limitations: How individual perceptions shape organizational readiness for AI adoption. arXiv.
Vial, G. (2021). Understanding digital transformation: A review and research agenda. Journal of Strategic Information Systems, 30(2), 101620.
Walhidayah, I., Suryani, T., Prasetyo, Y. A., & Nugroho, A. (2025). Development of an AI governance model for higher education institutions. Jurnal Teknologi Informasi dan Ilmu Komputer, 12(1), 1–15.
Westerman, G., Bonnet, D., & McAfee, A. (2022). Leading digital: Turning technology into business transformation. Harvard Business Review Press.
