The Rise of Artificial Intelligence in the Fashion and Clothing Industry

Fashion has always been at the forefront of innovation. Fashion, like technology, is cyclical and forward-thinking. Any cutting-edge technology that generates advanced tools for the fashion industry, whether to improve production or consumption, is referred to as fashion technology.

Designers, producers, merchants, and customers might all benefit from the technology, depending on its role. We may anticipate fashion technology to become more widespread as new technologies become accessible.

According to Tractica, the global AI software industry would produce USD 118.6 billion in sales by 2025. Another Juniper Research estimate, worldwide retail investment in artificial intelligence is predicted to reach $7.3 billion per year by 2022.

Your business is already behind if you aren’t leveraging Artificial Intelligence (AI) to connect with your clients. This article examines the state of artificial intelligence in online fashion retail, contrasting organizations that employ it with others that don’t.

Applications of Artificial Intelligence Ranging From Inventory Planning to Personalisation.

Through automation, the breadth of AI applications increases to the actual garment production process. Despite the fact that using robots to handle fabric is difficult, a number of firms are working in this area. Grabit, situated in the United States, uses a combination of static electricity, machine learning, and automation to assemble clothing, and has collaborated with Nike on the production of their trainers. At a macro level, AI allows employees to focus on value creation rather than standardized everyday duties.

Meanwhile, this labour, which is embedded in the intricate fashion supply chain, has paid the price for fast fashion. Workers at garment factories are compelled to labour in sub-optimal circumstances and are underpaid.

Established companies have been chastised for failing to do more to address this distressing aspect of the fashion supply chain. Again, AI-based solutions are being used to track the tiers of suppliers involved in various phases of garment production and to promote a transparent culture.

The application of AI in inventory demand planning is proving to be quite beneficial. Real-time data enables the brand to respond to the stock demands of both its physical and online outlets. To outperform the competition and avoid extra inventory, Zara continues to invest in AI, automation, and big data in its supply chain and business strategy.

In comparison to the back-end or supply chain, firms were quicker to experiment with and incorporate AI-based solutions on the consumer side of the company.

Artificial Intelligence-based fashion trend identification might cut predicting mistakes in half.

Artificial Intelligence (AI) has shown to be a powerful driver of technical innovation, and fashion firms may make use of this technology’s potential to become more sustainable in their operations.

Today, AI may be defined as intelligent programs that do jobs that would normally be performed by people. Machine Learning, Deep Learning, Natural Language Processing, and Visual Recognition are all examples of this field of study. Simply said, by extracting information from enormous amounts of data, the technology provides insights that back up a fashion designer’s creative intuition with facts, allowing them to create better and become more sustainable.

The employment of artificial intelligence (AI) at many phases of production in the fashion industry, from pre- to post-design, as well as logistics, results in intelligent manufacturing. It all starts with intelligence from the very beginning, with demand forecasting and planning. The precision spreads like a domino effect, ensuring long-term viability.

According to research by Opus Restructuring and Juniper Research, AI in the fashion sector will be so common in 2020 that 44 per cent of UK fashion shops that have not embraced AI would go insolvent. As a result, by 2022, the fashion and retail industries are expected to invest $7.3 billion annually in AI.

These predictive analytics aids in production and demand planning to minimize overstocking, a strategy that lowers waste by ensuring that collections are scheduled with proper inventory assortment and amount. Furthermore, trend forecasting may support the design team’s intuition and creativity by predicting which styles will succeed or fail.

As a consequence, the brand sees a favourable return on investment as overstock is reduced, manufacturing is streamlined, turnover is optimized, and relevant sales are generated.

Conclusion

Without a doubt, the jobs now carried out by AI will get more sophisticated, faster, and precise. Artificial intelligence will become ingrained in our daily lives, allowing us to perform better at work, thanks to high-quality training data as it allows machines to perform as per the requirement. Cogito Tech LLC provides high quality training dataets for AI business.