What Fashion And Beauty Can Teach One Another About Digital Transformation

Fashion and beauty are complementary industries in some respects, with crossover in the markets they reach and the broad parameters of the workflows they use, but their systems need to take account of very different variables. One sector works in textiles, sizing, and seasonal cycles. The other deals in ingredients, compliance, and clinical claims. At a glance, their tools might look alike, virtual try-on, AI diagnostics, recommendation engines, but their paths are shaped by different legacies, different structures, and different pressures. 

Nevertheless, the digital problems those two industries are now tackling (personalisation, simulation, sustainability, amongst others) are starting to line up to where there are common challenges, solutions, and ideas – making it worthwhile to look at where the two sectors can learn from, and teach, one another.

Fashion, for its part, has become far more technical than many outside the sector realise. Step inside any mid-sized – or even startup – apparel brand and you’ll find yourself surrounded by technology and infrastructure that would historically have only resided in the offices of the biggest companies. Product lifecycle management platforms, 3D simulation and sampling tools, demand forecasting algorithms, production optimisation platforms and more are no longer edge experiments. These technologies have found their way into daily operations, particularly in design, merchandising, and sourcing, and fashion brands big and small are both more heavily-influenced by technology than ever before, but also exert their own strong pull on the direction and the development of tech itself.  

But where the industry still falls short is knitting together those different solutions into a seamless, end-to-end workflow. Fragmentation is still the rule, not the exception, and what’s left is a kind of uneven digital scaffolding, strong in places, less so in others, and rarely built with a complete blueprint.

Beauty, for its part, begins from a tighter, more centralised place. It’s science-led, for one thing, built around documentation, compliance, ingredient traceability and rigorous auditing of claims as standard. All the elements that slot neatly into digital systems. Unlike fashion, beauty hasn’t always viewed digital as a growth engine. But the pieces are already there. Manufacturing tends to be automated. Ingredient data is structured from the start. Formulas get tested, tracked, signed off for different markets. And while this core is more connected by default than fashion’s is, there is still much ground for beauty to gain in digital transformation of everything that comes after the science.

One of the clearest splits between fashion and beauty shows up in how they use simulation. Where fashion has pushed much further into digital product creation, material digitisation, pattern simulation and so on, beauty has leaned more into the use of digital assets for consumer-facing virtual try-on. In fashion, despite internal 3D tools being considerably more mature, virtual try-on tends to show up as a web feature or an app add-on, mostly aimed at boosting engagement or cutting down returns.  And those applications still run into the same stubborn problems: how fabric moves, how bodies vary, how clothes really sit when someone wears them. Even the strongest examples on the market today can miss the mark when it comes to capturing the real physics of fit, especially across different body types. For a lot of shoppers, it still doesn’t feel like a substitute for stepping into a changing room.

In beauty, simulation already plays a more trusted role for consumers, even though the same can’t be said for how internal users are (or more specifically aren’t) interacting with digital representations of their products for other purposes. Shade matching, tone analysis, and augmented reality overlays have become standard parts of the online experience for many customers. AI diagnostics that once felt like novelties are already guiding purchasing decisions across skincare and colour cosmetics. Behind the interface, platforms like ModiFace and YouCam are doing more than visualising outcomes – they are capturing structured, personalised data that can feed directly into product development, shape planning, and even inform long-term R&D strategy.

This difference is not just a matter of visual fidelity. It reflects the nature of the data itself. In beauty, things like skin tone, texture, hydration, and pigmentation can be measured, analysed, and tracked with consistency. These traits shift slowly and tend to respond in ways you can actually observe. That kind of stability makes it easier to build a simulation that works, and to link it to a result you can trust. 

Fashion is a different story. The variables move faster. Fabric changes with gravity, with wind, with how someone moves. Fit isn’t fixed or even fully objective, it’s shaped by culture, trend, preference, and subjective assessment. That messiness makes clothing much harder to simulate in a way that feels real, especially with tools built for everyday shoppers.

This gap shows up in personalisation too. Fashion’s recommendation engines tilt much more strongly towards behaviour: what you’ve clicked on, what you’ve bought, where you live, who else shops like you. These recommendations can seem profound sometimes, but they are most often built on fairly shallow foundations. Beauty’s technology toolkit for recommendations can read what’s in front of it – the skin itself. Diagnostics that pick up tone, dryness, texture can give advice that feels rooted in present conditions, concerns, and considerations, not just in a trail of past behaviour.

Beauty is also beginning to adopt more of the in-house use cases for simulation that fashion has successfully either pioneered or adapted from other industries where 3D is more widely-adopted. Brands are beginning to use simulation tools to guide decisions about product ranges, to test colour matches virtually before committing to production, and to refine R&D priorities based on aggregated user data, thanks to technology startups who are translating some of the same principles applied in fashion to beauty and cosmetics. There are even early efforts to use simulation in areas such as claims substantiation or predictive formulation. These applications are not yet common, but they indicate a broader shift in how digital tools are being positioned as structural components.

The success of these applications depends not only on data quality, but also on the architecture that supports them – an area where beauty is, on aggregate, ahead of fashion. Many beauty brands, particularly those operating at scale, have already built centralised systems for managing ingredients, testing, and compliance as a result of developing in a more tightly-regulated environment. These systems enable traceability, enforce standardisation, and create consistent feedback loops. When simulation enters that environment, it has something stable to plug into, as well as an incredibly high bar for accuracy and fidelity to reach.

There are, of course, limitations. Skin biology remains complex. What people think of as clean, effective, or beautiful shifts from place to place, group to group, and as well-regulated as beauty is, some of these words are still open to being twisted or misinterpreted. Diagnostic tools do a decent job at the moment someone’s buying, but they’re far less useful when it comes to what happens next. Long-term results, how people actually use the product, what sticks and what doesn’t, that’s harder to track, and that gap causes problems, especially for brands trying to feed all this data back into product design.

Still, beauty enters the sustainability and traceability conversation from a position of relative strength. Ingredients have to be listed. Banned substances are already monitored to a more finite degree than they are in fashion, excluding subsectors such as childrenswear. Most formulas are built to pass the rules in each region they’re sold in. Those rules pushed a lot of companies to build detailed internal systems years ago, well before simulation or AI showed up. Now that groundwork is starting to pay off in new ways.

Fashion is facing the same pressures, but with different obstacles. Traceability, transparency, and sustainability have become critical priorities across the apparel sector. Yet for many companies, even the basic task of tracking fibres, finishes, and suppliers remains ongoing. The industry’s reliance on fragmented supply chains, third-party manufacturing, and private-label arrangements has made end-to-end visibility difficult to achieve. While progress is being made (particularly in fibre tracing, digital product passports, and ESG reporting) the system remains incomplete.

The types of risk these industries face also differ. In beauty, scrutiny often focuses on what goes into a product. Microplastics, allergens, hormone disruptors, and synthetic compounds are under constant examination. Regulatory changes can render an entire product line non-compliant with little warning. In fashion, the focus lands more often on what happens around the product, labour conditions, environmental impact, and overproduction. These risks emerge further upstream and are more diffuse, which makes them harder to measure and manage without system-wide change.

Even after comparing the relative maturity of these two industries in different areas, it would be a mistake to treat this as a race. Beauty has an advantage in some areas, but fashion has built genuine capability in others, and the two industries – despite starting from very different points – share a common ambition for digital transformation. And where one industry is behind the other, that relative lack of maturity is typically down to the unique nature of the product and the setup of the supply chain, rather than being the result of hesitancy – although industry mindsets are certainly still evolving. The apparel industry, for example,  has made significant progress in areas such as digital asset management, logistics automation, and predictive demand planning. Many of its challenges stem from the structural complexity of the product and the market, not from a lack of digital ambition.

For both fashion and beauty, the next step is not just about deploying more technology. The tools already exist. The real challenge lies in integration, in building workflows, data models, and regulatory frameworks that make the most of what these systems can offer. For beauty especially, this is not a question of catching up or pulling ahead. It is about building with intent, using the structure already in place to support a more connected, responsive, and transparent future – and building on what other sectors, including but not limited to fashion, have learnt from making technology a stronger pillar of the way they operate.