Introduction

Artificial intelligence (AI) is ushering in a new era of hyper-personalisation in the fashion and beauty industries. From online style recommendations to custom-formulated skincare, AI-driven customisation is changing how brands interact with consumers. In an age where omnichannel retail tech connects online and in-store experiences, shoppers have come to expect tailored interactions at every touchpoint. This article explores what AI customisation means for fashion and beauty, why it matters, and how it’s being applied both globally and in the Australian market.

Today’s time-pressed executives need to grasp that AI is not a futuristic novelty – it’s a core part of retail strategy. Leading brands are using AI to analyse customer data, predict preferences, and deliver personalised experiences that boost loyalty and sales. In fact, personalisation has become so critical that businesses not embracing AI risk falling behind. Let’s dive into how AI-powered customisation works in fashion and beauty, and what opportunities and challenges it brings.

‍

What Is AI Customisation in Fashion and Beauty?

AI customisation refers to the use of artificial intelligence to tailor products, services, and experiences to individual customer preferences in the fashion and beauty sectors. Unlike one-size-fits-all retailing, AI enables hyper-personalised shopping journeys – essentially giving each customer their own virtual stylist or beauty consultant. By crunching vast amounts of data (from browsing history and past purchases to body measurements and skin type), AI systems can make highly relevant recommendations or even create bespoke products for each person.

For example, an AI algorithm might learn that a particular fashion shopper favours minimalist designs and earthy colours, and then curate a homepage feed or email promotion filled with items matching that style. In beauty, AI can analyse a user’s skin profile and recommend a foundation shade or skincare regimen uniquely suited to them. This level of personalisation goes beyond simple segmentation; it uses machine learning to detect subtle patterns and predict what each customer will love, often before the customer explicitly knows it. The result is a shopping experience that feels curated just for the individual – whether they’re buying a dress or a moisturizer.

In essence, AI customisation combines data-driven insights with automation. It encompasses everything from AI-powered recommendation engines and chatbots to virtual try-on tools and generative design platforms. The goal is the same: make every customer feel seen and understood by delivering exactly what they need or want, at the right time, through their channel of choice. It’s a powerful way to cut through retail clutter and build genuine customer loyalty.

‍

Why Personalisation Matters More Than Ever

Personalisation isn’t just a marketing buzzword – it has a direct impact on business performance. Shoppers have grown accustomed to the personalised suggestions they get from digital leaders like Amazon and Netflix, and they now expect similar treatment from fashion boutiques and beauty brands. Studies consistently show that consumers prefer and reward personalised experiences:

• Higher Conversion and Sales: Offering tailored recommendations and content can significantly boost conversion rates and revenue. For instance, personalised product suggestions on websites have been shown to lift sales by as much as 19%, and companies that get personalisation right see shoppers spend more – sometimes 30% or more per purchase – compared to a generic experience. In fact, one analysis found that 80% of consumers are more likely to buy from brands that offer personalised experiences, and retailers investing in personalisation have reported over 400% return on investment in some cases.
• Customer Loyalty and Retention: Personalisation builds loyalty. When customers feel a brand truly “gets” them, they tend to return and recommend that brand to others. Roughly two-thirds of consumers say they’ll stay loyal to businesses that provide a more personalised experience. On the flip side, a lack of personal touch can drive shoppers away – many customers admit they get frustrated or lose trust when marketing messages or product suggestions aren’t relevant to their needs. In an era where switching brands is easy, tailoring the experience can be a deciding factor in keeping long-term customers.
• Competitive Advantage: In the competitive retail landscape, especially in omnichannel environments, personalisation is becoming a key differentiator. Executives and managers are finding that AI-driven customisation is not just enhancing customer experience but also optimising operations. For example, smarter targeting means marketing budgets are spent more efficiently, as AI helps identify which products or offers each segment of customers is most likely to respond to. Retailers who leverage AI for personalisation can better compete with e-commerce giants by offering the kind of bespoke service traditionally found in high-end boutiques – but at scale. Those who ignore this trend risk being seen as out of touch, especially as younger, digitally-native shoppers gravitate towards brands that engage them one-on-one.

In summary, personalisation matters because it aligns retail with fundamental human desires – the desire to be understood, valued, and catered to. AI makes it possible to deliver this kind of experience consistently to millions of customers. For decision-makers in fashion and beauty, the message is clear: investing in AI customisation is not just about tech adoption, it’s about meeting rising customer expectations and driving concrete business results.

‍

AI Customisation in Fashion: Personal Stylists at Scale

In the fashion industry, AI is acting like a supercharged personal stylist, customising the shopping journey for each customer. Here are several ways AI-driven personalisation is being applied in fashion:

• Personalised Product Recommendations: Online fashion retailers use AI algorithms to recommend items that match each shopper’s style, size, and even occasion. By analysing browsing behavior, purchase history, and feedback (like what items were clicked or ignored), machine learning models can display “hand-picked” product suggestions on homepages, product detail pages (“Wear it With” recommendations), and in marketing emails. For example, Australia’s leading online fashion platform THE ICONIC employs AI to rank and sort its catalogue in real-time based on customer preferences and trends. If a customer frequently buys streetwear, the site’s AI will show more sneakers and hoodies front-and-center, whereas a formalwear shopper will see tailored suits and dresses. The Iconic even introduced an AI-powered “Wear It With” feature that suggests complementary pieces to complete an outfit, effectively upselling while helping the customer envision a full look. This level of curation mimics the attention of a personal shopper, boosting engagement and conversion rates by making the selection process feel more relevant.
• Virtual Fitting and Sizing Tools: One of fashion retail’s biggest challenges is fit – a medium in one brand might fit completely differently in another. AI is tackling this through virtual fitting rooms and sizing assistants. Many brands now offer mobile apps or online tools where customers input their body measurements (or even scan their body using a smartphone camera), and AI algorithms recommend the best size in a particular garment. Some solutions create a 3D avatar of the shopper to virtually try on clothes. This technology helps shoppers visualize how a jacket or dress might look on them without stepping into a physical changing room. For instance, Levi’s and Nike have experimented with AI-driven body scan apps (like Nike Fit, which scans your feet to recommend the correct shoe size for each style). These tools not only reduce the guesswork for customers but also cut down on returns – a major cost for online fashion retailers. By getting the size right the first time, AI customisation enhances customer satisfaction and contributes to more sustainable fashion by reducing the waste associated with return shipping and discarded garments.
• Design Personalisation and On-Demand Fashion: AI is also enabling custom fashion design. Generative AI models can create clothing designs or prints based on individual inputs, allowing brands to offer on-demand fashion that aligns with a customer’s unique taste. For example, a customer could answer a short style quiz or upload inspiration images, and an AI design tool would generate a few custom dress designs or T-shirt prints tailored to that person’s preferences. Some fashion startups are already using AI to analyse social media trends and customer mood boards to propose new styles, which can then be made to order. This approach not only delivers a one-of-a-kind product to the customer, but also supports sustainability – garments are produced only when there’s confirmed demand, avoiding overproduction. Sustainable fashion is a growing priority, and AI helps by predicting trends and optimizing inventory (so brands stock more of what will sell and less of what won’t). In fact, many fashion companies use AI demand forecasting to decide what to manufacture for a season, aligning supply with actual consumer interest. This reduces excess inventory and waste, marrying personalisation with eco-conscious practice.
• Omnichannel Personalised Experiences: Fashion shoppers often hop between online and physical channels, and AI is central to creating a seamless, personalised experience throughout this omnichannel retail journey. Modern point-of-sale systems and clienteling apps in stores are empowered by AI to recognise loyal customers and their preferences. For example, a sales associate with a mobile POS tablet can see that an arriving customer has a history of buying athletic wear and can instantly recommend the new sneaker release or a matching workout outfit. Retailers like Nike and Adidas integrate their apps with in-store experiences: a customer might use the app to browse and save favorites, then an AI-driven system can alert store staff to set those items aside when the customer walks in. Some smart fitting rooms even recognise items you bring in and suggest accessories or alternatives via an interactive mirror display, using the same recommendation engines that power e-commerce sites. The result is that the personalisation isn’t confined to a website algorithm – it continues in the brick-and-mortar environment, making shoppers feel the brand knows them wherever they shop. Australian department stores and boutiques are beginning to explore these technologies too, ensuring that local customers get the same level of high-tech personal service found in global retail hubs.

AI customisation in fashion essentially transforms data into stylistic intuition. It means every customer can discover clothes that fit their body and style as if a human stylist were guiding them – but it’s all powered by algorithms behind the scenes. The payoff for retailers is higher sales, fewer returns, and a stronger connection between the customer and the brand.

AI-driven virtual try-on tools and fit advisors can lower return rates, boost customer engagement, and increase conversion by helping shoppers find the perfect style and size on the first try. In the image above, a customer uses a mobile app to virtually test a beauty product – a similar approach is used in fashion for trying on outfits digitally before purchase.

‍

AI Customisation in Beauty: From Virtual Try-Ons to Bespoke Formulas

The beauty industry is equally transformed by AI customisation, as brands harness technology to act like a personal beauty advisor for each customer. Key applications of AI in beauty include:

• Virtual Try-Ons for Makeup and Hair: The days of guessing whether a lipstick shade will suit you are over. Augmented reality (AR) combined with AI now lets customers virtually try on cosmetics using their smartphone camera or smart mirrors in stores. Pioneered by companies like ModiFace (acquired by L’Oréal) and Perfect Corp, these virtual try-on tools overlay digital makeup on a live image of the customer’s face in real time. Shoppers can swipe through different shades of eyeshadow, lip colour, or even see how a new hair color might look, all without physically applying a thing. Global retailers such as Sephora have integrated virtual try-on into their apps (e.g., Sephora’s “Virtual Artist” can instantly show how hundreds of lipstick shades or styles of false eyelashes will appear). This not only makes the buying process more fun and interactive, but also boosts confidence in purchase decisions – customers know exactly what they’re getting, leading to higher conversion rates. In Australian beauty retail, Mecca and other stores have begun offering in-store iPad stations or mirrors where customers can try on products digitally, reflecting a trend that has quickly become an industry standard worldwide.
• AI Skin Analysis and Personalised Skincare: Skincare is deeply personal – effective routines vary based on an individual’s skin type, concerns, environment, and genetics. AI is revolutionising skincare consultations by providing high-tech analysis that was once only possible in a dermatologist’s clinic. Using a smartphone selfie or a specialized scanner, AI-powered tools can assess skin characteristics (like moisture levels, pores, wrinkles, hyperpigmentation) and then recommend products or routines tailored to that person’s needs. For example, Olay’s Skin Advisor asks users to upload a selfie and answer a few questions; the AI then analyzes the apparent skin age and highlights areas of concern, suggesting specific Olay products that match the user’s profile. Similarly, some beauty brands provide AI-driven skin diagnostic kiosks in stores – you place your face into a device or take a scan, and it spits out a custom report plus product suggestions. This kind of data-driven personalisation makes the often confusing world of skincare more accessible. It also builds trust – when a customer sees an analysis that, say, their skin is oilier in the T-zone and needs hydration elsewhere, they feel the brand understands their unique complexion and can offer the right solution.
• Custom-Formulated Products: Beyond recommending off-the-shelf items, AI is enabling bespoke beauty products crafted for one particular customer. This is an exciting development in cosmetics – think custom-mixed foundation that perfectly matches your skin tone or a serum formulated with ingredients responding to your specific skin DNA and lifestyle. Some high-end brands have introduced foundation mixing machines in-store that use AI shade-matching; they scan your skin and then physically mix pigments on the spot to produce a one-in-a-million foundation shade just for you. In skincare, brands like Proven, Function of Beauty, and Skin Inc use online quizzes and AI algorithms to design custom formulas (lotions, shampoos, etc.) for each client from a library of ingredients. The AI crunches data from your responses (and sometimes broader datasets like environmental conditions in your area) to determine the optimal blend for your needs. The result is that customers receive products literally labeled with their name on it, with formulations no two people have exactly alike. This level of personalisation was previously only available via bespoke consultation services; AI has helped automate and scale it, making customised beauty accessible to more consumers. It strengthens customer loyalty too – once someone has a perfect-match product, they’re less likely to switch to a generic alternative.
• AI Beauty Advisors and Chatbots: Just as fashion has AI stylists, beauty has AI-powered consultants. Some retailers and brands deploy chatbots on websites or messaging apps that can answer beauty questions and provide tailored advice 24/7. These conversational AI agents use large language models and trained beauty datasets to simulate an expert beauty advisor in chat form. For example, Perfect Corp recently showcased a “BeautyGPT” assistant that customers can chat with to get makeup tips, product suggestions based on personal preferences, and even virtual makeup tutorials. By learning from each interaction – noting a user’s preferences, skin concerns, or style – the AI assistant refines its recommendations over time. It’s an always-on, personalised beauty concierge that can handle everything from “What kind of moisturizer should I use for winter?” to “Can you show me a look with a smoky eye?”. While a human makeup artist or sales clerk might not be available at midnight or during a big online sale, the AI beauty advisor is. This improves customer engagement and helps shoppers make informed choices from the comfort of home. Major beauty retailers internationally and in Australia are exploring these chat-based advisors to complement their e-commerce and customer service teams.
• Smart Mirrors and In-Store Tech: In physical beauty stores and salons, AI is powering smart mirrors that recognise customers and provide personalised recommendations on the spot. A smart mirror might, for instance, recall that you bought a certain skincare regimen last time and ask how it’s working, or suggest a new product that complements the ones you use. It could also use facial recognition and AR to overlay makeup suggestions as you look into it, essentially bringing the virtual try-on experience into a life-sized reflection. Brands like Charlotte Tilbury and L’Oréal have experimented with these magic mirrors to intrigue tech-savvy shoppers and create a unique in-store experience. Similarly, some hair salons use AI apps to let clients visualise haircuts or colours before making the leap. All of this reduces the uncertainty that often comes with beauty decisions, making customers more comfortable and satisfied with their choices.

AI customisation in beauty ultimately makes the cosmetic shopping experience more personal, precise, and empowering. Consumers get expert guidance tailored to their face, skin, and style – much like having a personal dermatologist and makeup artist on call – delivered via apps and devices. For beauty brands, this fosters higher engagement (users spending more time trying products virtually), increased sales (customers confident in purchases buy more), and potentially fewer product returns or dissatisfaction. It’s a win-win where technology enriches the age-old pursuit of looking and feeling one’s best.

‍

Global Trends and the Australian Market

AI-driven customisation is a global phenomenon, but its adoption can vary by market. Internationally, we see both luxury and mass-market brands embracing AI in creative ways. In the US and Europe, retailers like Amazon, H&M, and Zara use AI recommendation engines and inventory optimisations, while beauty giants like Estée Lauder and Shiseido invest in AI labs to develop proprietary personalization tech. Asian markets are also leading in some respects – for instance, Chinese e-commerce platforms are famed for their highly personalised feeds and use of AI influencers or virtual models to engage shoppers.

In the Australian market, retailers have been quick to recognise the importance of AI personalisation to stay competitive. A striking data point is that as of 2025, over 91% of retailers in Australia and New Zealand report they are investing in AI (especially generative AI) as part of their toolkit. Australian companies see AI as a way to level up to global standards: for example, supermarket chains like Woolworths and Coles use AI to personalise promotions and shopping suggestions in their apps (remember the earlier example of Woolworths’ system learning your grocery preferences to make your next visit smoother). On the fashion front, The Iconic – being a digital-native company – is a local leader in AI adoption, using it for everything from customer service chatbots to personalised product sorting as detailed. Traditional retailers are not far behind; department stores and even smaller boutiques are experimenting with personalised emails, AI styling apps, and more, often through partnerships with tech providers.

The Australian beauty industry, too, is riding the AI wave. Major pharmacy chains and beauty retailers in Australia offer skin analysis apps or devices (sometimes the same ones used globally, tailored for the local market). Australian consumers, much like their global counterparts, show strong interest in personalised beauty solutions – surveys indicate a majority are open to sharing data (like a skin scan or quiz answers) if it leads to better product matches. We’re also seeing global brands bring their AI experiences to Australian stores: Sephora Australia, for example, has introduced its Virtual Artist tools in-store and online so that Aussie customers can try on products virtually. Local skincare brands are popping up with customisable product offerings, leveraging AI or online quizzes to formulate bespoke creams or serums for clients.

An important trend globally and in Australia is the blending of sustainability with AI customisation. Brands are aware that personalisation can support sustainability goals by reducing overstock and waste. In fashion, Australian brands are considering “made-to-order” models informed by AI demand predictions, ensuring they only produce what will sell. In beauty, personalised product recommendations can steer consumers towards items they’ll actually use (cutting down the drawer of half-used products and encouraging mindful consumption). Additionally, fewer returns from better sizing/try-on means less waste. All these aspects resonate in a market like Australia, where consumers are increasingly environmentally conscious.

In summary, Australia may be smaller in market size than the US or China, but it’s very much aligned with the global shift towards AI-driven personalisation. Australian retailers and brands look to global success stories for inspiration but often move nimbly to implement what makes sense locally. With a high percentage of retailers investing in AI and a tech-savvy population, Australia is set to benefit from the AI customisation trend just as much as – and perhaps in some niche ways even more than – other major markets.

‍

Benefits of AI Customisation for Retailers and Customers

Implementing AI customisation in fashion and beauty offers several compelling benefits:

• Enhanced Customer Experience: Shoppers enjoy a smoother, more relevant experience. They don’t have to sift through hundreds of products – the most pertinent items are presented to them, whether it’s that perfect dress for an event or a foundation that matches their skin tone. This convenience and personal touch increase customer satisfaction. Many consumers have expressed that they feel more valued when brands tailor experiences to their needs. The result is often higher customer happiness scores and brand affinity.
• Higher Conversion Rates and Sales Uplift: Personalisation driven by AI tends to translate into immediate sales improvements. By showing customers products they are more likely to want, retailers increase the chance of purchase. For example, recommendation engines (“You might also like” or “Frequently bought together”) often generate a significant share of e-commerce revenue. When brick-and-mortar staff use AI insights (like knowing a returning customer’s preferences), they can cross-sell and upsell more effectively. The end effect is that average order values often rise and so do overall conversion rates. There are numerous case studies of brands seeing double-digit percentage growth in sales after rolling out AI personalisation features.
• Improved Loyalty and Retention: AI customisation can boost customer loyalty in the long run. By consistently delivering relevant content and products, brands build trust – the customer feels the company truly understands them. Loyalty programs integrated with AI can send personalised rewards or recommendations, keeping customers engaged beyond transactions. Furthermore, a personalised experience encourages repeat business; one statistic notes a large percentage of shoppers are more likely to become repeat buyers after a positive personalised online shopping experience. Loyal customers not only come back more often, but they also tend to refer friends and family, amplifying the benefit.
• Operational Efficiency & Stock Optimisation: On the back-end, the data and predictions used for personalisation help retailers manage inventory and supply chains more efficiently. AI can predict trends or surges in demand for certain styles or shades, allowing companies to stock appropriately and avoid the twin problems of stockouts or overstock. This is particularly beneficial in fashion, where trends can be fleeting, and in beauty where certain products may spike in popularity due to social media. By aligning inventory with likely purchase patterns, retailers reduce waste and holding costs. They can also dynamically adjust pricing (markdowns or promotions) for slow-moving items targeted to interested customers, thereby clearing inventory in a more targeted way. These efficiencies ultimately improve margins.
• Reduction in Returns and Increased Sustainability: As mentioned, better fit guidance and accurate product matching mean customers are more satisfied with what they buy, leading to fewer returns. This is a major benefit because returns are costly for retailers (especially online orders of apparel) and contribute to environmental waste. When a dress fits right or a cosmetic product meets expectations thanks to virtual try-on, the customer is less inclined to send it back. Over time, this can significantly lower return rates, which directly improves profitability and also helps the brand’s sustainability profile. Less shipping back and forth and less discarded product is good for the planet – a growing concern for modern consumers. Brands can even highlight this in their messaging: that by leveraging AI to personalise, they are committing to sustainable fashion practices through waste reduction.
• Data-Driven Decision Making: AI customisation systems provide a wealth of data and insights that can guide broader business decisions. Retailers learn more about their customers’ preferences, which can inform everything from product development (what new style or formula to create next) to marketing strategy (which segments respond to what messaging). In essence, every interaction in a personalised system is a learning opportunity. Over time, companies build rich customer profiles and can identify trends early. This is strategic gold for executives – knowing, for instance, that a certain fabric or ingredient is trending among your highest-value customers allows you to double down in that area ahead of competitors.

For customers, the benefits are feeling understood, saving time, and achieving better outcomes (like looking good in a garment that truly suits them or using a skincare product that actually works for their skin). For retailers and brands, the benefits hit the bottom line and future growth: more sales, stronger loyalty, leaner operations, and a modern brand image.

‍

Challenges and Considerations

While AI customisation brings many advantages, it also comes with challenges and things to watch out for:

• Data Privacy and Security: Personalisation requires data – and often very personal data at that, from body measurements to facial images and purchase histories. Retailers must handle this information with care. Consumers and regulators (especially in places like Europe and Australia with strict privacy laws) expect transparent data practices and robust security. A breach or misuse of personal data can severely damage trust. Companies need to ensure they have consent to use customer data for personalisation, anonymise data where possible, and store it securely. Striking the balance between helpful personalisation and not seeming “creepy” is key – overly intrusive recommendations can backfire if customers feel their privacy is invaded.
• AI Bias and Inclusivity: AI systems are only as good as the data and algorithms behind them. If not carefully managed, they can reflect or even amplify biases. In fashion and beauty contexts, this might mean an AI stylist that only recommends outfits in straight sizes unless it’s trained on diverse body types, or a beauty AI that doesn’t work as well for darker skin tones because the training data lacked diversity. Brands must actively work to ensure their AI is inclusive and fair – for example, including a wide range of skin tones, ages, body shapes, and cultural preferences in their data. This is both an ethical consideration and a business one: an AI that fails certain customer groups will alienate those potential buyers. Regular audits and updates of AI models are necessary to mitigate bias.
• Technical and Implementation Costs: Adopting AI customisation isn’t as simple as flipping a switch. It can require significant investment in technology (software platforms, possibly new hardware like AR mirrors or scanners) and in talent (data scientists, AI specialists, or at least training for staff to use new tools). For some smaller retailers, off-the-shelf solutions or partnerships (for example, using a third-party AI recommendation engine service) can lower the barrier, but there is still a learning curve. The cost and effort to integrate AI into existing systems (like linking an AI engine with inventory and e-commerce platforms) can be high. Without careful planning, an organisation might struggle to see quick returns, so a phased or strategic implementation (starting with one area such as personalised recommendations, then expanding) is advisable.
• Customer Adoption and Trust: Not all customers immediately embrace AI features. Some may be sceptical of a chatbot’s advice or unsure about using a virtual try-on tool. There is often a need for customer education – explaining how a feature works and its benefits. For example, an in-store customer might not use a smart mirror until a beauty advisor shows them how it can instantly compare lipstick shades. Building trust in AI recommendations is a process; if the first few suggestions an AI gives are off-target, a customer may ignore it entirely. Thus, brands need to monitor the quality of personalisation and possibly blend AI with a human touch (e.g., having human experts oversee or refine AI-generated recommendations) especially in the early stages, to ensure customers get value from it. Over time as consumers get used to AI helpers (think of how voice assistants like Siri or Alexa gained acceptance), this hurdle will lessen, but it’s an important consideration during rollout.
• Keeping the Human Touch: While AI can automate and scale personalisation, there’s still value in human creativity and empathy. The best approach often uses AI to augment human experts, not replace them. For instance, AI might narrow down fashion choices, but a human designer or buyer ensures the selections align with brand aesthetics and seasonal trends. In customer service, a chatbot might handle FAQs, but live agents are there for complex, sensitive issues. Retailers should be careful not to lose the human element entirely. Many customers appreciate knowing there are real people behind the brand. Maintaining an option for human consultation (like a live stylist chat or in-store expert advice) alongside AI features can provide reassurance and a superior experience.
• Ethical Use and Transparency: There are emerging ethical questions around AI use in creative industries. If an AI designs a clothing pattern or touches up a model’s face in a makeup ad, should that be disclosed? Some consumers feel deceived if AI-generated content isn’t labeled as such, especially in beauty where authenticity is prized (for example, overly “perfect” AI-edited images could set unrealistic beauty standards). Brands are encouraged to use AI responsibly – for example, using it to enhance diversity in marketing by generating models of varied appearances (a positive use) versus secretly replacing all human models with AI avatars without telling viewers (which could cause backlash). Being transparent about how AI is used – say, “This look was visualised using our AR Makeup Try-On tool” – can actually be a selling point, showcasing innovation. But hiding AI’s role, if discovered, might invite criticism. As AI customisation becomes more prevalent, retailers will likely develop guidelines for ethical AI use, much like they have for sustainability and fair trade.

In facing these challenges, the overarching principle is to keep the customer’s interest and comfort at heart. AI customisation should ultimately serve the customer, not just the bottom line. If implemented thoughtfully, with attention to privacy, inclusivity, and quality control, the hurdles can be overcome. Many brands find that starting small, measuring results, and iterating helps to address technical or adoption issues early. And as industry standards and best practices evolve, even mid-sized and smaller players will find it easier to deploy AI personalisation effectively.

‍

Future Outlook: Personalisation 2.0 and Beyond

Looking ahead, AI customisation in fashion and beauty is poised to become even more sophisticated and deeply integrated into the retail experience. Executives and managers should watch these emerging trends as they plan for the future:

• Generative AI for Design and Content: We’re entering an era where generative AI models (like advanced image and text generators) could take personalisation to new heights. Imagine a fashion AI that doesn’t just recommend a dress but actually designs one from scratch based on your individual preferences and body shape – a truly one-of-a-kind piece delivered to you. This is already on the horizon with experimental projects allowing customers to tweak design elements in real time. In beauty, generative AI might create custom colour palettes or even virtual influencers that represent different customer archetypes. Marketing content will also be hyper-personalised; AI can generate unique ad copy or imagery tailored to each customer’s profile (for example, showing a skincare product’s ad with a model who has a similar skin tone or concern as the viewer). Hyper-personalisation could reach an almost granular level, with AI creating bespoke shopping experiences that vary widely from person to person.
• Deeper Omnichannel Integration: The line between online and offline will blur further. Future stores might universally adopt AI-driven features – from entrance sensors that recognise VIP customers and alert a salesperson along with the customer’s wishlist, to smart shelves that adjust displays based on the demographic of shoppers currently in the store. Mobile apps will sync seamlessly with in-store experiences; for instance, you might receive a personalised discount on your phone as you pick up an item in a shop, facilitated by AI predicting what would entice you in that moment. Omnichannel retail tech powered by AI will ensure that whether a customer is on social media, an e-commerce site, a physical store, or using a voice assistant at home, the experience is consistently tailored and connected.
• AI Assistants as Standard Shopping Companions: Just as many people now wouldn’t consider driving somewhere unknown without a GPS, in a few years shoppers might routinely use AI assistants for any purchasing decision. We’re already seeing the early signs – around one-third of shoppers (and an even higher share of Gen Z) have experimented with AI tools (like ChatGPT or shopping bots) to discover products. By 2030, it’s predicted that a significant portion of consumer transactions will be influenced by AI, either directly or indirectly. Voice-activated shopping assistants could become personal fashion advisors (“AI, I need an outfit for a wedding next month”) or skincare coaches (“AI, analyze my skin today and update my regimen”). As these assistants improve and gain trust, brands will need to ensure their products and data are accessible to these AI systems. This may open new fronts in competition – for example, vying to be the top recommendation from someone’s personal AI stylist.
• Greater Personal Control and Customisation by Customers: The future might also empower consumers to fine-tune how they are personalised. Some platforms may give users an interface to input their preferences or feedback to the AI (“show me more of this style, less of that”), essentially co-creating their personalised experience. This kind of transparency and control can enhance comfort with AI. Additionally, advancements in user data portability could allow consumers to carry their preference profile from one brand to another – maybe via a secure digital identity – meaning a new retailer’s AI could immediately personalise offerings without starting from scratch. This could benefit smaller players if industry standards emerge, allowing them to tap into an existing ecosystem of personalization data (with user consent).
• AI and Sustainability Convergence: As environmental concerns grow, AI-driven personalisation and sustainability will likely work hand in hand more tightly. For instance, AI might guide consumers toward more sustainable choices that fit their profile (highlighting eco-friendly fashion options to a shopper who values sustainability). Retailers will use AI not just to sell more, but to sell smarter – encouraging quality over quantity, recommending timeless pieces tailored to the individual’s wardrobe needs, or beauty products with refills once the AI knows a customer loves them. This nuanced approach could redefine loyalty from just repeat purchases to a long-term customer relationship where the AI helps manage the customer’s wardrobe or beauty cabinet in a sustainable, efficient way.

In conclusion, AI customisation in fashion and beauty is here to stay, and we are just scratching the surface of its potential. For executives and managers, the imperative is to stay informed and open to integrating these technologies in a way that aligns with your brand’s values and customers’ expectations. Early adopters have already reaped rewards in terms of customer engagement and sales, and soon, personalised AI-driven experiences will be the norm. Brands that embrace this trend thoughtfully – putting customer benefit at the center of their AI strategies – will not only meet the demands of today’s consumers but also build a foundation for continued relevance in the tech-driven decade ahead. It’s an exciting time where creativity, data, and technology intersect to make retail more personal and delightful than ever.