How DPC And AI Could Combine To Advance Digital Transformation

This article was originally published in The Interline’s DPC Report 2024. To read other opinion pieces, exclusive editorials, and detailed profiles and interviews with key vendors, download the full DPC Report 2024 completely free of charge and ungated.

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As we have seen throughout this report, DPC encompasses a range of digitally-led processes like designing, prototyping, and marketing products. And the key benefits are just as far-ranging: DPC can be used to streamline workflows, enhance sustainability, and speed up time-to-market.  

But there is, of course, another technology segment that has been shaking up all kinds of industries in the last two years, and challenging maybe an even broader range of processes than DPC: AI. Many brands, including household names  like Adidas, Nike and Zara, have been using digital product creation tools some time now, for rapid prototyping and market responsiveness. Are they – and the wider fashion industry – now taking the same steps using AI? And where do we see those two different but equally broad fields interacting?

Where fashion is using AI

Fashion has been exploring the use of AI within supply chains for a while now, using it to transform many traditional workflows over the years. The global fashion industry generates over $1.7 trillion annually, but inefficiencies in design, production and distribution can equally cost companies billions.  

AI is particularly well suited to solving these issues, because of its ability to process vast volumes of data, predict trends and simulate realistic designs.  From design ideation to material optimisation, AI is embedding itself into every stage of the fashion workflow. Here are some of the key areas where it has the potential to reshape the fashion landscape.

Generative Design and Ideation: With the development of tools like generative adversarial networks (GANs) and natural language processing (NLP), designers can now create multiple variations of designs quickly.  GANs work by using two neural networks—a generator and a discriminator—that compete to produce highly realistic images of garments, enabling designers to explore innovative styles and patterns through AI-generated outputs. NLP leverages language models to analyse trends, interpret design briefs, and generate detailed textual descriptions of garments, which can be transformed into visual prototypes or inspire new creative concepts.  

AI-powered generative tools analyse inputs such as colour palettes, textures, and trending silhouettes to create a range of design variations. For example, a designer might input keywords like ‘minimalist streetwear,’ and the AI will generate multiple options that align with these themes, all while adhering to specific brand guidelines or seasonal trends. These tools use machine learning models trained on extensive datasets, including historical designs, fashion shows, and even social media imagery, to produce outputs that feel both innovative and market-relevant.

The overall impact of using AI in this way is a reduction in the time and cost of brainstorming and sketching, allowing designers to focus on refining ideas.  An early example of this approach was when Tommy Hilfiger worked with IBM’s Watson to revive archival designs, blending the brand’s heritage with modern aesthetics in a fraction of the time it would take a human team.  More recently, Nike have been using AI to create pattern designs for personalised prototypes for a range of athletes within their roster, across a number of sports.

Virtual Sampling and Prototyping: Virtual sampling and prototyping represent a paradigm shift in how fashion brands approach design iteration. By integrating AI into 3D design tools like CLO3D, the vision is now to  create hyper-realistic digital garments that mimic the drape, texture, and movement of real fabrics. This not only eliminates the need for excessive physical samples but also allows teams to refine designs collaboratively in virtual spaces, significantly accelerating the development cycle while reducing waste.

AI-driven virtual prototyping combines body scan data with advanced fabric simulation algorithms to create lifelike digital garments. By analysing the physics of fabrics—how they stretch, fold, and flow—AI ensures that each virtual piece behaves as it would in the real world, offering designers and clients an accurate representation of drape, fit, and movement without needing a physical sample.

The use of data-driven body simulation, virtual sampling and digital prototyping cuts down on material waste and shortens the product development cycle by around 35%, whilst maintaining the accuracy of the final designs before putting them into production. Solutions like SEDDI’s Textura software use AI to produce accurate digital fabrics that mimic both the visual and the physical properties of the materials they are simulating.

Future directions for these processes could include linking virtual samples with AR/VR platforms for customer try-ons before production.  The Fashion Innovation Agency explored these processes in collaboration with visual-effects company Digital Domain, combining AI for cloth deformation with real-time motion capture data.

Trend Forecasting: AI-driven trend forecasting is overhauling the way brands anticipate consumer preferences and market shifts. By leveraging machine learning and natural language processing, these systems sift through vast datasets—from social media hashtags to online shopping patterns—identifying emerging styles and behaviours long before they reach mainstream adoption. This proactive approach allows brands to stay ahead of trends and align collections with evolving consumer demands.

Companies like Heuritech use advanced AI to analyse millions of social media images and identify subtle shifts in consumer preferences, helping brands like Louis Vuitton (since 2020) stay ahead of the curve in luxury fashion. By decoding patterns in emerging styles, colours, and materials, the platform enables designers to make data-backed decisions that resonate with their audience before trends peak.

Similarly, EDITED applies AI to vast datasets of e-commerce activity, tracking everything from sell-through rates to competitor pricing. This granular analysis allows brands to set optimal pricing strategies for new collections, ensuring a competitive edge while maximising profitability in a rapidly changing retail landscape.

US fashion house FINESSE combines AI algorithms with votes from their online community to produce the most appropriate styles, in the correct volumes, for their customer base.

Material Selection: AI-powered material selection tools are reshaping how designers and manufacturers approach sustainability and innovation. By analysing parameters like durability, environmental impact, and compatibility with design requirements, these systems recommend materials that balance cost-efficiency with creative vision. This ensures that each choice aligns with both the product’s function and the brand’s sustainability goals, often encouraging sustainable practices and reducing material experimentation costs.

Finnish company Spinnova, who developed pioneering sustainable textile solutions by creating fibres derived from wood pulp and other waste materials, use AI to optimise material development, ensuring that production processes are both efficient and scalable.

Nanoloom is revolutionising fabric and knit design by using advanced AI algorithms to optimise fibre combinations and predict the precise properties of the resulting materials. By intelligently analysing the ratios of different fibres, Nanoloom’s systems can create blends with tailored characteristics, such as strength, flexibility, and texture. A key innovation lies in the manipulation of graphene nanomaterials, with AI-driven insights that enhance the fibres’ performance. 

Production Process Automation: AI-driven robotics and automation systems are redefining the fashion manufacturing landscape by streamlining repetitive tasks, such as cutting, stitching, and assembly. These innovations not only increase throughput but also ensure that production is faster and more cost-effective.

Vision-based AI systems are central to this transformation, guiding robotic arms with remarkable precision. These systems analyse digital designs, translating them into exact movements for cutting machines and sewing robots, ensuring each piece is produced to exact specifications. The integration of deep learning algorithms allows robots to adapt and refine their processes over time, improving efficiency with each cycle.

The result is a significant reduction in human error, as robots execute repetitive tasks with consistent precision. This not only enhances the overall efficiency of production but also reduces labour costs, making manufacturing processes more scalable and cost-effective. The increased automation also offers the potential for faster turnaround times, allowing brands to meet demand more swiftly without compromising on quality.

Sewbo is addressing the growing challenge of labour shortages in the garment industry by automating the sewing process with its innovative robotics. By combining AI with robotics, Sewbo’s system is capable of autonomously stitching garments with minimal human intervention, making it a vital solution for manufacturers facing workforce gaps. This technology not only reduces the reliance on skilled labour but also ensures that production processes remain consistent and efficient.

A significant challenge with this type of technology is the high cost of implementation, as well as understandable resistance from traditional craftspeople and those currently employed in garment production.

AI’s potential to replace creative roles 

In the evolving world of fashion, AI’s growing involvement in the creative process has sparked a fundamental debate: can a machine truly replace the ingenuity of human designers, or will AI remain a powerful assistant? As AI continues to improve in generating designs, it is important to understand the scope of its strengths and limitations, particularly in relation to its potential to reshape creative roles.

AI excels in areas where speed, volume, and pattern recognition are key. By processing vast datasets, AI systems can identify emerging trends, predict consumer preferences, and generate large quantities of design variations in a fraction of the time a human designer might. This efficiency allows brands to rapidly iterate on designs, refine concepts, and experiment with new combinations. Tools like generative design software have already demonstrated AI’s ability to optimise pattern-making and even explore aesthetics that might otherwise be overlooked. These capabilities are invaluable in fashion, where staying ahead of trends and market demands is crucial.

Despite these advantages, AI still struggles with areas fundamental to creative work: storytelling, emotional resonance, and the deep, cultural understanding that drives brand identity. While AI can create designs, it lacks the emotional intelligence required to weave these into a narrative that resonates with audiences on a human level. In the fashion world, where brand identity is intricately woven into the fabric of storytelling, AI’s limitations become glaringly evident. 

Fashion brands are built on a delicate balance of history, tradition, and identity—elements that AI, no matter how advanced, cannot currently fully understand or replicate. AI-generated campaigns may be able to produce visually compelling content, but without human oversight, they risk missing the intricate subtleties that imbue a brand’s story with authenticity and emotional resonance. This is why human intervention remains crucial in the creative process, ensuring that the final narrative reflects not only the visual aesthetic but also the core values and cultural nuances that make a brand unique.

As AI plays an increasingly prominent role, the ethical implications are profound. The potential for job displacement in creative industries is a real concern—could AI replace designers entirely? Beyond job losses, there is the question of what it means for creative expression. Can machines truly create art, or is creativity an inherently human quality that should not be outsourced to algorithms?

Rather than replacing human creativity, AI is more likely to serve as a powerful ally. In hybrid workflows, AI can handle the technical aspects—generating variations, predicting trends, or streamlining the design process—while human designers bring the vision, emotion, and narrative that make designs truly stand out. This collaborative model could herald the future of creative industries, where AI enhances, rather than replaces, the human touch.

Sustainability and resource efficiency 

Sustainability and resource efficiency are becoming cornerstones of the fashion industry’s shift towards more responsible practices, and AI is playing a pivotal role in driving this transformation. With growing pressure from both consumers and regulators, brands are increasingly relying on AI to optimise their resource use and reduce their environmental impact.

One of the most significant ways AI contributes to sustainability is by forecasting demand more accurately, thereby reducing overproduction and excess inventory. Through the analysis of vast amounts of sales and consumer data, AI tools help brands predict trends and consumer preferences with remarkable precision. This not only prevents waste but also ensures that supply chains remain lean and efficient. For example, Hyran uses AI alongside point-of-sale data to optimise production volumes and profitability for brands, balancing the fine line between meeting demand and minimising unsold stock.

AI also plays a key role in evaluating the environmental impacts of various materials, offering insights into more sustainable alternatives. By assessing a material’s lifecycle—from sourcing to disposal—AI can recommend the most resource-efficient options for production. In some cases, it can even suggest new materials that have a lower environmental footprint. This helps brands reduce their reliance on non-renewable resources, creating more eco-conscious products.

AI is also integral to advancing the circular economy, by enabling better recycling and upcycling processes. Machine learning algorithms are being used to sort and identify materials that can be recycled or repurposed, thus reducing the reliance on virgin resources. By automating these processes, AI ensures greater efficiency in material recovery, encouraging a closed-loop system where waste is minimised and resources are continually reused.

Customer engagement and personalisation

Artificial intelligence has created a raft of new opportunities in terms of how fashion brands engage with consumers, offering highly personalised experiences that resonate on a deeper level. By leveraging consumer data, AI tools provide tailored recommendations, virtual interaction opportunities, and efficient support, ensuring a more seamless shopping journey.

Personalised Recommendations: Brands and retailers have begun to utilise AI to curate outfits based on individual preferences. For instance, Zalando’s ChatGPT-powered fashion assistant enables users to describe their desired looks in natural language, generating outfit ideas based on weather, events, and even mood. This conversational AI model is accessible across its European markets, creating a personalised shopping experience for millions of users. 

Sociate AI have introduced a tool that enables shoppers to search retail websites through a conversation with a chatbot and find personalised results in real-time, regardless of the original state of the website’s metadata or product attribution. 

Virtual Fitting Rooms and AR Try-Ons: AI-driven technologies, such as Zalando’s virtual fitting room pilot, allow customers to visualise garments on avatars built from their own measurements. Partnering with Levi’s, Zalando offers tools for accurately fitting items like jackets and jeans, reducing returns by up to 40% in initial trials. Such innovations address both consumer satisfaction and environmental impact by mitigating waste from returns.

Similarly, Google introduced a virtual try-on tool last year utilising generative AI that enabled online shoppers using the Google search tool to see how items would look on a range of models with different sizes, ethnicities and body shapes.

Enhanced Customer Interactions: AI chatbots, such as the My AI tool integrated into Snapchat, not only assist with technical queries but also provide styling advice. These chatbots create engaging, interactive experiences, enabling consumers to visualise products in real-time through augmented reality, offering brands an opportunity to connect with tech-savvy audiences.  Through my role at the Fashion Innovation Agency, I worked on a project in collaboration with MIcrosoft that combined scanning the items in your wardrobe through mobile-phone-based photogrammetry with state of the art natural language processing (NLP) to produce a Digital Human Stylist. This stylist would integrate with your calendar of upcoming events and the weather for your region to make styling suggestions for outfits using the entire breadth of your wardrobe and could be refined and directed using speech as an input.

Limitations of AI within the DPC workflow

While AI holds immense potential for digital product creation in fashion, there are still some significant limitations that hinder its full integration. As mentioned earlier, one prominent challenge lies in creativity and cultural nuance. AI excels at generating designs based on patterns and trends but often fails to capture the emotional resonance or culturally specific elements that underpin truly impactful fashion. For instance, a garment inspired by traditional Japanese aesthetics might require a nuanced understanding of cultural symbolism—something an AI trained on global datasets might overlook or misinterpret. This can result in products that lack authenticity or even risk cultural appropriation. 

Another critical issue is dataset biases. AI tools rely on training data, which, if skewed, can perpetuate narrow aesthetic norms or exclude diverse perspectives. This has been highlighted in instances where generative AI models reinforced Eurocentric beauty standards or failed to account for body diversity in virtual fitting tools. There are multiple stories online about AI biases perpetuating stereotypes or excluding groups of people, including failing to represent diverse skin tones, reinforcing unattainable beauty ideals, and perpetuating stereotypical beauty aesthetics.

Additionally, the high implementation costs of AI pose challenges, particularly for small-to-medium enterprises (SMEs). Advanced AI systems require substantial investments in hardware, software, and skilled personnel, making them accessible predominantly to larger brands. A recent McKinsey report highlights that while AI adoption in the fashion industry is growing, it is particularly prevalent among large-scale enterprises, with over 30% of major fashion brands utilising AI for various functions. However, the uptake among small-to-medium enterprises (SMEs) remains low due to challenges such as high implementation costs and limited access to talent and technology. This disparity underscores the financial and operational barriers SMEs face in integrating advanced AI solutions, particularly those requiring significant investment in infrastructure and expertise.

Future directions and emerging trends

The future of AI in fashion’s digital product creation workflow is defined by a delicate balance between automation and human creativity, alongside groundbreaking technological advancements that promise to reshape the industry.

AI is poised to complement, rather than replace, human-led processes. Designers increasingly leverage AI for initial ideation and rapid prototyping, delegating repetitive or time-intensive tasks like fabric simulation or colour-matching to machines. For instance, platforms like CLO3D allow AI-powered visualisations of garment concepts, which designers can refine manually to ensure creative intent is preserved. This synergy enables designers to focus on artistry while benefiting from AI’s efficiency.  Similarly, sketch-to-image and image-to-video tools utilising generative AI can allow designers to get a compelling visual output from even a basic sketch, producing video outputs that show increasingly accurate representations of textures and fabric movement, allowing them to quickly iterate and refine their ideas.

As AI automates routine tasks, the role of workers is shifting toward managing technology and interpreting AI-generated insights. This evolution could demand retraining initiatives, ensuring traditional skillsets adapt to a tech-centric workflow.

AI has the potential to democratise access to professional-grade design tools, empowering emerging designers and SMEs to compete with established brands. Tools like Generative AI in Canva already allow individuals to create sophisticated designs without extensive technical expertise, foreshadowing a more inclusive fashion ecosystem.

Outlook

Artificial intelligence is undeniably transforming digital product creation in fashion, offering unparalleled efficiency, precision, and scalability. From automating mundane tasks to enabling hyper-personalised consumer experiences, its impact is vast. However, as has been discussed, AI cannot currently replicate human creativity, cultural sensitivity, or emotional depth—qualities at the heart of fashion design.

The future lies in a harmonious collaboration between AI and human talent, where technology enhances, rather than replaces, creative workflows. With continued innovation and a commitment to inclusivity and ethical practices, AI holds the promise of democratising design and reshaping the industry for the better.