Released in The Interline’s new Solution Spotlight, this is the second in a 13-part series examining where fashion is looking to invest in technology, process by process, and the maturity level each stage has reached.
Download the full Solution Spotlight for a deep-dive on SHIMA SEIKI (our sponsor for this instalment), Kalypso and Style3D Assyst (also featured here) and to discover other technology vendors who are supporting one or more vital fashion functions in 2026-27.
Fashion, in 2026, has a fractious relationship with 3D – one that we believe is born out of short-termism and a miscalibration of what Digital Product Creation (DPC) tools and workflows were intended to accomplish.
The Interline has, for several years, documented the different ways that fashion companies have embraced 3D, and the different reasons behind those initiatives. They have been as idiosyncratic as the brands behind them, and we encourage readers who are interested in investing in DPC to read the most recent edition of those reports: The DPC Report 2026.
As heterogenous as those strategic pushes have been, though (from creative visualisation to finite technical engineering to downstream content creation) they all shared a common vision: the idea that, irrespective of where 3D found its foothold in the enterprise, its value would eventually be so self-evident that its utility would increase and transcend disciplines, and every product lifecycle function would benefit from being able to refer to a single 3D asset as the complete, accurate, extensible digital twin of the physical good.
This vision is still, from our vantage point, as valid as it ever was. But it would also be naive to ignore the fact that the different steps along that pathway are now being challenged by generative AI.
For example: if a company adopted 3D design and visualisation as a way to shorten the distance between a creative professional having a product idea and them being able to communicate that idea to colleagues and external partners, then that workflow is no longer a “slam dunk”. Instead, generative image models, and the workspaces built on top of them, offer a potentially much quicker, cheaper route to a visual instantiation of a product idea – and one with a much lower, arguably non-existent, skill floor.
By contrast: if a brand bought into 3D as a way of codifying and improving fit, reducing sample iterations, eliminating waste, and building towards a fully integrated, end-to-end digital lifecycle… that investment will still feel directionally correct, even if the investment required to see it through will be competing against the budget demands of AI.
Or, to put it another way, digital product creation has entered 2026 in the grip of an identity crisis — under pressure to justify itself against a newer, faster, cheaper technology (even as the limitations of that technology become clearer). Writing in our DPC Report 2026, ASOS’s Nick Eley called this idea of ‘3D for everyone and everything’ the defining mistake of the first DPC wave: tasking 3D with all of the heavy lifting, and misunderstanding where it actually creates value and for whom.
Fashion, in 2026, has a fractious relationship with 3D – one that we believe is born out of short-termism and a miscalibration of what Digital Product Creation (DPC) tools and workflows were intended to accomplish.
But this framing is also over simplistic, and companies that are evaluating 3D tools today, in the second half of 2026, are likely doing so because they understand something fundamental: generative AI can only take them so far, and the future of everything from internal visualisation, partner communication and production, and external-facing content will rely on something that AI, as a direct result of its architecture, struggles to provide. Accuracy, consistency, and a source of the unimpeachable data that’s required to turn a visual representation of a product into a final physical sample.
As a consequence, the companies looking to buy into 3D, or to continue to build their digital product creation ecosystems, are seeking vendors who lean in one of two directions – they either prioritise component, operation, and material accuracy to the exclusion of almost everything else, or they offer 3D pipelines that incorporate AI, either as an aid to engineering and pattern creation, or as a way of enhancing the visual presentation of 3D assets without extensive staging work, or without creating physical samples to then be scanned in afterwards.
The upshot of this is that the notion of 3D vs. AI is based on both a false distinction (it is highly unlikely that any company will fully commit to one and not the other) and on a widespread misunderstanding of the difference between a simulation and a visualisation. The snappiest encapsulation of that misunderstanding we have encountered is this: the photographs of a meal contained in a menu are not the same thing as the recipe required to create that meal, and the ability to take that photograph is not equivalent to the talent required to make the dish.
There is not sufficient space in this Spotlight to truly dissect the difference between an approximation of the visual output of a simulation engine and that engine itself, but the fashion brands continuing their commitment to 3D and digital product creation understand this distinction – and in our opinion they are likely to emerge ahead of the companies that have either downsized or eliminated their 3D functions.
If we consider the vendors who are featured in this category, this understanding is also the reason that simulation engines for deformable materials exist as part of the same pipeline as generative video models – because both serve part of a single purpose, which is to digitally transform as much of the product journey as possible.
This hybrid pipeline is fundamental to Style3D’s offering, which includes claims of both providing a foundational technology for Physical AI, and 99% accuracy between virtual sample and physical fit – all alongside tools that shorten the distance between 3D renders and generative images and video based on those accurate foundations.
Similarly, if AI was truly able to deliver a producible garment, would we see continued investments in true, 1:1 virtualisation of complex processes such as knitting? Or is it more likely that there remains significant value in being able to directly drive production hardware through knit programming, even if the lifestyle shots of the eventual knitted product that make it to the product detail page are either wholly generated by, or enhanced with, AI?
This is the bet that SHIMA SEIKI is making with its “Total Fashion System,” which is built on precisely that direct connection between virtual sample and physical product – with the vision of increasing speed, driving down waste, and dramatically improving the sustainability profile of production.
According to data contained in our AI Report 2026 these kinds of hybrid workflows are fast becoming the norm. Across fashion, AI is seen as being the most mature and the most market-ready at the beginning and the end of the product journey (in creative design and in downstream marketing and content creation, as enumerated in the AI section of this Spotlight) but the centre of the sandwich is correctly seen, by real professionals, as a space where other tools are potentially much better suited to bridging the gap between concept and production.
There are, of course, entire categories of fashion where those two extremes of the product lifecycle matter more than the middle. In value-driven, fast, and mass-market fashion, the inspiration and the image make up more of the overall story than the science of fit, or the multi-faceted story of materials. In those industries, AI is set to make deeper inroads into territory that has traditionally been owned and occupied by 3D, and within those well-delimited lanes this feels, to our team, like a logical and natural evolution.
But for companies where technical accuracy is as important, or more so, than visual representation, there is highly likely to be a strong place for 3D in 2026 and far beyond. That place will, of course, be reliant on more than just the vendors represented in our Spotlight, though. From fabric digitisation platforms to size-accurate avatar libraries and bills of materials, 3D alone will not deliver against the original vision. Instead, investments in the core technology will need to be backed by commensurate investments in the wider ecosystem and in the organisational change required to do that ecosystem justice.
This is, not by coincidence, something that the providers of 3D ecosystem tools and platforms understand. From the vendors shown here, to the wider landscape, brands will find almost every vendor willing to make their solutions part of the kind of whole-enterprise change that’s still going to be required to realise that original vision for 3D.
Because that vision is alive and well, and the companies that recognise that also recognise that buying a single solution to all their challenges is unrealistic, and that the true north for digital product creation is a long-term direction rather than a quick fix – something that readers investing in AI will do well to remind themselves, even as AI continues to carve out its own unique, and complementary, value proposition.
And at the same time, this complexity increases the need for companies to partner with expert advisors like Kalypso, who have sufficient direct domain expertise to understand the value proposition of investing in 3D, as well as how to turn those investments into longer-term build-outs of end-to-end, connected ecosystems.