The Next Frontier For Digital Product Creation: Patternmaking With AI Assistance

When I look back at how far the digital transformation of fashion has come, it’s clear that what we now think of as “digital product creation” didn’t actually start with 3D visualisation, simulation, or automation. It began decades earlier with the quiet digitisation of the pattern room. 

If you’ll indulge me a brief walk down memory lane: the earliest computer automated design or CAD systems in the 1980s and 1990s were revolutionary back then, in the same way that 3D has felt for a lot of the younger readers of reports like this one over the last decade. The move from manual drawing to 2D CAD allowed patternmakers to trace, grade, and mark patterns on screens instead of on paper. For those of us who lived through that shift, it wasn’t glamorous—it was a matter of survival – but it was fundamentally different from anything that had gone before, and those of us on the ground also had the opportunity to not just learn on the job, but to actually influence the development of the software. 

At the time, we needed to adapt to screens, plotters, and software that didn’t always understand what we were trying to make (this was partly due to the global transfer of manufacturing, and patternmaking being outsourced) but we also had the opportunity for our expertise to make its way into technology products. And that first wave of digitalisation then laid the groundwork for a lot of what we’re seeing in Digital Product Creation today – both in terms of what fashion wants to accomplish with technology, and from the perspective of how software tools need to meet the real needs of specialist professionals in order to create value.

The goal has stayed consistent between then and now: accuracy, speed, simulation, and better communication between design and production. What’s changed is that now the existing technology is catching up to creative intent and an incredibly high bar for technical precision, at the exact moment that a new technology wave (generative AI) is heading for us.

From a patternmaker’s point of view, 3D has already become something close to a universal design language, enabling real-time global collaboration in modern design rooms. Digital patterns won out over physical drawing, because they can be instantly simulated and even fit-approved, speeding up timelines, reducing waste, and shoring up the pipeline for converting creative intent into finished products. 

Now we’re approaching what both long established patternmakers and the new crop of 3D and DPC-native designers and garment technicians will recognise as the next turn: AI patternmaking. And while, at first glance, those words will feel like a threat, I believe there’s now the same opportunity for the people who’ve helped shape two generations of digital product creation to also inform and steer the next one.

With the combination of 3D simulation and AI tools that seems to be the standard for the next wave of capabilities being added to existing platforms, the objective isn’t, I don’t believe, to replace patternmakers any more than the shift from manual drawing to 2D CAD was. Instead, it’s a new window of opportunity for specialists to define the rules for a new set of smarter systems, and to inject decades’ worth of expertise into the code that will both preserve and protect their knowledge, and provide new tools to assist them in coping with the volume and velocity of work that’s coming their way.

There’s beauty in this idea, but also responsibility on the part of patternmakers, software developers, and brands. If we, together, teach these systems poorly, unbalancing the equilibrium of art and science that informs patternmaking, then the AI tools that will increasingly make up the tech environment will repeat our mistakes. If we train them thoughtfully, they should amplify our strengths. 

This isn’t a new idea, because it’s also the way things worked in the transition from 2D to 3D. One constant, primary challenge in 3D workflows has been the pattern itself, which is simultaneously the foundation and the finish line for a garment. Even with the best simulations, fabric scans, or high-quality renders, an inaccurate pattern can throw off the whole process. Garments might not fit avatars, proportions could be off, and the intended style may be lost. This is why patternmakers and owners have been (rightfully) positioned as the gatekeepers for determining what constitutes a complete “digital twin” versus 3D assets that are better suited to being storytelling content or visualisation exercises.

The promise of AI patternmaking systems – whether they’re standalone or, now part of the software suites that the DPC community already relies on day-to-day – is to give those gatekeepers new sets of keys, and to automate the repetitive, non-value-add tasks—drafting, measuring, adjusting, and re-drawing – so that patternmakers can concentrate on what matters most: refining, validating, and solving creative challenges.

It’s important for me to note that I’m not talking about theory here. AI patternmaking may not be proven and stress-tested the way that 3D design and simulation has been, but it is already available. Today, in real time, brands can digitise their entire legacy pattern archive or create a fit block system with generative AI. From there, companies engaged in these pilots can analyse measurement deviations and cluster similar fits together, can easily learn valuable analytical data from across their products, collections, and sizes, and have a new opportunity to reveal the evolution of their brand’s fit identity, and to set new, higher bars for customer satisfaction.

On the other end of the spectrum, independent designers are also already using pattern automation tools to start their first collections. With nothing more than an avatar, a vision, and a prompt to the right tool, they can create patterns and visualise collections before they sew a single sample. 

For professionals in both cohorts, AI pattern tools are removing limitations and, in the same way that the shift from 2D to 3D did, allowing them to iterate, test, and refine ideas. Deployed correctly, AI pattern tools can be a gateway to understanding the logic of construction just as 3D tools have opened up different horizons and disciplines for designers.

And again, just as the move from 2D to 3D did, AI patternmaking is promising significant speed increases in a market that sorely needs them. Instead of spending hours manually building a pattern draft, designers can create, adjust and visualise results on an avatar in minutes.

I recently chatted to Sylwia Szymczyk, a prominent figure in the DPC community who’s now building her own AI patternmaking tool, and she told me that the objective of AI here is to speed up and streamline digital product creation, rather than to sidestep it, and to help push the industry further along a trajectory it’s already on, rather than redirecting it.  

“There is something more important behind all of this — to finally make the fashion industry what it should be: a place where creativity thrives and garments really fit people”

But something else Sylwia said also resonated with me: a reminder that the early stage of the adoption / diffusion curve for software is also the opportunity to influence its direction, and a wake-up call to the fact that how this will manifest itself in reinforcement learning for AI systems might follow a familiar trajectory, but it has the potential to happen at an unprecedented speed. Each validated pattern, fit-tested block, and minor correction can, effectively, add to a dynamic AI resource that gets smarter with every project – retaining successful garment elements, avoiding past issues, and integrate each new garment into a broader, more intelligent pattern narrative that, over time, becomes unique to each brand.

But this conversation – and others I’ll quote from in a moment – also spotlighted an essential truth: no matter how advanced the tools become, or how much pattern history they ingest, the fundamentals of craftsmanship, and the demand for specialist curation, will remain the same. The people who get the real results from AI patternmaking, as integrated into the digital product creation ecosystem, will still need to be patternmakers.

They will still need an eye for proportion. They will still need to feel balance and shape. They still need to understand fabric behaviour and human movement.

Automated systems might calculate fit, but they cannot perceive it. They lack the ability to recognise elegance on a runway, or to understand the comfort experienced by a customer. Subtle design changes and their expressed impact remain within the design and development team’s domain, and in this respect the roll-out of AI could be a dual-edged sword in the sense that it promises speed and efficiency in the medium term, but it requires the input and the curation of an incredibly scarce pool of hybrid traditional / digital talent in the here and now.

When you see seasoned patternmakers interact with both digital and physical prototypes, they are the experts in spotting subtleties in drape, balance, and proportion that algorithms miss. In a discussion I had with Matt Bakhoum, who’s the Director of US Operations for DPC vendor Style3D, about their AIGS platform, this theme came up again: that digital product creation has created bottlenecks that AI has the potential to loosen, provided it’s deployed in the right places and that it recognises the value that patternmakers bring to unifying the product journey:

“The clearest win for us was in fixing this broken link between design intention & production.”

For the team at Style3D, the approach with AI is to produce designs that require technical review by specialist patternmakers before becoming wearable garments. The initial AIGS platform draws on a library of existing 3D blocks, tagged with brand-specific metadata, which designers can then modify. Most large brands and retailers can integrate their own 3D catalogues, and the objective is to streamline the path from design concept to 3D asset with minimal setup. 

As well as brand pilots and live deployments, AI patternmaking is also set to have a significant impact on the way traditional skills and DPC proficiencies are taught in education.  After years of making digital skills optional to acquire, institutions now seem to be recognising that students should learn, early in their education, 3D and AI as their first design-language, not as an elective. 

While traditional patternmaking remains indispensable, digital tools can significantly enhance the learning process and also prepare students for the realities of work. For example, teaching measurement logic alongside real-time demonstrations of how changes affect drape on a 3D avatar accelerates intuition and bridges the gap between conceptualization and outcome. Early adopters will enter the industry fluent in digital product creation, and prepared to interact with AI as discerning partners, rather than as people blindly prompting a system they had no say in the development of, and that they have very limited understandings of the inner workings of.

This evolution is not solely about increasing the speed with which brands and suppliers can onboard new talent following the next turn in digital product creation; it also enhances comprehension. The objective is to enable new patternmakers to understand the underlying reasons for design decisions, rather than merely learning the mechanics of drafting patterns, so that, as intelligent systems take on more of the technical heavy lifting, designers and patternmakers are freer to focus on innovation, narrative, and the artistry that sets great fashion apart – safe in the knowledge that they can actually trust what AI is bringing to the DPC pipeline.

Large brands should, then, be looking to these AI advances not as a way to supplant patternmakers and garment engineers, but as a method of ensuring consistency, accelerating sampling even further than they already have with DPC, and strengthen their product pipelines. Independent designers should gain access to powerful tools that let their creativity shine without technical barriers. In education, digital patternmaking will become as fundamental as the dress form, giving the next generation of makers a head start in both logic and intuition, potentially with an AI assistant by their side.

This diversity is also likely to see multiple technology players co-existing in the AI patternmaking space, the same way that the 3D ecosystem has expanded. As well as fitting into existing tools, I don’t expect that we’ll see a single de facto AI patternmaking application emerge, because the DPC field encompasses so many distinct approaches already. 

One company that knows this well is CLO Virtual Fashion, which has released several AI Tools over the past few releases of their 3D portfolio, including the AI Studio Pattern Drafter. Patterns are generated from prompting with image upload, or by the user entering specific pattern data that is then translated to a 2D pattern, with AI creating instant sewing relationships.

Joon Lee, who leads Brand Communications at CLO, had this to say about how these features are organically becoming embedded into the tools that designers already rely on:

“At CLO, our core focus has always been developing features that truly aid the designer, and our latest AI-enabled tools reflect this commitment. While these features in the latest releases may feel really new to some of our users, we have utilised AI and machine learning within CLO for many years. Tools like auto arrangement, nesting, and digital fabric creation have been smartly automating processes behind the scenes for designers & patternmakers at brands, vendors and suppliers.” 

This is also a philosophy that others share: the need to ensure that the next turn in digital product creation safeguards the voice of patternmakers at the same time as making it easier for professionals to take advantage of the potential that exists inside software, but that isn’t immediately apparent how to access.

“Using AI to get software interfaces out of users’ way and including more people in the process by lowering barriers to entry – is central to our long-term vision” says Matt Bakhoum of Style3D. “If we can make the lives of those users easier by making the lift a bit lighter, or empowering one user to achieve more – that’s what we’re striving for.” 

But when it comes to bringing AI into the fold, and into the solutions that designers and patternmakers already use every day, usability is only part of the puzzle: one of the most pressing issues is trust. It’s fair to say that while 3D faced an uphill battle when it came to convincing different departments that what they saw was what they were going to get, AI is likely to face an even more pitched fight because of both the public discourse around AI, and because of the prevailing lack of clarity around how generative models really work. And on that basis, I think it’s essential that we, as patternmakers, play an active role in determining where AI becomes part of the design and engineering workflow, so that it can be deployed in a way that serves the specific needs of our craft, and the specific objectives of our brands.

“Legacy-based, brand-specific generation; This is what we do” says .” Sylwia Szymczyk. “AI patternmaking is for companies that already have strong blocks, and for professionals that already care deeply about continuity and fit.”

Looking ahead, then, I don’t expect machines to replace patternmakers. Instead, I see patternmakers having a unique chance to shape, direct, and optimise the next step in digital product creation.