AI in Industry Forecasting: The Use of AI in Predicting Industry Trends and Demands
Keywords:
AI Forecasting, Industry Trends, Machine Learning, Predictive Modelling, Sustainable DevelopmentAbstract
The rapid advancement of Artificial Intelligence (AI) has increasingly transformed industrial operations, yet its application in accurately forecasting industry trends and market demands remains underexplored. This study aims to evaluate the effectiveness of AI-based predictive models in anticipating market shifts and consumer needs across multiple sectors. Employing a quantitative research design, data were collected from 150 industry reports and historical market datasets. Predictive modelling techniques, including machine learning algorithms such as random forests and neural networks, were applied to analyze trends and forecast demand patterns. The results indicate that AI models significantly enhance the accuracy of industry forecasting, reducing prediction errors by up to 25% compared to traditional approaches. These findings suggest that integrating AI into strategic decision-making can optimize resource allocation, improve market responsiveness, and support sustainable industrial development. By enabling more efficient production planning and market adaptation, AI-driven forecasting contributes indirectly to long-term economic and environmental sustainability. Overall, the research demonstrates that AI not only advances technological capabilities but also aligns with broader sustainability objectives.
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1] M. Rakhmansyah, M. S. Hadi, S. R. P. Junaedi, F. A. Ramahdan, and S. N. W. Putra, “Integrating blockchain and ai in business operations to enhance transparency and efficiency within decentralized ecosystems,” ADI Journal on Recent Innovation, vol. 6, no. 2, pp. 157–167, 2025.
[2] A. Amroni, A. A. Darmawan, and G. A. Pangilinan, “The role of big data and blockchain in enabling transparent and sustainable business processes,” ADI Journal on Recent Innovation, vol. 6, no. 2, pp. 180–189, 2025.
[3] M. Murod, S. Anhar, D. Andayani, A. Fitriani, and G. Khanna, “Blockchain based intellectual property management enhancing security and transparency in digital entrepreneurship,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 1, pp. 240–251, 2025.
[4] F. Weber, Artificial intelligence for business analytics. Springer, 2023.
[5] A. M. Rahmani, E. Azhir, S. Ali, M. Mohammadi, O. H. Ahmed, M. Y. Ghafour, S. H. Ahmed, and M. Hosseinzadeh, “Artificial intelligence approaches and mechanisms for big data analytics: a systematic study,” PeerJ Computer Science, vol. 7, p. e488, 2021.
[6] U. Rahardja, C. T. Sigalingging, P. O. H. Putra, A. Nizar Hidayanto, and K. Phusavat, “The impact of mobile payment application design and performance attributes on consumer emotions and continuance intention,” Sage Open, vol. 13, no. 1, p. 21582440231151919, 2023.
[7] H. D. Natalia and A. Aprillia, “Exploring the impact of e-wom on generation z purchase intention: The mediating role of brand image and perceived quality,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 6, no. 2, pp. 164–176, 2025.
[8] J. Khalid, M. Chuanmin, F. Altaf, M. M. Shafqat, S. K. Khan, and M. U. Ashraf, “Ai-driven risk management and sustainable decision-making: Role of perceived environmental responsibility,” Sustainability, vol. 16, no. 16, p. 6799, 2024.
[9] N. Lutfiani, S. Wijono, U. Rahardja, A. Iriani, and E. A. Nabila, “Artificial intelligence based on recommendation system for startup matchmaking platform,” in 2022 IEEE Creative Communication and Innovative Technology (ICCIT). IEEE, 2022, pp. 1–5.
[10] Z. I. Ajwa, A. Patel, and A. Moseley, “Harnessing ai technologies: Innovations in literacy libraries for diverse learners,” International Journal of Cyber and IT Service Management (IJCITSM), vol. 4, no. 1, pp. 19–25, 2024.
[11] K. Dev, S. A. Khowaja, A. S. Bist, V. Saini, and S. Bhatia, “Triage of potential covid-19 patients from chest x-ray images using hierarchical convolutional networks,” Neural Computing and Applications, vol. 35, no. 33, pp. 23 861–23 876, 2023.
[12] X. Kong, Z. Chen, W. Liu, K. Ning, L. Zhang, S. Muhammad Marier, Y. Liu, Y. Chen, and F. Xia, “Deep learning for time series forecasting: a survey,” International Journal of Machine Learning and Cybernetics, vol. 16, no. 7, pp. 5079–5112, 2025.
[13] A. Rom´an-Portabales, M. L´opez-Nores, and J. J. Pazos-Arias, “Systematic review of electricity demand forecast using ann-based machine learning algorithms,” Sensors, vol. 21, no. 13, p. 4544, 2021.
[14] U. Rahardja, P. A. Sunarya, N. Lutfiani, M. Hardini, and H. R. Dananjaya, “Analysis of renewable energy utilization using solar power technology in eliminating microplastic emissions,” in 2022 IEEE Creative Communication and Innovative Technology (ICCIT). IEEE, 2022, pp. 1–6.
[15] M. A. Olawumi and B. I. Oladapo, “Ai-driven predictive models for sustainability,” Journal of Environmental Management, vol. 373, p. 123472, 2025.
[16] M. Malempati, V. Krishnaprasath, P. S. Raja, S. Darshana, K. Chava, and T. Suresh, “Ai-driven predictive models for future sustainability initiatives,” in International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024). Atlantis Press, 2025, pp. 86–99.
[17] K. Douaioui, R. Oucheikh, O. Benmoussa, and C. Mabrouki, “Machine learning and deep learning models for demand forecasting in supply chain management: A critical review,” Applied System Innovation, vol. 7, no. 5, p. 93, 2024.
[18] R. G. Munthe, M. Abbas, R. Fernandez, and N. Ulita, “The impact of educational information systems on learning accessibility in higher education,” International Transactions on Education Technology (ITEE), vol. 3, no. 1, pp. 94–103, 2024.
[19] L. K. Choi, N. Iftitah, and P. Angela, “Developing technopreneur skills to face future challenges,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 127–135, 2024.
[20] M. Tsuneki, “Deep learning models in medical image analysis,” Journal of Oral Biosciences, vol. 64, no. 3, pp. 312–320, 2022.
[21] Y. Kristiana, V. A. H. Goeltom, and T. C. Nathalia, “Ai-driven pos, adaptable workload, and service culture influence on frontline employee performance,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 1, pp. 217–227, 2025.
[22] Q. Aini, E. Sediyono, K. D. Hartomo, D. Manongga, U. Rahardja, I. Sembiring, and N. A. Santoso, “Relationship quality analysis using technology in the business sector,” in 2023 11th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2023, pp. 1–6.
[23] T. Hariguna, U. Rahardja, and Q. Aini, “The antecedent e-government quality for public behaviour intention, and extended expectation-confirmation theory,” Computer Science and Information Technologies, vol. 4, no. 1, pp. 33–42, 2023.
[24] R. Aprianto, S. Surajiyo, S. Suwarno, and Y. Santia, “The power of celebrity endorsements: Examining the barenbliss brand image among tiktok users,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 6, no. 1, pp. 21–33, 2024.
[25] N. Ulita, A. T. Kartanegara, J. Salsabila, A. Saleh, and Z. Queen, “Empathy map gen z towards healthy food: A foodpreneur design strategy,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 2, pp. 242–253, 2024.
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Copyright (c) 2025 Mohd Faiz Hilmi, Hamdan Hamdan, Adam Faturahman, Bintang Nandana Henry, Jessica Wilson
This work is licensed under a Creative Commons Attribution 4.0 International License.
