Ra00158

NameSubject name- AC1 Trend publication
Topic- Artificial Intelligence Everywhere
Word limit- 1500
Format- Harvard
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Introduction
The trend in this context is the Artificial Intelligence that is ruling the fashion styling segment
due to increased aspects of personalisation by the fashion retailers. They have redefined the
fashion styling by reaching to their target consumers through their personalised styling
suggestions. The study will be using STEEP analysis followed by key innovators and drivers,
current and future market effects and consumer effects.
STEEP method
Social- Most of the customers particularly Gen Y and Z like personalised style by AI and this
can happen when they are asked to take a quiz, share their dislikes and likes and the styles that
they prefer most. On submission of their answers, the AI gives them suggestions (Tolcheva,
2021). AI has the ability of shifting through millions of clothing options where the customers
can pick their preferred choices.
Technological- The first and foremost use of AI in fashion industry is its advisory role that is
changing the trends of the fashion industry from readymade to customised clothes. AI digital
assistants are used for recommending clothes to the customers as per their current size, shape,
height and weight. The customers are given the accurate sizing as per their details into the
system. On an average 40% of online purchases are usually returned so AI helps in reducing
number of returns (Byers, 2020).
Economic- AI is setting the fashion trend of “circular economy business models” so that the
fashion retailers can deliver fashion products at competitive prices, while setting a good margin
on the second-hand market. The fashion industry is the biggest sector in the world, generating
about 3 trillion dollars in the year 2018 (Schmelzer, 2019). AI has been transforming the
industry in each element of its value chain like sales, manufacturing, marketing, logistics and
designing. As per “McKinsey Global Fashion Index (2018)”, AI is the game changer of the
industry with tripling of sales between 2016 and 2018 from 1.5% to 4.5% (Masciarelli, 2019).
Environmental- The fashion sector is known for its environmentally damaging and wasteful
practices accounting for 10% of carbon emissions that will get increase to 24% of the global
carbon by the year 2050 (perfectsourcing.net, 2020). AI helps in accelerating and informing
efforts for designing waste minimisation strategies for reducing pollution. Digital samples have
been replacing the physical garments by AI during development and design phases is reducing
the carbon footprint up to 30%. Well-known brands are reducing the wastage of water, toxic
chemical pollution and CO2 pollution with the use of virtual designs through AI (Chen, 2021).
Political- AI has been acknowledged as the advanced technology that has the ability of making
better decisions and augmenting core business processes so it has been advocated by the
governments. The fashion sector is booming in the country as AI can help the retailers to save
$340 billion annually by ensuring efficiency in its operations and processes (Chen, 2021). With
the support from the political stability, AI can help in reducing errors in forecasting trends and
predictions in the future.
Key drivers and innovators
Fashion brands are reshaping their approaches for design and development of products for
predicting the things that the customers want. By harnessing data directly from the users, the
brands like Stitch Fix and Finery for having easy and fast access to information for helping the
people to indulge in styles that they love. Stylumia has deployed machine learning and AI for
helping the lifestyle and fashion brands to forecast demand, manage inventory, spot trends and
make effective business decisions (bernardmarr.com, 2021). Brands like Next plc and Zara are
the forerunners of AI in fashion industry where the customers can get fashionable products as
they seek. Both the brands have infused Big Data and Analytics and Artificial Intelligence for
staying ahead in the competition of fashion industry.
AI is helping the fashion industry in making 3D technologies for improving personalised
styling so that the online experiences of the customers can be upgraded. For instance, Stitch
Fix has created a Style profile for the customers utilising the initial Style Quiz where the data
points for personal characteristics of the customers, their clothing characteristics, historical
interactions, merchandise data and post-fix feedback help in developing predictive algorithms.
This has helped in overcoming the traditional frustrating experiences of the customers
searching clothes online that suit to their styles and preferences (Chen, 2021). On the other
hand, the online fashion retailer ASOS has launched its new visual search technology via its
app. ASOS AI technology can easily identify its patter, colour and shape of the object and then
cross-reference its inventory of products while serving the best results. Its capturing of vast
amounts of data has helped in personalisation in online fashion (perfectsourcing.net, 2020).
This ensures that the large amount of data can be captured for designing and providing clothing
wear, driving the market trends.
Key current and future market impacts
Figure 1: Use of AI in fashion
(Source: created by the learner)
Culture- The AI has helped in shifting the traditional culture of online searching of the
products by the customers to the customised culture where based of characteristics and
personalised preferences, the fashion products are delivered to them. It has reduced their time
and effort and the brands can easily capture customer preferences. The virtual ecosystems in
fashion sector is revamping the needs of the younger generations who are looking for fast
fashion where the real-time information mapping can help the brands to get quick conversion
rates and data on stock management, profits and sales (Silvestri, 2020). This is generating a
new cultural era where the retailers can take fast decisions, fill gaps in the system and minimise
the possibility of producing excess stock of fashion products.
Brands- Fast fashion brands have been under pressure to integrate technologies into their
sector due to differentiation and exclusivity in trend forecasting. The use of technology in areas
of risk management, procurement payment systems, delivery and supply chain can help in
capturing preferences of Gen Y and Z (Bide, 2021). One of the brands like Thread is the UKoriented fashion retailer that is built on AI where the human stylists play an important role
selecting the inventory and turning them into suggested outfits. Recommendations are
generally sent to the customers per week and they sent the feedback regarding the outfit. It can
spot the patterns in the images that helps in reflecting the customers preferred style and then
combine it with the right products for enriching the customers’ experiences. The optimal
functioning through AI is the central aspect of Thread’s technology since with the more
feedback from the customers they deliver more recommendations (Cook, 2017).
Products- Retailers have used AI for replacing the photoshoots and predicting the things that
the people want in the future. For example, Startup Finesse is utilising AI for trawling the web
in predicting the next trend and utilising the algorithm design for producing the small line of
clothing within 25 days. The company is utilising the 3D modelling software for its gender
neutral-clothing wear in reducing the costs and wastes created during the sampling process. It
is expected that AI in retail is going to make it worth $19 billion by the year 2027 (Burgess,
2021).
Creative works and services- AI plays an influential role in tracking the elements of design
like patterns, cut, fabric and colour for analysing their past performance and future performance
indicators. It helps in making new designs of apparel with the sewing patterns and these are
sent directly to the manufacturing processes so that it can incorporated into the clothing for
automating the fit process and pattern making (Dennis, 2019). AI can help in scaling the
manufacturing processes in the fashion industry by automating fabric quality control, defect
detection, colour matching and pattern inspections. However, Bide (2021) argued that AIoriented smart textiles can help in increasing value to the fashion sector by shifting towards
sustainable practices. this can help in making right predictions in the future and implement
decisions accordingly.
Key consumer impacts
The target customers for AI in fashion are those who are tech-savvy and are driven by the
online technology that influence their purchases and needs. The increased need of
customisation and personalisation is evident across Gen Y and Z who are selective in their
designs, textiles, cut and look. This is because the fast fashion segment is growing at an
increasing rate and this is influencing the customers to try for new style and designs (Dennis,
2019). In this current era, the customers prefer to deliver their opinions and feedback regarding
fashion that suits their taste and preferences. The use of AI in fashion sector is driving the
retailers to catch the pace of changes of preferences as per their personality needs and demands.
This target age group is generally those having higher to moderate income and can change their
brands if they gain advantages from other brands (Jin and Shin, 2020). These are generally
working professionals, young entrepreneurs and college goers who seek to gain enriching
experiences from their online shopping experiences via websites.
References
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