Released in The Interline’s DPC Report 2026, this executive interview is one of an eight-part series that sees The Interline quiz executives from major DPC companies on the evolution of 3D and digital product creation tools and workflows, and ask their opinions on what the future holds for the the extended possibilities of digital assets.
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For a while now, the broad shape and scope of 3D and DPC strategies have been generally accepted, but now companies are asking some fundamental questions about how far those initiatives should stretch. Some see a clear opportunity to take them further. Others potentially see arguments for either ringfencing them where they stand, or possibly even scaling them back. Technology footprints will always morph over time, but this feels like a deviation from the standard. What’s your perspective?
This shift is healthy. For years, teams focused on simply “getting 3D in place.” Now they’re asking whether it solves the problems that matter, such as cycle time, sampling, waste, and collaboration across teams. Companies who are pushing further aren’t expanding for the sake of it; they expand because the results are real and repeatable. When 3D is accurate, stable, and integrated into the supply chain, it grows naturally. When it is superficial or siloed, it stalls. What we’re seeing is not a question of scaling up or down, but a demand for higher performance. Browzwear meets that demand: we design our technology for daily production decisions, not just visuals. That’s why the most mature DPC programs continue to expand on our platform.
At any point of technology introspection and re-evaluation, it’s important to actually test the business cases and evaluate the returns. Whether it’s time saved, cost reduced, or samples eliminated, what are the metrics that you and your customers use to quantify success? And are those results still holding over time?
The most important metrics are those tied directly to operations: sample reduction, fewer rounds of corrections, shorter development cycles, and cleaner vendor communication. These numbers matter because they show up in time, money, and predictability. Once teams trust the accuracy of the digital twin, they stop relying on physical processes, and efficiencies compound. Customers consistently report fewer physical prototypes, faster approvals, and fewer late-stage surprises. That consistency shows that the technology is achieving precisely what it was designed to do.
The next port of call is to look to future value, and it’s here that we believe that there’s likely to be a fork in the road between 3D for visualisation purposes, as 3D as a viable, feature-complete “virtual twin”. What additional value, across the extended supply chain, do you think could be built from having 3D representations of products serve as those complete virtual twins? And how close are we to that goal of more comprehensive 3D twins?
A virtual twin is a valuable tool in helping the design and evaluation process move from initial style visualization to more and more complete and accurate details. Used properly, the digital twin can be created quickly and easily for early visualization and then take on more and more detail as the product development process continues. Currently, many image generation AI tools exist to aid in the initial visual idea phase. Those ideas need to be quickly translated into more accurate and precise virtual twins for further development. The ultimate expression of that twin is a completely accurate and true to life representation of the style which can be rendered into video or image on lifelike avatars for use as samples or in e-commerce. Incredibly, this can mostly be automated today on Browzwear’s platform. The hurdle that companies need to jump to make this efficient and easy for themselves is to see that the digital process is not an exact replica of the physical process. A digital prototype can start off with nothing but an image of an idea, but then must quickly evolve to something more accurate and realistic. Browzwear’s AI capabilities can enable a design team to avoid technical development of a more robust digital twin until it is necessary for the next step of development. This enables more time on ideation and efficient use of technical resources for the detailed development of those early ideas.
One of the key drawbacks that we hear brands and their upstream partners talking about, when it comes to digital product creation, is the labour-intensive nature of the typical workflow and toolchain. It feels as though so much of 3D work is either re-engineering or restarting, product by product. What potential do you see to meaningfully smooth this onramp out, and to collapse some of the design-to-production timeline through automation and integration?
The tools to dramatically reduce labor intensive work already exist in Browzwear. Automation or repetitive tasks as well as re-use of core assets and templates are key to taking advantage of what is currently available. Much of the inefficiency in digital product creation comes from repetitive work that could be automated or reused across multiple seasons. Many times, teams are essentially starting over for each style, which is time-consuming and error-prone. Workflows where digital assets connect directly with downstream systems—PLM, costing, material management, and production—combined with automation and guided workflows such as intelligent pattern prep, grading, and reusable components, dramatically reduce repetitive work. This does not replace human decision-making; it frees teams to focus on the most important decisions. Over time, these features reduce the time from the point of first sketch to final production, giving processes higher predictability, scalability, and consistency.
What do you see as the near-future balance between 3D and AI? On the surface, it feels like the probabilistic nature of generative models and the full simulation required to do “95% right first time,” are at odds with one another. Are those two things actually in tension with one another? And what will it take to adopt AI in a way that’s not depletive to the objectives of digital product creation, and that makes practical sense considering the current capabilities of generative and non-generative models?
There is no conflict if we define the advantages of AI and 3d in terms of how to best achieve getting great products into the market quickly, easily, sustainably, creatively, and with a high level of technical precision and accuracy on a brand-by-brand basis. AI is currently amazing at quicky helping to generate ideas. It is not currently amazing at precision and accuracy or at analyzing fit or making judgements a human might make on these issues let alone what is best for a brand and the trend in the market. AI is really only as good as the person who is prompting and evaluating it. 3D is similar in that way. Creating a virtual twin is fast and easy for a digitally mature organization with a library of blocks, real fabrics, trims, avatars, rendering templates, and experience with trusting and approving twins to quickly take initial visual ideas to something more complete as a digital twin and to get a first time right physical prototype back if necessary. Less mature organizations without this foundation and without a trust and process to use digital twins, have to do more work on each style. The mistake in these cases is to not build the foundation with every approved style. At Browzwear we see a similarity between AI and 3D in that companies who take the time to build a foundation of assets will accelerate their advantage with these technologies as they evolve. While those who just use them as one-time items will continue to have to create them as one-time items and will not unlock the larger benefits for their organizations.
At Browzwear, our vision is to enable the development of an “Idea to Twin in Minutes.” To achieve that, we see value and advantages in both 3D and AI based on a strong foundation of assets that include patterns, blocks, fabrics, trims, avatars, poses and a variety of visual standards that enable fast and efficient decision making for each stage of the product development cycle.
What’s the most useful question that companies can ask themselves, right now, to better understand what they want to accomplish next with 3D – whether that’s driven by their own ambitions, or by changes in the market?
The most productive question is: “Where is uncertainty or inefficiency costing us the most?” That could be excessive sampling, delays in approvals, misalignment with vendors, or repeated corrections. Locating those areas highlights areas where 3D can truly make an impact. The next step is tangible: focus on outcomes that improve accuracy, reduce re-work, and integrate into existing workflows. Consider where virtual twins serve as a single source of truth, where AI speeds up repetitive processes, and where digital assets weave seamlessly into downstream tools. Companies that take this approach don’t just adopt technology—they tie it to the problems that truly matter, creating measurable improvement across the entire product development process.
