ANALYSIS AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE MANAGEMENT SYSTEMS: ISO/IEC 42001

Abstract

Artificial intelligence, defined as the activity of reproducing human cognitive processes such as learning, reasoning, understanding, decision-making, and problem-solving through numerical and algorithmic models, has become a frequently discussed concept in our daily lives in recent years. This research analyzes the transformative role of artificial intelligence in different sectors and the ISO/IEC 42001:2023 Artificial Intelligence (AI) management system for the responsible management of this technology. Primarily, the benefits and drawbacks of AI, the use of AI in various countries, and the types of AI, namely Narrow Artificial Intelligence (NAI) and Generative Artificial Intelligence (Gen-AI), are discussed. NAI refers to artificial intelligence systems designed for a specific purpose, focused on limited task sets, and capable of operating only within the operational scope for which they were trained. Gen-AI refers to advanced systems that possess the capacity to make human-like inferences, generate extensive knowledge, and analyze and reconstruct context. Furthermore, studies in literature regarding the use of generative AI in education, human resources management, and healthcare are examined. The ethical dimension and aspects of AI, as well as critical elements for effective governance, are highlighted. ISO 42001 is an ideal framework for the dynamic structure of Industry and Technology because it offers an auditable and sustainable governance mechanism designed around the Plan-Do-Check-Act cycle. Here, the concept of governance, unlike traditional management approaches, centers on multi-stakeholder interaction, accountability, ethical considerations, transparent decision-making mechanisms, and shared responsibility. It has been determined that ISO/IEC 42001 guides organizations in the planning, implementation, maintenance, and continuous improvement processes of artificial intelligence management systems, and that it forms the basis of reliable and sustainable artificial intelligence applications thanks to human-centered governance, data security, ethical principles, and a risk-based approach. The research summarizes recent studies in the literature on ISO/IEC 42001, Artificial Intelligence Management Systems, and general AI applications. The importance of implementing the ISO/IEC 42001 standard and global cooperation for the safe, ethical, and sustainable development of the artificial intelligence ecosystem is emphasized, and recommendations are made. Artificial intelligence is not just a technical puzzle for humanity, but also a moral and social choice facing mankind. Conscious, courageous, and inclusive steps taken by societies will ensure that this technology enhances human well-being and guarantees that we can live together in a just world.

Keywords

• ISO/IEC 42001
• Artificial intelligence management system
• Narrow artificial intelligence
• Generative artificial intelligence
• Quality management
• Risk management.

YAPAY ZEKÂ YÖNETİM SİSTEMLERİNİN ANALİZİ VE UYGULAMALARI: ISO/IEC 42001

Abstract

İnsan bilişsel süreçlerinden olan; öğrenme, akıl yürütme, anlamlandırma, karar verme ve problem çözme gibi yetkinliklerinin, sayısal ve algoritmik modeller aracılığıyla yeniden üretme faaliyeti olarak tanımlanan Yapay zekâ, son yıllara gündelik yaşantımızsa sıkça konuştuğumuz bir kavram haline gelmiştir. Araştırmada, Yapay zekânın farklı sektörlerdeki dönüştürücü rolü ve bu teknolojinin sorumlu bir şekilde yönetilmesi için, ISO/IEC 42001:2023 Yapay Zekâ (YZ) yönetim sistemi analiz edilmiştir. Öncelikli olarak, yapay zekânın, faydaları, zararları, dünya ülkelerinin yapay zekâ kullanımları ve yapay zekâ türleri olan Dar Yapay Zekâ (DYZ) ve Üretken Yapay Zekâ (ÜYZ) ele alınmıştır. Ayrıca üretken yapay zekânın; eğitimde, insan kaynakları yönetiminde ve sağlıkta kullanımı ile ilgili literatürde yapılan çalışmalar incelenmiştir. YZ’nin ahlaki boyutu ve etiği ile etkili yönetişim için kritik unsurlar vurgulanmıştır. ISO/IEC 42001’in kuruluşlara yapay zekâ yönetim sistemlerini planlama, uygulama, sürdürme ve sürekli iyileştirme süreçlerinde rehberlik ettiği, insan merkezli yönetişim, veri güvenliği, etik ilkeler ve risk temelli yaklaşım sayesinde güvenilir ve sürdürülebilir yapay zekâ uygulamalarının temelini oluşturduğu tespit edilmiştir. Araştırmada, ISO/IEC 42001, Yapay Zekâ Yönetim Sistemleri ve YZ’nin genel uygulamaları ile ilgili son yıllarda literatürde yapılan çalışmalar özetlenmiştir. Yapay zekâ ekosisteminin güvenli, etik ve sürdürülebilir gelişimi için ISO/IEC 42001 standardının uygulanması ve küresel iş birliğinin önemi vurgulanarak önerilerde bulunulmuştur.

Keywords

• ISO/IEC 42001
• Yapay zekâ yönetim sistemi
• Dar yapay zekâ
• Üretken yapay zekâ
• Kalite yönetimi
• Risk yönetimi.

References

1. Aguinis, H., Beltran, J. R., & Cope, A. (2024). How to use generative AI as a human resource management assistant. Organizational Dynamics, 53, 101029. https://doi.org/10.1016/j.orgdyn.2024.101029
2. Ardichvili, A., Dirani, K., Jabarkhail, S., El Mansour, W., & Aboulhosn, S. (2024). Using generative AI in human resource development: An applied research study. Human Resource Development International, 27(3), 388–409. https://doi.org/10.1080/13678868.2024.2337964
3. Aseri, V. (2023). The symbiotic relationship between AI and internet governance in India. CyberPeace. https://www.cyberpeace.org/resources/blogs/the-symbiotic-relationship-between- AI -and-internet-governance-in-india
4. Attia, A. A., Kazi, M., Caroline, A., Moktali, V., & Hamm, E. (2025). Implementing America’s AI Action Plan in Healthcare: AYZH Strategic White Paper. American Association for the Advancement of Artificial Intelligence in Healthcare. https://www.theaYZh.org/publications/implementing-americas-YZ-action-plan-in-healthcare-aYZh-strategic-white-paper
5. Aydoğmuş, U.; Engin, O. (2021). Endüstri 4.0 Sürecinde Ağırlama Sektörüne Yönelik Uygulamaların İncelemesi. İstanbul Aydın Üniversitesi Sosyal Bilimler Dergisi, 13(3), 851-874. DOI: 10.17932/IAU.IAUSBD.2021.021/iausbd_v13i3013
6. Baig, M. M., Hobson, C., GholamHosseini, H., Ullah, E., & Afifi, S. (2024). Generative AI in Improving Personalized Patient Care Plans: Opportunities and Barriers Towards Its Wider Adoption. Applied Sciences,14(23), 10899. https://doi.org/10.3390/app142310899
7. Biroğul, S., Şahin, Ö., & ƏSGƏRLİ, H. (2025). Exploring the impact of ISO/IEC 42001:2023 AI management standard on organizational practices. Advances in Artificial Intelligence Research, 5(1), 14–22. https://doi.org/10.54569/aYZr.1709628
8. Bixler, K., & Ceballos, M. (2025). Principals leading AI in schools for instructional leadership: A conceptual model for principal AI use. Leadership and Policy in Schools, 24(1), 137–154. https://doi.org/10.1080/15700763.2024.2411295

9. Castonguay, A., Wagner, G., Motulsky, A., & Paré, G. (2024). AI maturity in health care: An overview of 10 OECD countries. Health Policy, 140, 104938. https://doi.org/10.1016/j.healthpol.2023.104938
10. Costa Mazzinghy, A. O., Silva, R.M.d.S.e.; Fernandes, R.M.; Batista, E.D.; Picanço, A.R.S.; Monteiro, N.J.; de Amorim, D.M.; Cardoso, B.d.F.O.; Silva, J.M.N.d.; Martins, V.W.B (2025). Assessment of the benefits of the ISO/IEC 42001 AI management system: Insights from selected Brazilian logistics experts. Standards, 5(2), 10. https://doi.org/10.3390/standards5020010
11. 11.DeSantis, N., Wade, T., Supples, C., & Phillips, L. (2025). Leveraging AI for enhanced alignment of national biodiversity targets with global biodiversity goals. Nature-Based Solutions, 7, 100198. https://doi.org/10.1016/j.nbsj.2024.100198
12. Engin, O., Yazıcı, K. M., Yaşa, E., Çakırköy, F. (2025). Üretimde dijital ikizlerin kullanımı: ISO 23247 standardı. Bartın University International Journal of Natural and Applied Sciences, 8(1), 157-177. https://doi.org/10.55930/jonas.1626558
13. Ferrari, F., van Dijck, J., & van den Bosch, A. (2025). Observe, inspect, modify: Three conditions for generative AI governance. New Media & Society, 27(5), 2788–2806. https://doi.org/10.1177/14614448231214811
14. Foffano, F., Scantamburlo, T., & Cortés, A. (2022). Investing in AI for social good: An analysis of European national strategies. AI & Society, 37(4), 1353–1368. https://doi.org/10.1007/s00146-022-01445-8
15. Harris, E., Harris, J., & Beall, M. (2024). Defense in depth: An action plan to increase the safety and security of advanced AI. Gladstone Institute. https://assets-global.website-files.com/62c4cf7322be8ea59c904399/65e7779f72417554f7958260_Gladstone%20Action%20Plan%20Executive%20Summary.pdf
16. ISO/IEC (2023). ISO/IEC 42001:2023 Information technology — Artificial intelligence — Management system. International Organization for Standardization.
17. ISO/IEC 22989 (2022). ISO/IEC 22989:2022Information technology — Artificial intelligence — Artificial intelligence concepts and terminology
18. Kativha, P., Lin, E. Y. T., & Vogel, S. (2024). Global regulatory frameworks for the use of artificial intelligence in the healthcare services sector. Healthcare, 12(5), 562. https://doi.org/10.3390/healthcare12050562
19. KPMG AG. (2025, May). ISO/IEC 42001 certification: The global standard for AI management systems (AIMS): How to establish trust, transparency and control in artificial intelligence governance. KPMG Switzerland. https://assets.kpmg.com/content/dam/kpmgsites/ch/pdf/ISOIEC-42001-certification.pdf
20. Khazieva, N., Paulikova, A., & Chovanova, H. H. (2024). Maximising synergy: The benefits of joint implementation of knowledge management and AI system standards. Machine Learning and Knowledge Extraction, 6(4), 2282–2302. https://doi.org/10.3390/make6040112
21. Lim, S. S., Chng, G. (2024). Verifying AI: Will Singapore’s experiment with AI governance set the benchmark? Communication Research & Practice, 1–10.
22. Manzak, R., Engin, O. (2023). Akıllı fabrikalarda çizelgeleme yöntemlerinin analizi, Verimlilik Dergisi, 57(4), 761-774. https://doi.org/10.51551/verimlilik.1136778
23. McDonald, F. J. (2022). AI, alignment and the categorical imperative. AI and Ethics, 3(1), 337–344. https://doi.org/10.1177/14614448231214811
24. Mohamed, E. A. E. (2025). The effects of implementing ISO 42001 AI management systems into autonomous maritime operations. Maritime Research and Technology, 4(1), 1253. http://dx.doi.org/10.21622/MRT.2025.04.1.1253
25. Oviedo, J., Rodriguez, M., Trenta, A., Cannas, D., Natale, D., & Piattini, M. (2024). ISO/IEC quality standards for AI engineering. Computer Science Review, 54, 100681. https://doi.org/10.1016/j.cosrev.2024.100681
26. Palaniappan, N., Mateša, B., & Mladinić, A. (2025). Structuring AI risk management framework: EU YZ Act FRIA, GDPR DPIA and ISO 42001/23894.In Proceedings of the IEEE MECO 2025 Conference. IEEE.https://doi.org/10.1109/MECO66322.2025.11049196
27. Parlov, N., Mateša, B., & Mladinić, A. (2025). Structuring AI risk management framework: EU AI Act FRIA, GDPR DPIA and ISO 42001/23894.In Proceedings of the IEEE MECO 2025 Conference. IEEE.https://doi.org/10.1109/MECO66322.2025.11049196
28. Productivity Commission. (2024). Making the most of the AI opportunity: The challenges of regulating AI. Research, https://www.pc.gov.au/research/completed
29. Robert, J., & Muscanell, N. (2023). EDUCAUSE Horizon Action Plan: Generative AI. EDUCAUSE.https://doi.org/10.13140/RG.2.2.10626.27840
30. Simonetta, A., & Paoletti, M. C. (2024). ISO/IEC standards and design of an artificial intelligence system. In Proceedings of the UNINFO UNI TC 533 Technical Committee Artificial Intelligence (CEUR Workshop Proceedings, Vol. 3916). CEUR-WS.org. https://ceur-ws.org/Vol-3916/paper_07.pdf
31. The White House. (2025). America’s AI action plan. Executive Office of the President of the United States. https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-YZ-Action-Plan.pdf
32. Thiers, F. A., & Harned, Z. (2024). The emerging role of ISO 42001 certification in fostering the deployment of responsible generative AI healthcare solutions. OSF Preprints. https://doi.org/10.31219/osf.io/5ks37
33. Thiers, F. A., & Lucy, K. (2024). A distinct approach to clinical Gen AI oversight. Conver Gen AI & Microsoft. https://doi.org/10.31219/osf.io/vm6zy
34. Trenta, A. (2024). AI evaluation and ISO standards: Is the gap closed? In Proceedings of the UNINFO UNI TC 533 Technical Committee Artificial Intelligence (pp. 1–10). CEUR-WS.org. https://ceur-ws.org/Vol-3916/paper_04.pdf
35. Xu, Q., (2024). Action research plan: A methodology to examine the impact of AI on the cognitive abilities of university students. AI and Education, 4, Article 330. https://doi.org/10.1007/s44217-024-00330-4
36. Xu, J., Lee, T., & Goggin, G. (2024). AI governance in Asia: Policies, praxis and approaches. Asian Journal of Communication. https://doi.org/10.1080/22041451.2024.2391204