سامانه مدیریت نشریات علمی دانشگاه شهید بهشتی

قائمی نیا, محمد مهدی, اسدیان فیلی, سپهر, امیری فرد, شیرین. (1404). هوش مصنوعی در بستر تحول دیجیتال: بازتاملی آسیب‌شناختی در درک مفاهیم و کاربردها. , 16(2), 80-103. doi: 10.48308/jpap.2025.240557.1487
محمد مهدی قائمی نیا; سپهر اسدیان فیلی; شیرین امیری فرد. "هوش مصنوعی در بستر تحول دیجیتال: بازتاملی آسیب‌شناختی در درک مفاهیم و کاربردها". , 16, 2, 1404, 80-103. doi: 10.48308/jpap.2025.240557.1487
قائمی نیا, محمد مهدی, اسدیان فیلی, سپهر, امیری فرد, شیرین. (1404). 'هوش مصنوعی در بستر تحول دیجیتال: بازتاملی آسیب‌شناختی در درک مفاهیم و کاربردها', , 16(2), pp. 80-103. doi: 10.48308/jpap.2025.240557.1487
قائمی نیا, محمد مهدی, اسدیان فیلی, سپهر, امیری فرد, شیرین. هوش مصنوعی در بستر تحول دیجیتال: بازتاملی آسیب‌شناختی در درک مفاهیم و کاربردها. , 1404; 16(2): 80-103. doi: 10.48308/jpap.2025.240557.1487

هوش مصنوعی در بستر تحول دیجیتال: بازتاملی آسیب‌شناختی در درک مفاهیم و کاربردها

چشم انداز مدیریت دولتی
دوره 16، شماره 2 - شماره پیاپی 62، تیر 1404، صفحه 80-103    اصل مقاله (768.59 K)
نوع مقاله: پژوهشی (با رویکردهای کیفی)
شناسه دیجیتال (DOI): 10.48308/jpap.2025.240557.1487
نویسندگان
محمد مهدی قائمی نیا* 1؛ سپهر اسدیان فیلی2؛ شیرین امیری فرد2
1دانشکده حکمرانی دانشگاه تهران
2دانشگاه آزاد اسلامی واحد تهران مرکز
چکیده
هدف: هدف اصلی مقاله، یافتن پاسخ دقیق و ارائه مبنایی متقن جهت درک هرچه عمیق‌تر مفاهیم و کاربردهای هوش‌مصنوعی در بستر تحول‌دیجیتال است که از طریق پاسخ به پرسش‌هایی در خصوص مفهوم هوشمندسازی و تفاوت آن با پیاده‌سازی هوش‌مصنوعی، نسبت اتوماسیون با این مقولات، مراحل کلی و تقدم و تاخرهای پیاده‌سازی مدل‌های هوشمند و در نهایت چیستی تحول‌دیجیتال از منظر نسبت با هوش و هوشمندسازی، حاصل خواهد گشت.
طراحی/ روش‌شناسی/ رویکرد: تحقیق این پژوهش، بر اساس روش «تحلیل کیفی محتوا» انجام شده که به عنوان چارچوبی برای تحلیل داده‌ها مورد بهره‌برداری قرار می‌گیرد. این روش در مقاله حاضر، با برخورداری از سازوکار نظام‌مند ولی انعطاف‌پذیر خود، با تاکید بر پی‌رنگ پدیدارشناسی‌های اجتماعی شکل گرفته، بر استخراج مضامین، الگوها و مفاهیم پنهان در داده‌های متنی متمرکز می‌باشد. این روش به طور کلی، بر آن است تا با نگاهی تفسیری به مقولات اجتماعی، به فهم زمینه و بافتار، نیات پنهان و ساختارهای معنایی درون اسناد بپردازد. داده‌های متنی این تحقیق، بر اساس بررسی مجموعه‌ای شامل بیش از 50 کتاب، مقاله، گزارش و متون علمی و تخصصی در رابطه با مقولۀ تحول‌دیجیتال، هوشمندسازی و هوش‌مصنوعی و درک مفاهیم و نوع کارکرد آنها و با بهره‌گیری تکمیلی از نرم‌افزار MAXQDA بدست آمده است.
یافته‌­های پژوهش: مقاله در بخش یافته‌های خود، نگاهی از دو زاویه را به پدیده تحول‌دیجیتال، حل ابهامات و بازتبیین مفاهیم، جایگاه‌ها، کارکردها و برخی ویژگی‌های اساسی آن، ارائه نموده است: ابتدا سیر تطور فنی و سپس سیر تطور صنعت. تطور نوع اول، با آغاز تدقیق در سیر تحول‌دیجیتال، از پیش زمینۀ آن در انقلاب صنعتی اول، یعنی «مکانیزاسیون»، به بازشناسی «اتوماسیون»، «هوش‌مصنوعی» و «هوشمندسازی» اختصاص یافته و خلاصه‌ای از مسیر رشد تاریخی و ویژگی ‌های هریک و نیز نقاط ظریف مربوط به برخی تفاوت‌های میان آنها را پیش روی نهاده و در واقع، بیشتر ناظر بر فهم ویژگی‌های فنی مراحل تحول‌دیجیتال به پیش می‌رود. اما تطور دوم، متمرکز بر تحلیل تغییرات فرایندها، نه از جنس رخدادهای فنی، بلکه از جنس تحولات مکانیزم‌ها و قابلیت‌های حاصل شده در کسب‌کارها و صنایع است که به تبیین موجز مراحلِ رقومی‌سازی تا تحول‌دیجیتال‌ می‌پردازد. این دو زوایه نگاه، به نحوی روشن، به صورت مکمل یکدیگر در ارائه تصویری صحیح نسبت به مقولات تعریف شده، عمل می‌نماید.
محدودیت­‌ها و پیامدها: یکی از محدودیت‌های حاکم بر پیشبرد این پژوهش، محدودیت زمانی با توجه به حجم ادبیات و دغدغه‌های مرتبط با مسئله هدف مقاله بود که طبعا مانع از انجام یک پژوهش همه‌جانبه و ارائه یک چارچوب ایده‌ال مفهومی می‌گشت. لذا اولویت‌بندی مفاهیم، از منظر پرداخت به مباحث بنیادین این حوزه، در دستور کار قرار گرفت و همان‌گونه که در بخش انتهایی مقاله نیز قابل مشاهده است، کاوش وسیع‌تر و امتداد پژوهش به لایه‌های پیچیده‌تر قلمرو بحث، به طور جدی برای تعریف گام‌های آتی، پیشنهاد گردیده است. محدودیت جدی بعدی، با توجه به ماهیت تحلیل کیفی پژوهش که مبتنی بر تحلیل داده‌های محتوایی انجام می‌گشت، دسترسی به متن کامل برخی مستندات و مقالات بود که در عمل، منجر به عدم بهره‌گیری مطلوب از جزئیات و لایه‌های تفصیلی برخی نظریات می‌گشت. با این حال تلاش پژوهش حاضر بر این بوده که از ظرفیت منابع موجود، استفاده حداکثری گشته و به موازات آن، ارتباط‌گیری با برخی از همکاران پژوهشگر در دانشگاه‌های مطرح جهان، جهت امکان دسترسی به برخی از منابع، در دستور کار قرار گیرد.
پیامدهای عملی: باور تیم پژوهشی مقاله بر این است که خروجی این تحقیق، می‌تواند منجر به ساختاربندی مجدد و نظم‌بخشی به لایه‌های ادراکی در خصوص برخی مفاهیم بنیادین مرتبط هوش‌مصنوعی و تحول‌دیجیتال، برای مخاطبان کشور، در هر سه سطح حکمرانی، دانشگاهی و صنعتی گردد. بدیهی است که این مهم، زمینه همگرایی بیشتر فکری و پی‌ریزی موثرتر برنامه‌های توسعه‌ای و ترویجی در قلمرو هدف را فراهم خواهد نمود.
ابتکار یا ارزش مقاله: تمرکز بر فهم عمیق‌تر، از رهاورد توجه به ساده‌سازی و نظم‌بخشی به تصویر سازوکار بسیار پیچیدۀ برخی مفاهیم بنیادین حوزه هوش‌مصنوعی و تحول‌دیجیتال را می‌توان به عنوان ابتکار این مقاله برشمرد.
نوع مقاله: مقاله پژوهشی
کلیدواژه‌ها
هوش مصنوعی؛ هوشمندسازی؛ اتوماسیون؛ رقومی سازی؛ دیجیتالی سازی؛ تحول دیجیتال
عنوان مقاله [English]
Artificial Intelligence in the Context of Digital Transformation: A Diagnostic Reappraisal of Concepts and Applications
نویسندگان [English]
Mohammad Mahdi Ghaemi Nia1؛ Sepehr Asadian Feili2؛ Shirin Amirifard2
1The Faculty of Governance, of Tehran University
2Islamic Azad University - The Central Tehran Branch
چکیده [English]
Purpose: The primary objective of this article is to provide precise answers and establish a robust foundation for a deeper understanding of the concepts and applications of artificial intelligence within the context of digital transformation. This is achieved by addressing questions regarding the concept of intelligentization and its distinction from the implementation of artificial intelligence, the relationship of automation with these concepts, the general stages and priorities in implementing intelligent models, and ultimately, the nature of digital transformation from the perspective of its relationship with intelligence and intelligentization.
Design/ methodology/ approach: The research in this study was conducted using the "qualitative content analysis" method, which serves as a framework for data analysis. This method, in the present article, with its systematic yet flexible mechanism, is shaped by an emphasis on social phenomenological narratives and focuses on extracting themes, patterns, and latent concepts from textual data. Generally, this method aims to interpret social phenomena to understand the context, hidden intentions, and semantic structures within documents. The textual data for this research were obtained by reviewing a collection of over 50 books, articles, reports, and specialized scientific texts related to digital transformation, intelligentization, and artificial intelligence, as well as their conceptual understanding and functionality, with additional use of the MAXQDA software.
Research Findings: The article, in its findings section, presents a dual perspective on the phenomenon of digital transformation, addressing ambiguities, redefining concepts, roles, functions, and some fundamental characteristics. First, it examines the technical evolution, starting with a detailed exploration of the digital transformation trajectory, from its precursor in the first industrial revolution, namely "mechanization," to the recognition of "automation," "artificial intelligence," and "intelligentization." It provides a summary of the historical growth trajectory, the characteristics of each, and the subtle differences between them, primarily focusing on understanding the technical features of the stages of digital transformation. The second perspective focuses on analyzing changes in processes, not from the perspective of technical events, but from the transformations in mechanisms and capabilities achieved in businesses and industries, succinctly explaining the stages from digitization to digital transformation. These two perspectives complement each other clearly, providing an accurate portrayal of the defined concepts.
Limitations & Consequences: One of the limitations affecting the progress of this research was the time constraint, given the volume of literature and concerns related to the article's objective, which naturally hindered conducting a comprehensive study and presenting an ideal conceptual framework. Therefore, prioritizing concepts from the perspective of addressing fundamental issues in this field was adopted, and as evident in the article's concluding section, broader exploration and extending the research to more complex layers of the discussion domain have been strongly recommended for defining future steps. Another significant limitation, given the qualitative nature of the research based on content data analysis, was the access to the full text of some documents and articles, which in practice led to suboptimal utilization of the details and in-depth layers of certain theories. Nevertheless, the current research endeavored to maximize the use of available resources, and concurrently, efforts were made to establish connections with research colleagues at leading global universities to access some of these resources.
Practical Consequences: The research team believes that the output of this study can lead to restructuring and organizing the perceptual layers regarding some fundamental concepts related to artificial intelligence and digital transformation for audiences in the country at all three levels of governance, academia, and industry. It is evident that this will foster greater intellectual convergence and lay a more effective foundation for developmental and promotional programs in the targeted domain.
Innovation or value of the Article: The focus on a deeper understanding, achieved through simplifying and organizing the highly complex mechanisms of some fundamental concepts in the field of artificial intelligence and digital transformation, can be considered the originality of this article.
Paper Type: Original Paper
 
کلیدواژه‌ها [English]
AI, Smartening, Automation, Digitization, Digitalisation, Digital Transformation
مراجع
  1.  Getting robots right: How to avoid the six most damaging mistakes in scaling-up robotic process automation. (2016). https://www.accenture.com/
  2. Agaton, B., & Swedberg, G. (2018). Evaluating and developing methods to assess business process suitability for robotic process automation [Unpublished manuscript].
  3. Agoston, D. V. (2024). Of artificial intelligence, machine learning, and the human brain: Celebrating Miklos Palkovits’ 90th birthday. Frontiers in Neuroanatomy, 18, Article 1374864. https://doi.org/10.3389/fnana.2024.1374864
  4. Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2024). AI-powered innovation in digital transformation: Key pillars and industry impact. Applied Sciences, 14(2), Article 809. https://doi.org/10.3390/app14020809
  5. Asquith, A., & Horsman, G. (2019). Let the robots do it! Taking a look at robotic process automation and its potential application in digital forensics. Forensic Science International: Reports, 1, Article 100007. https://doi.org/10.1016/j.fsir.2019.100007
  6. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv. https://arxiv.org/abs/1409.0473
  7. Boston Consulting Group. (2020). Flipping the odds of digital transformation success. https://web-assets.bcg.com/c7/20/907821344bbb8ade98cbe10fc2b8/bcg-flipping-the-odds-of-digital-transformation-success-oct-2020.pdf
  8. Bewersdorff, A., Zhai, X., Roberts, J., & Nerdel, C. (2023). Myths, mis- and preconceptions of artificial intelligence: A review of the literature. Computers and Education: Artificial Intelligence, 5, Article 100143. https://doi.org/10.1016/j.caeai.2023.100143
  9. Boulton, C. (2017). What is RPA? A revolution in business process automation. Computerworld Hong Kong. https://www.computerworld.com.hk/
  10. Bryman, A. (2012). Social research methods (4th ed.). Oxford University Press.
  11. Burnett, S., Aggarwal, M., Modi, A., & Bhadola, S. (2018). Defining enterprise RPA. Everest Group Research. https://www.everestgrp.com/
  12. Burstein, F. (2008). Foreword. In P. J. Phillips-Wren, N. Ichalkaranje, & L. C. Jain (Eds.), Intelligent decision making: An AI-based approach (pp. v–vii). Springer-Verlag.
  13. Cognitive View. (2025). 3-step roadmap for your responsible AI journey. https://blog.cognitiveview.com/3-step-roadmap-for-your-responsible-ai-journey/
  14. Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on Audio, Speech, and Language Processing, 20(1), 30–42. https://doi.org/10.1109/TASL.2011.2134090
  15. Davenport, T. H., & Bean, R. (2025). Five trends in AI and data science for 2025. MIT Sloan Management Review. https://sloanreview.mit.edu/
  16. Davenport, T. H., & Kirby, J. (2016). Just how smart are smart machines? MIT Sloan Management Review, 57(3), 21–25.
  17. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
  18. Decker, G., Kopp, O., Leymann, F., & Weske, M. (2007). BPEL4Chor: Extending BPEL for modeling choreographies. In Proceedings of the IEEE International Conference on Web Services (ICWS) (pp. 296–303). IEEE. https://doi.org/10.1109/ICWS.2007.68
  19. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 4171–4186). https://arxiv.org/abs/1810.04805
  20. Dey, A. (2016). Machine learning algorithms: A review. International Journal of Computer Science and Information Technologies, 7(3), 1174–1179.
  21. Dijkstra, E. W. (1976). A discipline of programming. Prentice-Hall.
  22. Durgess, A. (2018). Starting an AI journey. In The executive guide to artificial intelligence (pp. 1–20). Palgrave Macmillan. https://doi.org/10.1007/978-3-319-74557-2_1
  23. Emmert-Streib, F., Yli-Harja, O., & Dehmer, M. (2020). Artificial intelligence: A clarification of misconceptions, myths and desired status. Frontiers in Artificial Intelligence, 3, Article 524339. https://doi.org/10.3389/frai.2020.524339
  24. Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. arXiv. https://arxiv.org/abs/1703.03400
  25. (2018). The RPA market will reach $2.9 billion by 2021. https://www.forrester.com/report/The+RPA+Market+Will+Reach+29+Billion+By+2021/-/E-RES137229
  26. Galusha, B. (2018). Considering RPA? Database Trends and Applications, 31(6), 44–45.
  27. Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. In Proceedings of the 32nd International Conference on Machine Learning (pp. 1180–1189). http://proceedings.mlr.press/v37/ganin15.html
  28. (2022). Gartner webinar: Top strategic technology trends for 2022. https://www.gartner.com/en/webinar/729419/1637467
  29. Goldman, B. (2023). Experts weigh in: AI vs. the human brain. Stanford Medicine Magazine, (3). https://stanmed.stanford.edu/experts-weigh-ai-vs-human-brain
  30. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (pp. 2672–2680). https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
  31. Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv. https://arxiv.org/abs/1308.0850
  32. Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527
  33. Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82–97. https://doi.org/10.1109/MSP.2012.2205597
  34. Hinton, G., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. https://doi.org/10.1126/science.1127647
  35. Infopédia. (2020). Dicionário Infopédia da Língua Portuguesa. https://www.infopedia.pt/
  36. (2023). AI: Where the human brain goes for inspiration. https://www.infosys.com/insights/ai-automation/ai-where-human-brain-goes.html
  37. Izadyar, M. (2024). Review of the digital transformation path in Iranian industries (in Persian). Peyvast, (123).
  38. Jalonen, H. (2017). Assessing robotic process automation potential [Master’s thesis, Tampere University of Technology].
  39. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
  40. Kamaruddin, S. S., Mohammad, M. F., & Mahbub, R. (2016). Barriers and impact of mechanisation and automation in construction to achieve better quality products. Procedia - Social and Behavioral Sciences, 222, 111–120. https://doi.org/10.1016/j.sbspro.2016.05.197
  41. Kanagalabalu, R. (2022). AI vs ML vs DL. Medium. https://medium.com/@kanagalabalu04/ai-vs-ml-vs-dl-95653bafe35d
  42. Kitsios, F., & Kamariotou, M. (2021). Artificial intelligence and business strategy towards digital transformation: A research agenda. Sustainability, 13(4), Article 2025. https://doi.org/10.3390/su13042025
  43. Kosiorek, A., Sabour, S., Teh, Y. W., & Hinton, G. (2019). Stacked capsule autoencoders. In Advances in Neural Information Processing Systems (pp. 15512–15522). https://papers.nips.cc/paper/2019/file/3de568f8597b94bda53149c7d7f5958c-Paper.pdf
  44. Koushki, A., Hashemi, S. A., Saemi, M., & Bakhshi, F. S. (2021). Challenges and obstacles of digital transformation in organizations, and the role of digital leadership in overcoming barriers (in Persian). Civilica. https://civilica.com/doc/1486837/
  45. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
  46. Kristian, S. (2018). Robotic process automation of office work: Benefits, challenges and capability development [Master’s thesis, Aalto University].
  47. Kuckartz, U., & Rädiker, S. (2019). Analyzing qualitative data with MAXQDA. Springer. https://doi.org/10.1007/978-3-030-15671-8
  48. Kurzweil, R. (2024). The singularity is nearer: When we merge with AI. Penguin Publishing Group.
  49. Lacity, M., & Willcocks, L. (2016). A new approach to automating services. MIT Sloan Management Review, 58(1), 41–49.
  50. Lacity, M., & Willcocks, L. (2016). Robotic process automation at Telefónica O2. MIS Quarterly Executive, 15(1), 21–35.
  51. Lacity, M., Willcocks, L., & Yan, A. (2015). Are the robots really coming? Service automation survey findings. Pulse Magazine, 17, 14–21.
  52. Lamberton, C., Brigo, D., & Hoy, D. (2017). Impact of robotics, RPA and AI on the insurance industry: Challenges and opportunities. Journal of Financial Perspectives, 4(1), 1–16.
  53. Larson, E. J. (2021). The myth of artificial intelligence. Harvard University Press.
  54. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  55. Lintukangas, A. (2017). Improving indirect procurement process by utilizing robotic process automation [Master’s thesis, Lappeenranta University of Technology].
  56. Manna, Z., & Waldinger, R. (1980). A deductive approach to program synthesis. ACM Transactions on Programming Languages and Systems, 2(1), 90–121. https://doi.org/10.1145/357094.357098
  57. Mazur, K. E., Barwicki, J., & Tseiko, V. (2024). Comparison of mechanized and automated technologies in the scope of cumulative energy in sustainable milk production. Sustainability, 16(2), Article 906. https://doi.org/10.3390/su16020906
  58. McKinsey & Company. (2019). Why do most transformations fail? A conversation with Harry Robinson. https://www.mckinsey.com/~/media/mckinsey/business%20functions/transformation/our%20insights/why%20do%20most%20transformations%20fail%20a%20conversation%20with%20harry%20robinson/why-do-most-transformations-fail-a-conversation-with-harry-robinson.pdf
  59. McKinsey & Company. (2025). The state of AI: How organizations are rewiring to capture value. https://www.mckinsey.com/
  60. Meyer, A. (2015). Data management in automation systems. Journal of Automation Technology, 12(3), 1–15.
  61. 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), Article 103434. https://doi.org/10.1016/j.im.2021.103434
  62. Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Farrar, Straus and Giroux.
  63. Mitchell, T. M. (2013). Machine learning. McGraw-Hill Education.
  64. Morgan, K. E. (1965). Automation in agriculture: Is the time ripe for widespread automation as a sequel to mechanization? Journal of the British Institute of Agricultural Engineers, 20(4), 251–259. https://doi.org/10.1177/003072706500400605
  65. Nasscom, R. (2025). Digital enterprise 2025: Advancing to an AI-first reality. https://www.nasscom.in/
  66. (2007). Web Services Business Process Execution Language, Version 2.0. https://docs.oasis-open.org/wsbpel/2.0/wsbpel-v2.0.html
  67. Object Management Group. (2011). Unified Modeling Language (UML), Version 2.4.1. https://www.omg.org/spec/UML/2.4.1
  68. (2022). Digital transformation maturity model. Forum on Tax Administration: Tax Administration Maturity Model Series. OECD Publishing. https://www.oecd.org/
  69. OED Online. (2016). Digitization, n. In Oxford English Dictionary. Oxford University Press. https://www.oed.com/
  70. Phillips-Wren, G., Ichalkaranje, N., & Jain, L. C. (2009). An integrative evaluation framework for intelligent decision support systems. European Journal of Operational Research, 195(3), 642–652. https://doi.org/10.1016/j.ejor.2007.11.001
  71. Posadas, B. C. (2012). Economic impacts of mechanization or automation on horticulture production firms: Sales, employment, and workers’ earnings, safety, and retention. HortTechnology, 22(3), 388–401. https://doi.org/10.21273/HORTTECH.22.3.388
  72. Prov International. (n.d.). Top 5 benefits of robotics process automation (RPA) software. https://www.provintl.com/blog/top-5-benefits-of-robotics-process-automation-rpa-software
  73. Rashid, A. B., & Kausik, M. A. K. (2024). AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. Hybrid Advances, 7, Article 100277. https://doi.org/10.1016/j.hybadv.2024.100277
  74. Reinitz, B. (2020). Consider the three Ds when talking about digital transformation. EDUCAUSE Review. https://er.educause.edu/blogs/2020/6/consider-thethree-ds-when-talking-about-digital-transformation
  75. Reynolds, B. (2025). The state of artificial intelligence in 2025. BayTech Consulting. https://www.baytechconsulting.com/
  76. Rogers, D. L. (2016). The digital transformation playbook: Rethink your business for the digital age. Columbia Business School Publishing.
  77. Rogers, D. L. (2023). The digital transformation roadmap: Rebuild your organization for continuous change. Columbia Business School Publishing.
  78. Ruissalo, J. (2018). Human–computer interaction and role allocation in information intensive work processes: Robotic process automation in financial administration work [Master’s thesis, Aalto University].
  79. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. https://doi.org/10.1038/323533a0
  80. Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
  81. Saldanha, T. (2019). Why digital transformations fail: The surprising disciplines of how to take off and stay ahead. Berrett-Koehler Publishers.
  82. Sandhya, N. D., & Raina, C. K. (2016). A review on machine learning techniques. International Journal of Recent Trends in Computing and Communication, 4(3), 395–399.
  83. Sarikaya, F. (2024). The cycles of AI winters: A historical analysis and modern perspective. Zenodo. https://doi.org/10.5281/zenodo.14015032
  84. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
  85. Schwab, K. (2017). The fourth industrial revolution. Crown Business.
  86. Smith, D. R., & Lowry, M. R. (1990). Algorithm theories and design tactics. Science of Computer Programming, 14(2–3), 305–321. https://doi.org/10.1016/0167-6423(90)90023-3
  87. Sobotka, A., & Pacewicz, K. (2017). Mechanisation and automation technologies development in work at construction sites. IOP Conference Series: Materials Science and Engineering, 251(1), Article 012046. https://doi.org/10.1088/1757-899X/251/1/012046
  88. Tarquini, T. (2018). Practical robotics in insurance – The future is here already. In The InsurTech book (pp. 231–235). Wiley.
  89. The Institute for Robotic Process Automation. (2015). Introduction to robotic process automation: A primer. https://www.irpaai.com/
  90. Tiechert, R. (2019). Digital transformation in agriculture: Opportunities and challenges. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(6), 1673–1687. https://doi.org/10.11118/actaun201967061673
  91. Toosi, A., Bottino, A., Saboury, B., Siegel, E., & Rahmim, A. (2021). The cycles of AI winters: A historical analysis and modern perspective. PET Clinics, 16(4), 449–469. https://doi.org/10.1016/j.cpet.2021.06.005
  92. (2020). Artificial intelligence RPA capabilities. https://www.uipath.com/
  93. (2024). Artificial intelligence readiness assessment (AIRA). https://www.undp.org/
  94. Vanmali, K. (2017). Robotic software will help CFOs to play a more strategic role. Accountancy SA, 16–17.
  95. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998–6008). https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
  96. Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
  97. Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
  98. (2001). Web Services Description Language (WSDL) 1.1. https://www.w3.org/TR/2001/NOTE-wsdl-20010315
  99. (2010). XQuery 1.0: An XML query language (2nd ed.). https://www.w3.org/TR/2010/REC-xquery-20101214/
  100. (2022). Why have we fallen behind the global digital transformation process? (in Persian). https://way2pay.ir/303233/
  101. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.
  102. Zare, M. (2019). Why digital transformation plans fail? (in Persian). https://www.zoomit.ir/business/341879-big-reasons-digital-transformations-fail/
  103. Zhang, D., & Lu, G. (2020). The future of artificial intelligence (AI) and machine learning (ML) in engineering. Engineering, 6(3), 212–214. https://doi.org/10.1016/j.eng.2019.11.012
  104. Zhang, Y., Chen, Y., & Wang, J. (2010). Outlier detection techniques for wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 12(2), 159–170. https://doi.org/10.1109/SURV.2010.021510.00088
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