Information and engineering sciences
RESEARCH ARTICLE   (Open Access)

Fashion Innovation Driven by AI: Transforming Design, Manufacturing, and Customer Experience

Arpon Roy1*

+ Author Affiliations

Applied IT & Engineering 2(1) 1-8 https://doi.org/10.25163/engineering.2110224

Submitted: 05 December 2023  Revised: 07 February 2024  Published: 12 February 2024 

Abstract

Background: The fashion and apparel industry is being experienced a technological disruption from the developing prevalence of artificial intelligence (AI). The speed of trend cycles, demand for personalized experiences, and increased sustainability expectation from the consumer are all putting pressures on brands, but AI can generate new opportunities in various points across the value chain. Methods: This paper synthesizes peer reviewed literature, industry reports, and case studies of fashion brands, to assess how AI is currently used in fashion. Four areas were examined: virtual try-on technologies, personalization or recommendation systems, AI-assisted design, and supply chain. Data was gathered from over 50 sources and insights provided by McKinsey, Statista and leading fashion brands. Results: AI-driven technologies have significantly improved efficiency and customer engagement. Virtual try-on tools have increased online conversion rates by up to 40%, while personalized recommendations have boosted sales by 20–30%. AI-powered supply chain analytics have cut inventory costs by nearly 25%, and automated design tools have reduced prototyping times by over 30%. Retailers such as H&M and Stitch Fix have observed better operational performance and as much as 35% reduction in material waste, as reported by the organizations, on account of their use of AI. Conclusion: The findings conclude that AI provides changes to the fashion industry that improves personalization, efficiency and sustainable fashion. AI and AR are influencing consumer experiences and businesses. When deployed strategically and ethically, AI can change the course of the fashion industry.

Keywords: AI Fashion tech, AI in Design, Sustainability, AI in prediction, predictive analytics.

References

Akram, U. (2024, September 10). Exploring market entry strategies for pakistani exports in Germany. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5144

Al-Jarrah, O. Y., Yoo, P. D., Muhaidat, S., Karagiannidis, G. K., & Taha, K. (2015). Efficient Machine Learning for Big Data: a review. Big Data Research, 2(3), 87–93. https://doi.org/10.1016/j.bdr.2015.04.001

Babu, M. M., Akter, S., Rahman, M., Billah, M. M., & Hack-Polay, D. (2022e). The role of artificial intelligence in shaping the future of Agile fashion industry. Production Planning & Control, 35(15), 2084–2098. https://doi.org/10.1080/09537287.2022.2060858

Balasubramani, S., Aklin, H. R., Sabarishwaran, G. H., Sandeep, R., Sreeram, R., & Sureshbabu, Y. (2025, February). Design and fabrication of machine vision system for knitting machine. In AIP Conference Proceedings (Vol. 3204, No. 1). AIP Publishing.

Bidollahkhani, M., & Kunkel, J. M. (2024, April 20). Revolutionizing System reliability: The role of AI in predictive maintenance Strategies. arXiv.org. https://arxiv.org/abs/2404.13454

Biswas, P., Rashid, A., Biswas, A., Nasim, M. a. A., Chakraborty, S., Gupta, K. D., & George, R. (2024). AI-driven approaches for optimizing power consumption: a comprehensive survey. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00211-7

Busari, M. (2025). AI and Machine Learning Algorithms for Predictive Supply Chain Management in Textile Manufacturing.

De Mattos, F. B., Eisenbraun, J., Kucera, D., & Rossi, A. (2021). Disruption in the apparel industry? Automation, employment and reshoring. International Labour Review, 160(4), 519–536. https://doi.org/10.1111/ilr.12213

Elena, M. (2020, December 9). Artificial intelligence in fashion?: how consumers and the fashion system are being impacted by AI-powered technologies. https://www.politesi.polimi.it/handle/10589/167521

George, A. S. (2024). Artificial Intelligence and the Future of Work: Job Shifting Not Job Loss. puirp.com. https://doi.org/10.5281/zenodo.10936490

Haider, M. Z. (2007). Competitiveness of the Bangladesh ready-made garment industry in major international markets. Asia-Pacific Trade and Investment Review, 3(1), 3-27.

Hussain, H. I., Haseeb, M., Kot, S., & Jermsittiparsert, K. (2020). Non-linear impact of textile and clothing manufacturing on economic growth: The case of top-Asian economies. Fibres & Textiles in Eastern Europe, (5 (143), 27-36.

Ingle, N., & Jasper, W. J. (2024). A review of deep learning and artificial intelligence in dyeing, printing and finishing. Textile Research Journal. https://doi.org/10.1177/00405175241268619

Ingle, N., & Jasper, W. J. (2025). A review of the evolution and concepts of deep learning and AI in the textile industry. Textile Research Journal. https://doi.org/10.1177/00405175241310632

Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2021). Artificial Intelligence Applications for Industry 4.0: A Literature-Based Study. Journal of Industrial Integration and Management, 07(01), 83–111. https://doi.org/10.1142/s2424862221300040

Kalinin, A., Rudnik, R., Tsvetov, A., Bondarenko, K., & Shuranova, A. (2024). Emerging markets decoded 2024. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4862785

Khan, A., & Jalal, A. (2023). Supply Chain Optimization through Technology Integration: Riding the Digital Wave to Efficiency. Abbottabad University Journal of Business and Management Sciences, 1(01), 53-63.

Khan, M. A., Khan, H., Omer, M. F., Ullah, I., & Yasir, M. (2024). Impact of artificial intelligence on the global economy and technology advancements. In Advanced technologies and societal change (pp. 147–180). https://doi.org/10.1007/978-981-97-3222-7_7

Lee, J. (1995). Machine performance monitoring and proactive maintenance in computer-integrated manufacturing: review and perspective. International Journal of Computer Integrated Manufacturing, 8(5), 370–380. https://doi.org/10.1080/09511929508944664

Md Habibur Rahman, Tanjila Islam, Mohammad Hamid Hasan Amjad, Md Shihab Sadik Shovon, Md. Estehad Chowdhury, Md Rahatul Ashakin, Bayazid Hossain, Proshanta Kumar Bhowmik, Md Nurullah, Atiqur Rahman Sunny (2024). "Impact of Internet of Things (IoT) on Healthcare in Transforming Patient Care and Overcoming Operational Challenges", Journal of Angiotherapy, 8(11),1-8,10041. https://doi.org/10.25163/angiotherapy.81110041

Miller, T., Durlik, I., Kostecka, E., Kozlovska, P., Staude, M., & Sokolowska, S. (2025). The role of lightweight AI models in Supporting a Sustainable Transition to renewable Energy: A Systematic review. Energies, 18(5), 1192. https://doi.org/10.3390/en18051192

Mohan, T. R., Roselyn, J. P., Uthra, R. A., Devaraj, D., & Umachandran, K. (2021). Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery. Computers & Industrial Engineering, 157, 107267. https://doi.org/10.1016/j.cie.2021.107267

Orisadare, E. A., Achukwu, O. E., Ogunyemi, A. A., Adedeji, D. O., Diyaolu, I. J., Ugwu, E. I., Oluwatope, A. O., Bakare, K. O., & Awoyelu, I. O. (2025). Digitalisation and Green Strategies: A Systematic review of the textile, apparel and fashion industries. Circular Economy and Sustainability. https://doi.org/10.1007/s43615-025-00555-x

Ozek, A., Seckin, M., Demircioglu, P., & Bogrekci, I. (2025). Artificial Intelligence Driving Innovation in Textile Defect Detection. Textiles, 5(2), 12.

Rahman, M. H., Aunni, S. A. A., Ahmed, B., Rahman, M. M., Shabuj, M. M. H., Das, D. C., Akter, M. S., Numan, A. A. (2024). "Artificial intelligence for Improved Diagnosis and Treatment of Bacterial Infections", Microbial Bioactives, 7(1),1-18,10036. https://doi.org/10.25163/microbbioacts.7110036

Rahman, M. H., Biswash, M. A. R., Debnath, A., Siddique, M. A. B., Rahman, M. M., Rabbi, M. M. H., Mou, M. A. (2025). "The Future of AI in Laboratory Medicine: Advancing Diagnostics, Personalization, and Healthcare Innovation", Journal of Primeasia, 6(1),1-6,10151. https://doi.org/10.25163/primeasia.6110151

Rahman, M. H., Biswash, M. A. R., Siddique, M. A. B., Rahman, M. M., Mou, M. A., Debnath, A., Fatin, M. (2025). "Significance of Artificial intelligence in clinical and genomic diagnostics", Journal of Precision Biosciences, 7(1),1-14,10149. https://doi.org/10.25163/biosciences.7110149

Rahman, M. H., Islam, T., Hossen, M. E., Chowdhury, M. E., Hayat, R., Shovon, &. M. S. S., Shabbir, H. -. A. -., Alamgir, M., Akter, S., Chowdhury, R., Sunny, A. R. (2024). "Machine Learning in Healthcare: From Diagnostics to Personalized Medicine and Predictive Analytics", Journal of Angiotherapy, 8(12),1-8,10160. https://doi.org/10.25163/angiotherapy.81210160

Ramachandran Thampy Bindhu, V., & Kurumbadan Saseendran, A. (2024). AI REVOLUTIONIZING MANUFACTURING: CUTTING-EDGE ADVANCES: Redefining Manufacturing with Intelligent Solutions.

Rehman, M. M. U. (2024). Exploring the Link Between Artificial Intelligence and Circular Economy in the Fashion Industry: an example of Lennol Oy. Theseus. https://www.theseus.fi/handle/10024/865358

Rojas, L., Peña, Á., & Garcia, J. (2025). AI-Driven Predictive Maintenance in Mining: A systematic literature review on fault detection, digital twins, and intelligent asset management. Applied Sciences, 15(6), 3337. https://doi.org/10.3390/app15063337

Sikka, M. P., Sarkar, A., & Garg, S. (2024). Artificial intelligence (AI) in textile industry operational modernization. Research Journal of Textile and Apparel, 28(1), 67-83.

Taplin, I. M. (2014). Global commodity chains and fast fashion: How the Apparel industry continues to Re-Invent itself. Competition & Change, 18(3), 246–264. https://doi.org/10.1179/1024529414z.00000000059

Vangeri, A. K., Bathrinath, S., Anand, M. C. J., Shanmugathai, M., Meenatchi, N., & Boopathi, S. (2024). Green Supply Chain Management in Eco-Friendly Sustainable Manufacturing Industries. In Environmental Applications of Carbon-Based Materials (pp. 253-287). IGI Global.

Yilmaz, K., Aksu, I. Ö., Göçken, M., & Demirdelen, T. (2024). Sustainable Textile Manufacturing with Revolutionizing Textile Dyeing: Deep Learning-Based, for Energy Efficiency and Environmental-Impact Reduction, Pioneering Green Practices for a Sustainable Future. Sustainability, 16(18), 8152. https://doi.org/10.3390/su16188152

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