ISSN 1789-8919
e-ISSN 2786-1562
Over the course of several decades, the incorporation of artificial intelligence (AI) into organizational management has been a transformative voyage, characterized by significant milestones and innovations. The field of artificial intelligence went under a rapid evolution in the 1980s with the introduction of machine learning algorithms and neural networks, which allowed systems to learn from data.
This development was preceded by early AI research that concentrated on symbolic reasoning and problem-solving. In the 1990s, AI applications acquired prominence in business sectors as a result of data mining techniques and expert systems.
The 2000s were characterized by the rapid advancement of AI technologies, such as computer vision and speech recognition, which expanded their commercial applications. In recent years, organizations have been able to resolve complex challenges and spur innovation by further accelerating the adoption of deep learning and reinforcement learning.
This paper investigates the fundamental transformation of management practices by AI advancements, which have improved operational efficiency, facilitated innovation, and improved decision-making. AI has revolutionized conventional management methodologies by automating routine duties and offering predictive insights.
Organizations are now capable of anticipating market trends, optimizing resource allocation, and mitigating risks through the incorporation of predictive analytics and decision support systems. AI-driven tools have fostered an innovative culture by improving consumer interactions, workforce planning, and collaboration.
The function of AI in organizational management is anticipated to expand further as it continues to evolve, thereby fostering new paradigms in strategic planning and organizational efficiency.
Abdi, H.,Valentin, D., & Edelman, B. (1999). Neural networks (No. 124). Statistics in Medicine, 19(16). https://doi.org/10.1002/1097-0258(20000830)19:16<2199::AID-SIM475>3.0.CO;2-C
Abramson, N., Braverman, D., & Sebestyen, G. (1963). Pattern recognition and machine learning. IEEE Transactions on Information Theory, 9(4), 257–261. https://doi.org/10.1109/tit.1963.1057854
Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A. &, Aljaaf, A.J. (2020). A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In: Berry, M., Mohamed, A., Yap, B. (Eds.) Supervised and Unsupervised Learning for Data Science. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-22475-2_1
Cateni, S., & Colla, V. (2013). Data processing for outliers detection. Pattern Recognition: Methods and Application, 1-18. Corpus ID: 125899499. https://www.semanticscholar.org/paper/Data-processing-for-outliers-detection-Cateni-Colla/f6df892c359719ed71ba408c380343c8535a6615
Cateni, S., Vannucci, M., Vannocci, M., & Colla, V. (2012). Variable selection and feature extraction through artificial intelligence techniques. Multivariate analysis in management, engineering and the Science, 6, 103-118. https://doi.org/10.5772/53862
Chaudhuri, A., Mandaviya, K., Badelia, P., & Ghosh, S.K. (2017) Optical Character Recognition Systems. In: Optical Character Recognition System for Different Languages with Soft Computing, Studies in Fuzziness and Soft Computing, Springer International Publishing, New York. https://doi.org/10.1007/978-3-319-50252-6_2
Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2017). Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks. IEEE Communications Surveys & Tutorials. https://doi.org/10.48550/arXiv.1710.02913
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE signal processing magazine, IEEE Explore, 35(1), 53-65. https://doi.org/10.1109/MSP.2017.2765202
European Commission. (2023a). EU invests €220 million to test AI solutions for healthcare, food, industry, and everyday life, Shaping Europe’s digital future. News Article| Publication 29 June 2023. https://digital-strategy.ec.europa.eu/en/news/eu-invests-eu220-million-test-ai-solutions-healthcare-food-industry-and-everyday-life
European Commission. (2023b). Sectorial AI Testing and Experimentation Facilities under the Digital Europe. Shaping Europe’s digital future. 29 June 2023. https://digital-strategy.ec.europa.eu/en/activities/testing-and-experimentation-facilities
European Commission. (2023c). Commission opens access to EU supercomputers to speed up artificial intelligence development. Press release. IP/23/5739. Brussels 16 November 2023
Fu, K. S. (1982). Applications of pattern recognition. CRC press. Boca Raton,(pp. 38-61). https://doi.org/10.1201/9781351069809
Hua, Y., Guo, J., & Zhao, H. (2015). Deep belief networks and deep learning. Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things. IEEE Explore. (pp. 1-4). https://doi.org/10.1109/ICAIOT.2015.7111524
Isheawy, N. A. M., & Hasan, H. (2015). Optical character recognition (OCR) system. IOSR Journal of Computer Engineering, e-ISSN, 2278-0661. https://doi.org/10.9790/0661-17222226
Liu, J., Sun, J., & Wang, S. (2006). Pattern recognition: An overview. IJCSNS International Journal of Computer Science and Network Security, 6(6), 57-61.
Lopez P., & Walter H. (2020). Chapter 11 - Autoencoders. Machine Learning Methods and Applications to Brain Disorders, Academic Press. 193-208. https://doi.org/ 10.1016/B978-0-12-815739-8.00011-0
Medsker, L. R., & Jain, L. C. (2001). Recurrent neural networks. Design and Applications, 5(64-67), 2. CRC Press. https://doi.org/10.1201/9781003040620
Mori, S., Suen, C. Y., & Yamamoto, K. (1992). Historical review of OCR research and development. Proceedings of the IEEE, IEEE Xplore, 80(7), 1029-1058. https://doi.org/10.1109/5.156468
O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. Cornell University. arXiv. https://doi.org/10.48550/arXiv.1511.08458
Ozgur, A. (2004). Supervised and unsupervised machine learning techniques for text document categorization. Master’s Thesis, Boğaziçi University. İstanbul.
Park, S. H., & Lee, S. P. (1998). EMG pattern recognition based on artificial intelligence techniques. IEEE transactions on Rehabilitation Engineering, IEEE Explore, 6(4), 400-405. https://doi.org/10.1109/86.736154
Prorok, M. (2022). Applications of artificial intelligence systems. Deliberationes, 15, (Különszám), 73-88. https://doi.org/10.54230/Delib.2022.K.SZ.76
Saba, T., & Rehman, A. (2013). Effects of artificially intelligent tools on pattern recognition. International Journal of machine learning and cybernetics, 4, 155-162. https://doi.org/10.1007/s13042-012-0082-z
Sandhu, T. H. (2018). Machine learning and natural language processing—A review. International Journal of Advanced Research in Computer Science, 9(2), 582–584. https://doi.org/10.26483/ijarcs.v9i2.5799
Shinde, S. P., & Deshmukh, V. P. (2011). Implementation of Pattern Recognition Techniques and Overview of its Applications in various areas of Artificial Intelligence. International Journal of Advances in Engineering & Technology, 1(4), 127. https://api.semanticscholar.org/CorpusID:61658349
Sondak, N. E., & Sondak, V. K. (1989). Neural networks and artificial intelligence. Proceedings of the twentieth SIGCSE technical symposium on Computer science education. ACM Digital Library. (pp. 241-245). https://doi.org/10.1145/65293.71221
Szabadföldi I. (2022). A mesterséges intelligenciával támogatott nyílt információszerzés (OSINT) – evolúció és kihívások. Nemzetbiztonsági szemle, 10(1), 30–51. https://doi.org/10.32561/nsz.2022.1.3.
Theodoridis, S., & Koutroumbas, K. (2006). Pattern recognition. Second Edition. Elsevier. 1-11. eBook ISBN: 9780080513621
