THE ROLE OF ARTIFICIAL INTELLIGENCE IN BUSINESS PROCESS OPTIMISATION IN ECONOMY 5.0
Keywords:
Role, Artificial Intelligence, Optimisation, Business Process, Economy 5.0Abstract
In the era of Economy 5.0, artificial intelligence (AI) plays a critical role in business process optimisation aimed at improving operational efficiency, analytical data utilisation, and competitive advantage. AI enables automation of routine tasks, reduces operational costs, and improves productivity by minimising human errors. In addition, AI strengthens a company's analytical capabilities through predictive data analysis, which helps in smarter decision-making and responsiveness to market dynamics. Overall, the adoption and investment in AI technology is an important strategic step for companies to face the challenges and opportunities of the Economy 5.0 era, as well as to improve customer service and adaptability to market changes.
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References
Berman, S. J. (2012). Digital Transformation: Opportunities to Create New Business Models. In Strategy & Leadership.
Chollet, F. (2015). Keras: The Python Deep Learning library. Https://Keras.Io.
Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review Press.
Deloitte. (2019). AI in Business: Shifting Skills for the New Workforce.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv Preprint arXiv:1810.04805.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680.
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14.
Hart, C. (1998). Doing a Literature Review: Releasing the Social Science Research Imagination. SAGE Publications.
Hart, C. (2001). Doing a Literature Search: A Comprehensive Guide for the Social Sciences. SAGE Publications Ltd.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., 770–778.
Jesson, J., Matheson, L., & Lacey, F. M. (2011). Doing Your Literature Review: Traditional and Systematic Techniques. SAGE Publications.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimisation. arXiv Preprint arXiv:1412.6980.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Mia, M. A. B., & Shamsuddin, Z. H. (2010). Rhizobium as a crop enhancer and biofertiliser for increased cereal production. African Journal of Biotechnology, 9(37), 6001–6009.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., & Petersen, S. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.
Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd Edition). Pearson.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., & Lanctot, M. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., 1–9.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
Zhou, Z. H. (2012). Ensemble methods: Foundations and algorithms. CRC Press.
