Revolutionising the Fashion Industry through AI: Optimising Business Processes and Sustainability

An article by Anna Pang

Artificial intelligence (AI) is revolutionising the world of fashion, impacting everything from trends in manufacture to customer experience.

Most people’s minds will gravitate towards AI’s most common commercial use — automated manufacturing processes. This can lead to faster and more efficient production, as well as increased flexibility in responding to changes in consumer demand. Corporate responsibility increases engagement in sustainability, driving the development of new materials from natural resources by using AI technologies.

However, AI can also be used in more technical and even creative areas — such as optimising business strategy and consumer experience. One of the key benefits of AI in fashion is its ability to analyse large amounts of data and make predictions about consumer behaviour. This helps retailers to target their marketing campaigns and product offerings more effectively, leading to increased sales and customer loyalty.

The most innovative of these ideas allows customers to virtually try on clothes before purchasing them through styling and fitting tools. This greatly improves the online shopping experience whilst also impacting a company’s environmental impact by reducing the need for returns, helping to reduce quantity in production and carbon footprint.

With the AI fashion market expected to grow to $2.66 in 2026 at a compound annual growth rate (CAGR) of 4.2% (ResearchAndMarkets Report, 2022), is the fashion industry obliged to embrace the integration of big data and new technologies to maintain relevance and long-term commercial success?

Automation in Retail

Paying for your items without the frustration of scanning barcodes, stagnant queues, or cashier interaction is a problem solved by the current media target — Amazon Fresh. However, they are not the only businesses employing AI in this area with clothing stores, such as Uniqlo, also taking suit. Store automation acts on instant detection and calculation of the exact items you intend to purchase. Automating the common clothing store does not have to be overly complex to be effective; they just need to evoke a sense of satisfaction.

Multiple in-store operations are improved by a combination of AI technologies such as computer vision, machine learning, and Radio-Frequency Identification (RFID) tracking (Turbide, 2016). Computer vision can be used to track inventory by using cameras to scan and identify products and machine learning can be used to predict sales trends and make recommendations to customers. RFID tags can be placed on products to track their location within the store and monitor inventory levels in real time (Mcdowell, 2021).

Virtual reality mirrors will transform our perception of style, creating a more personalised and interactive shopping experience. This can include features such as virtual try-on, personalised styling, and size recommendations. When AI helps consumers determine intricate details such as fabric texture or colour palettes, they become more accustomed to experiment around a desired look.

In terms of augmented reality, these mirrors use a combination of 3D scanning, computer vision, and machine learning to create a realistic and personalised virtual try-on experience. 3D scanning — used to create a detailed model of the clothing — alongside computer vision — which tracks the customer’s body movements — can give the user an image of themselves wearing the products that the store provides. Machine learning further enhances this experience by generating personalised styling recommendations based on the customer’s preferences and body type (TextileToday, 2022).

In many ways, this technology will make retail workers’ lives a lot easier, however these changes can make workers significantly more vulnerable to redundancy. It is important to note that retail jobs are already in decline due to the sharp increase in e-commerce and online services, escalating to new levels during Covid-19. The UK saw a loss of 83,000 jobs (or a decline of 2.7%) compared to a year ago and a loss of 135,000 compared to three years ago (4.4%) (Retail Job Report, 2022). Therefore, forecasting and managing various risks is key when fashion revolves around AI.

Clearly, the impact of these technologies on the clothing industry will be revolutionary. Automation can lead to increased efficiency and cost savings for retailers, while augmented reality mirrors can enhance the shopping experience and drive sales. It could also lead to increased personalisation and convenience for customers, and potentially reduce the need for physical retail spaces — reducing jobs for low-skilled workers.

This is inevitable for any industry however, a technologically evolving world will drive the development and upskilling of our workforce. An increase in technical adeptness helps us find solutions to address the labour intensive and ‘undesirable’ aspects of the commercial fashion world. This — at its foundation — is an inequality issue due to the push towards the need for high-quality education, which benefits the privileged and isolates the poor.

Supply Chains

But why are machine learning algorithms so beneficial for supply chains (Burgess, 2021)? Because it introduces a new wave of sustainability — something the world of fashion is itching for. Both investors and the public are striving for greener companies to buy from, and the intensification of Environmental, Social and Governance (ESG) requirements fundamentally change the way companies and businesses operate.

One of the many sustainable steps may occur through the synthesis and production of materials. Analysing and predicting the properties of different materials based on their chemical composition allows for the creation of sustainable materials with specific desired properties. Integrating these technological processes into business models has the potential to increase the quantity of raw materials which produce high-quality goods at more affordable prices.

AI-synthesised materials can offer new and unique sustainable alternatives to traditional materials like cotton (Wolhuter, 2021), reducing the environmental impact of the fashion industry. In terms of supply chains, AI can be used to optimise logistics and production processes, reducing waste and emissions. This can lead to more efficient use of resources and a reduction in the cost of goods.

Businesses can use AI to analyse consumer data and predict market trends, allowing them to adjust pricing strategies according to supply and demand (Burgess, 2021). Additionally, the use of sustainable materials and efficient production processes can result in lower costs for businesses, which can be passed on to consumers in the form of lower priced products. It can be argued that, because of this, permitting our personal data to be stored by companies is benefiting all of us in the long run.

Many companies are already adapting to this new technology, using AI to develop clothing fabrics and materials that are more sustainable and cost-effective. Bolt Threads is just one of these companies, using AI and biotechnology to create sustainable, high-performance fabrics from mushrooms. Their proprietary technology allows them to optimise the properties of the fabric to meet specific performance criteria, while moving away from toxic processes, petroleum-based polymers, and non-biodegradable materials. Their sustainability efforts reach past this, also using AI to monitor and control the production process to reduce waste and emissions (Rao, 2015).

WG Flex has also joined the trend with their sustainable activewear line. With AI, the company can develop sustainable, performance-driven fabrics that are made from recycled plastic bottles. Their technology allows them to create fabrics with specific properties, such as moisture management, UV protection, and stretch recovery. Like Bolt Threads, their uses of AI reach outside of materials — with AI being employed to optimise production processes to reduce waste and emissions (WG Blog, 2022).

AI is also hitting more mainstream companies like H&M and Zara where it is being is used to predict trends through capturing information on search engines and blogs. AI helped H&M restock popular merchandise to reduce company waste and make more sustainable decisions. According to their CEO in 2019, Karl-Johann Persson reported that H&M’s venture had “significantly improved the company’s ability of trend prediction” (Nikolopoulos, 2022).

Challenges

Controversially, AI implementation in fashion isn’t as glamourous as it seems. The technology is still relatively new and there is a lack of standardisation in the industry which makes it difficult for companies to compare and evaluate different materials and technologies.

Moreover, the cost of using AI technology can be high, with a lack of infrastructure and resources in place to support the development and commercialisation of these materials. As many consumers still prefer traditional materials like cotton and polyester, companies find it difficult to sell these new and different materials. The lack of consumer awareness about the benefits of these materials makes it difficult for companies to justify the investment, especially when there is no guarantee that users will like the change. Finally, there are regulatory challenges, such as a lack of clear guidelines on how to test and certify these new materials, which may make it difficult for companies to bring these products to market (Rogovskiy, 2021).

Despite these challenges, the rapidly developing nature of AI means this technology will no doubt play a key role in reducing human error and improving the speed and accuracy of various tasks like sourcing materials, tracking inventory, and supply chain management. The implementation of AI in businesses relies on business acumen. Therefore, it is a question of business adaptability and investment strategy to create market opportunities which lead to long-term commercial success.

Conclusion

The world is opening its eyes to sustainable and ethical fashion. AI revolutionises the industry by leading businesses towards the optimisation of sustainability, commodities, and cost — being set to transform new business strategies and widen the dimensions of customer experience. By analysing customer data, creating virtual fitting tools, and automating production processes, AI can help fashion companies to better understand their customers, to target or expand their brand, and create more personalised, efficient, and innovative products. The integration of AI is proving to become much more than a profitability strategy for the future of the industry.

While fashion is a vehicle for this environmental, psychological, and cultural shift — artificial intelligence is the driver for the permanent redesign and reengineering of today.

References

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