VERİYE DAYALI İŞ SÜREÇLERİNDE BAŞARIYI ETKİLEYEN FAKTÖRLERİN KAVRAMSAL ANALİZİ: YAPAY ZEKÂ TABANLI BİR MODEL ÖNERİSİ
DOI:
https://doi.org/10.5281/zenodo.17119495Anahtar Kelimeler:
Yapay Zekâ, İş Süreçleri Yönetimi, Kritik Başarı Faktörleri, Veriye Dayalı Karar Verme, Dijital DönüşümÖzet
Son yıllarda, yapay zekânın (YZ) iş süreçleri yönetimine (BPM) entegrasyonu, özellikle veriye dayalı organizasyonel yapılarda önemli bir ivme kazanmıştır. Bu çalışma, organizasyonel kritik başarı faktörlerini (KBF) yapay zekâ destekli süreç yönetimi bileşenleriyle bütünleştiren, yöntem temelli kavramsal bir model önermektedir. Model; üst yönetim desteği, veri yönetişimi, veri kalitesi, veri okuryazarlığı ve teknik altyapı gibi giriş bileşenlerini içerecek şekilde tasarlanmıştır. Ayrıca makine öğrenmesi, tahmine dayalı analiz, süreç otomasyonu ve öneri sistemlerinden oluşan YZ tabanlı bir süreç mekanizması da modele dâhil edilmiştir. Bu yapı, karar kalitesi, operasyonel verimlilik, süreç başarısı ve çeviklik gibi çıktı değişkenlerini üretmek üzere kurgulanmıştır. Geri beslemeli ve uyarlanabilir bir sistem mantığını benimseyen model, öğrenmeye dayalı süreç döngülerinin geliştirilmesini amaçlamaktadır. Modelin bileşenleri, son beş yılı kapsayan kapsamlı bir literatür taramasıyla yapılandırılmış ve bilişsel BPM modelleri ile YZ destekli süreç çerçeveleriyle karşılaştırmalı olarak değerlendirilmiştir. Bulgular, YZ teknolojilerinin organizasyonel hazırlık faktörleriyle uyumlu olarak konumlandırılmasının, stratejik karar kalitesini ve dijital dönüşüm başarısını artırabileceğini göstermektedir. Bu kavramsal çerçeve, hem akademik bir katkı sunmakta hem de YZ destekli süreçleri uygulamak isteyen kurumlar için pratik bir yol haritası niteliği taşımaktadır.
Referanslar
Anica-Popa, L.-E., Vrîncianu, M., & Petrică Papuc, I.-M. (2023). AI – powered Business Services in the Hyperautomation Era. Proceedings of the International Conference on Business Excellence, 17(1), 1036-1050. https://doi.org/10.2478/picbe-2023-0094
Antonucci, Y. L., Fortune, A., & Kirchmer, M. (2020). An examination of associations between business process management capabilities and the benefits of digitalization: all capabilities are not equal. Business Process Management Journal, 27(1), 124-144. https://doi.org/10.1108/bpmj-02-2020-0079
Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technological Forecasting and Social Change, 168. https://doi.org/10.1016/j.techfore.2021.120766
Biernikowicz, A., Gabryelczyk, R., & Ashraf, R. U. (2025). Investigating the nexus between business process management maturity and digital maturity: an empirical study of organizations in Poland. Business Process Management Journal. https://doi.org/10.1108/bpmj-07-2024-0666
Broccardo, L., Vola, P., Alshibani, S. M., & Tiscini, R. (2023). Business processes management as a tool to enhance intellectual capital in the digitalization era: the new challenges to face. Journal of Intellectual Capital, 25(1), 60-91. https://doi.org/10.1108/jic-04-2023-0070
Butt, J. (2020). A Conceptual Framework to Support Digital Transformation in Manufacturing Using an Integrated Business Process Management Approach. Designs, 4(3). https://doi.org/10.3390/designs4030017
Chaudhuri, R., Chatterjee, S., Vrontis, D., & Thrassou, A. (2021). Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture. Annals of Operations Research, 339(3), 1757-1791. https://doi.org/10.1007/s10479-021-04407-3
Daradkeh, M. K. (2021). An Empirical Examination of the Relationship Between Data Storytelling Competency and Business Performance. Journal of Organizational and End User Computing, 33(5), 42-73. https://doi.org/10.4018/JOEUC.20210901.oa3
Decker, S. (2019). Data-driven business process improvement: An illustrative case study about the impacts and success factors of business process mining. In.
Distel, B., Plattfaut, R., & Kregel, I. (2023). How business process management culture supports digital innovation: a quantitative assessment. Business Process Management Journal, 29(5), 1352-1385. https://doi.org/10.1108/bpmj-12-2022-0637
Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), 673-686. https://doi.org/10.1016/j.ejor.2018.06.021
Dumas, M., Fournier, F., Limonad, L., Marrella, A., Montali, M., Rehse, J.-R., Accorsi, R., Calvanese, D., De Giacomo, G., & Fahland, D. (2023). AI-augmented business process management systems: a research manifesto. ACM Transactions on Management Information Systems, 14(1), 1-19.
Enriquez, J. G., Jimenez-Ramirez, A., Dominguez-Mayo, F. J., & Garcia-Garcia, J. A. (2020). Robotic Process Automation: A Scientific and Industrial Systematic Mapping Study. IEEE Access, 8, 39113-39129. https://doi.org/10.1109/access.2020.2974934
Eyitayo, R., Tochukwu Ignatius, I., & Osemeike Gloria, E. (2024). Data-Driven decision making in agriculture and business: The role of advanced analytics. Computer Science & IT Research Journal, 5(7), 1565-1575. https://doi.org/10.51594/csitrj.v5i7.1275
Gînguță, A., Ștefea, P., Noja, G. G., & Munteanu, V. P. (2023). Ethical Impacts, Risks and Challenges of Artificial Intelligence Technologies in Business Consulting: A New Modelling Approach Based on Structural Equations. Electronics, 12(6). https://doi.org/10.3390/electronics12061462
Hadysah, L. A., & Pratama, N. R. Digital Capability Maturity Improvement Strategy Design: A Case Study of a Mobile Phone Manufacturing Company.
Hildebrand, D., Rösl, S., Auer, T., & Schieder, C. (2024). Next-generation business process management (BPM): a systematic literature review of cognitive computing and improvements in BPM. International Conference on Subject-Oriented Business Process Management,
Hussinki, H. (2022). Business analytics and firm performance: A literature review. European Conference on Knowledge Management,
Kalluri, K. (2023). Artificial Intelligence in BPM: Enhancing Process Optimization Through Low-Code Development. International Journal for Multidisciplinary Research, 5(6), 23396.
Kampik, T., Warmuth, C., Rebmann, A., Agam, R., Egger, L. N. P., Gerber, A., Hoffart, J., Kolk, J., Herzig, P., Decker, G., van der Aa, H., Polyvyanyy, A., Rinderle-Ma, S., Weber, I., & Weidlich, M. (2024). Large Process Models: A Vision for Business Process Management in the Age of Generative AI. KI - Künstliche Intelligenz. https://doi.org/10.1007/s13218-024-00863-8
Kerpedzhiev, G. D., König, U. M., Röglinger, M., & Rosemann, M. (2020). An Exploration into Future Business Process Management Capabilities in View of Digitalization. Business & Information Systems Engineering, 63(2), 83-96. https://doi.org/10.1007/s12599-020-00637-0
Kerpedzhiev, G. D., König, U. M., Röglinger, M., & Rosemann, M. (2021). An exploration into future business process management capabilities in view of digitalization: results from a Delphi study. Business & Information Systems Engineering, 63(2), 83-96.
Labus, M., Despotovic-Zrakic, M., Bogdanovic, Z., Barac, D., & Popovic, S. (2020). Adaptive e-business continuity management: Evidence from the financial sector. Computer Science and Information Systems, 17(2), 553-580. https://doi.org/10.2298/csis190202037l
Lu, J., Cairns, L., & Smith, L. (2020). Data science in the business environment: customer analytics case studies in SMEs. Journal of Modelling in Management, 16(2), 689-713. https://doi.org/10.1108/jm2-11-2019-0274
Mahmoud, A. B., Tehseen, S., & Fuxman, L. (2020). The dark side of artificial intelligence in retail innovation. In Retail futures (pp. 165-180). Emerald Publishing Limited.
Odionu, C. S., Bristol-Alagbariya, B., & Okon, R. (2024). Big data analytics for customer relationship management: Enhancing engagement and retention strategies. International Journal of Scholarly Research in Science and Technology, 5(2), 050-067.
Oluwatoyin Ajoke, F., Adekunle Abiola, A., Blessing Otohan, I., & Evelyn Chinedu, O. (2023). Innovative Business Models Driven by Ai Technologies: A Review. Computer Science & IT Research Journal, 4(2), 85-110. https://doi.org/10.51594/csitrj.v4i2.608
P. P. Sari, A. W., & P.D. Dirgantari,. (2025). A Systematic Literature Review of Business Process Management in SMEs: Key Benefi ts and Challenges, . JMM17: Jurnal Ilmu Ekonomi dan Manajemen, 12, 55-65.
Pikkarainen, M., Huhtala, T., Kemppainen, L., & Häikiö, J. (2020). Success factors for data–driven service delivery networks. Journal of Innovation Management, 7(4), 14-46. https://doi.org/10.24840/2183-0606_007.004_0003
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51-59. https://doi.org/10.1089/big.2013.1508
Rafati, L., & Poels, G. (2014). Capability sourcing modeling: a high-level conceptualization based on service-dominant logic. International Conference on Advanced Information Systems Engineering,
Rahman, A. (2024). Ai and Machine Learning in Business Process Automation: Innovating Ways Ai Can Enhance Operational Efficiencies or Customer Experiences in U.S. Enterprises. Non human journal, 1(01), 41-62. https://doi.org/10.70008/jmldeds.v1i01.41
Rana, M. M. (2024). Industry 4.0 and Fayol’s 14 Principles of Functional Management: Relevances, Emerging Practices and Consequences. Asian Journal of Economics, Business and Accounting, 24(7), 82-96. https://doi.org/10.9734/ajeba/2024/v24i71393
Sadykova, E. (2020). Transformation to a data-driven company: success factors Dublin Business School].
Saleem, H., Li, Y., Ali, Z., Ayyoub, M., Wang, Y., & Mehreen, A. (2020). Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation. Journal of Enterprise Information Management, 34(4), 1121-1143. https://doi.org/10.1108/jeim-03-2020-0119
Saravia-Vergara, E., Sanchís-Pedregosa, C., & Albort-Morant, G. (2020). Organizational Culture, Process Management and Maturity of the Process: An Empirical Study of the Process Status in Peru. Global Business Review, 24(2), 258-280. https://doi.org/10.1177/0972150920916036
Sleep, S., Hulland, J., & Gooner, R. A. (2019). THE DATA HIERARCHY: factors influencing the adoption and implementation of data-driven decision making. AMS Review, 9(3-4), 230-248. https://doi.org/10.1007/s13162-019-00146-8
Storm, M., & Borgman, H. (2020). Understanding challenges and success factors in creating a data-driven culture.
Szelągowski, M., & Lupeikiene, A. (2020). Business Process Management Systems: Evolution and Development Trends. Informatica, 579-595. https://doi.org/10.15388/20-infor429
Turner, T., Martinez, V., & Bititci, U. (2004). Managing the value delivery process. International Journal of Physical Distribution & Logistics Management, 34(3/4), 302-318. https://doi.org/10.1108/09600030410533600
Weinzierl, S., Zilker, S., Dunzer, S., & Matzner, M. (2024). Machine learning in business process management: A systematic literature review. Expert Systems with Applications, 253. https://doi.org/10.1016/j.eswa.2024.124181
Zebec, A., & Indihar Štemberger, M. (2024). Creating AI business value through BPM capabilities. Business Process Management Journal, 30(8), 1-26. https://doi.org/10.1108/bpmj-07-2023-0566
İndir
Yayınlanmış
Nasıl Atıf Yapılır
Sayı
Bölüm
Lisans
Telif Hakkı (c) 2025 Salih Serkan KALELİ

Bu çalışma Creative Commons Attribution 4.0 International License ile lisanslanmıştır.