The dual-arm machines can recognise, pick and sort products without requiring item-specific training or predefined stock-keeping unit (SKU) data. This capability addresses a longstanding challenge in AI-powered fashion returns, where garments arrive in different conditions, packaging formats and combinations that make conventional automation difficult.

The deployment follows a Robotics-as-a-Service (RaaS) model, enabling warehouse personnel to concentrate on monitoring operations, managing exceptions and maintaining quality standards.

AI System Handles Unfamiliar Products

Sereact’s Cortex physical AI platform supports multiple robotic configurations, including single-arm picking systems, dual-arm return-handling units and humanoid robots. The platform draws on operational experience across its robotic systems to improve its ability to recognise and handle unfamiliar objects.

Chris Walton, SVP Contract Logistics at CEVA Logistics, said: “One of the toughest challenges is returns handling, due to its extreme operational nature and scale, unpredictable volumes, manual quality checks, seasonality, value decay, and a disproportionately high cost per unit.”

“Our Robotics-as-a-Service model classifies items faster, reduces manual touchpoints, and delivers consistent, predictable quality on a large scale. For our customers, this transforms returns from a cost driver into a controlled, high‑performance service.”

Zalando Expands Robotics Collaboration

Zalando participated as a strategic investor in Sereact’s Series B funding round in July 2026. The companies are also working together to expand intelligent automation throughout their European logistics networks.

Masood Choudhry, SVP Logistics at Zalando, said: “By solving the most complex challenges in modern fulfilment today, we are actively shaping the future of e-commerce logistics for fashion and lifestyle,”

Returns represent a substantial operational burden for online fashion retailers. Zalando reports that approximately 50% of its online sales are returned, making efficient processing essential to controlling logistics costs and maintaining product value.

By automating labour-intensive sorting tasks, AI-powered fashion returns technology could help retailers improve processing consistency, reduce manual intervention and manage fluctuating return volumes more effectively.

The “PX Pulse” survey, conducted by first-party data company Dynata among 1,000 US consumers aged 18 and over, paints a striking picture of eroding consumer trust and evolving shopping behaviour. Only 32% of respondents said they completely or mostly trusted retailers to offer a fair or competitive price. Meanwhile, 59% said price mattered more to them than it had six months prior — a clear signal that retail pricing is under greater scrutiny than ever before.

Shoppers Are Waiting, Watching and Comparing

The way consumers time their purchases has changed noticeably. Nearly four in five respondents — 79% — said they had postponed a purchase because they believed the price might drop later. This kind of deliberate, wait-and-see approach underscores just how central retail pricing strategy has become to the overall buying decision.

Price comparison is now a standard part of the shopping journey. Some 46% of respondents said they compare prices across multiple retailers before making a purchase, while only 9% said they typically buy without any form of comparison. The gap between these two groups tells its own story about where consumer expectations now stand.

Cross-channel price visibility is also intensifying. As many as 77% of consumers said they had noticed the same product listed at different prices across retailers or shopping platforms within the past year. An additional 68% said they at least sometimes check a retailer’s website or app while shopping in-store to determine whether the same item is available for less online. This blending of physical and digital shopping channels is making price inconsistencies harder for retailers to obscure.

Personalised Pricing Raises Consumer Trust Concerns

The survey also shed light on growing unease around personalised or behaviour-based pricing. Some 57% of respondents said they would trust a retailer less if they discovered that a product’s price had been adjusted based on their personal information or browsing and shopping behaviour. This finding highlights a key tension retailers must manage: the desire to leverage consumer data for pricing optimisation against the risk of alienating shoppers who view such practices as unfair.

AI Shopping Tools Are Reshaping Price Discovery

Perhaps the most forward-looking finding in the survey is the rising role of AI shopping tools in the consumer price comparison process. A notable 24% of consumers said they already use AI-powered tools — such as ChatGPT or Google Gemini — to compare prices or identify the best deals. Further, 56% of respondents said they trust these AI tools to provide accurate pricing information when evaluating products across different sellers.

This growing reliance on AI shopping tools is expected to carry through into the holiday shopping season. When asked how they plan to compare prices during that period, 40% of respondents said they would compare retailer websites directly, 37% cited search engines, and 24% said they expected to use AI tools such as ChatGPT or Google Gemini. The data suggests that AI-powered price discovery is no longer a niche behaviour — it is becoming a mainstream part of how consumers approach retail pricing decisions.

What This Means for Retailers

Akeneo CEO Romain Fouache addressed the implications directly: “Consumers are paying closer attention to price and that raises the stakes for retailers. Pricing can no longer sit in a silo from the rest of the product experience. Consumers, and increasingly AI-powered shopping tools, are constantly comparing products, prices and offers across channels. Brands need trusted product and pricing information working together so shoppers see a consistent, credible experience wherever discovery happens.”

His remarks reflect the broader challenge the survey data presents: as consumer trust in retail pricing weakens and AI shopping tools become more embedded in the price comparison journey, retailers must ensure that their pricing is not only competitive but also consistent and transparent across every channel where their products appear.

With holiday shopping on the horizon and price comparison habits firmly established among the majority of US consumers, the pressure on retailers to get their pricing strategies right — and to present that pricing in a way that consumers and AI tools alike can trust — has never been more pronounced.

Scalability challenges, labor costs, and a deeply fragmented domestic supply chain remain the defining obstacles. Yet the conversation also revealed a growing consensus: technology — from AI-driven design tools to advanced factory automation — may be the only realistic lever capable of making fashion onshoring work at any meaningful scale.

The Real Cost of Long Lead Times and Offshore Dependency

Ashley Stickler, Vice President of Marketing and Partnerships at CreateMe Technologies, Inc., framed the issue in terms of the fundamental economics of the current production model. With lead times stretching to 19 months and forecasting locked in well in advance, the pipeline brands rely on is slow, cumbersome, and deeply tied to inputs from overseas markets that take considerable time to produce and ship.

According to Stickler, the economic value of a garment produced offshore is already diminished by the time it arrives on U.S. soil, simply because of how much the market has shifted during that long journey from concept to shelf. “Trends have shifted. We’ve moved on,” she noted.

CreateMe Technologies directly addresses part of this problem. The company automates the manufacturing process for apparel and similar products, replacing traditional thread-based construction with digital adhesives to enable faster, more localized, on-demand production — a model better suited to the speed requirements of today’s fashion market.

Stickler was direct about why simply transplanting the Asian manufacturing model into the United States is not a viable solution. The labor cost structure that underpins large-scale garment production in countries like China simply does not translate to a market like California. In her words, the math only works if the entire system is redesigned from the ground up — a shift she sees factory automation making possible by removing labor as the primary cost variable.

AI’s Role in Smarter Production Decisions

Keith Hoover, President of Black Swan Textiles, a digital product development consultancy specializing in textiles, echoed the concern about the fashion industry’s traditional production calendar. The standard practice of forecasting consumer demand 18 months ahead consistently results in either overproduction leading to liquidation, or underproduction leading to lost revenue — both costly outcomes that have long plagued the industry.

Hoover sees artificial intelligence as integral to improving how brands decide what to make in the first place. By using AI agents to analyze readily available data, brands could identify near-term consumer demand more accurately, produce in smaller initial lots, and then scale up or pivot based on actual sales performance. This data-driven approach to production planning could reduce the chronic misalignment between what gets made and what consumers actually want — a core problem that the existing forecasting model has failed to solve.

On the design side, Hoover highlighted how tools like 3D design software are already helping creative teams work faster. Generative AI has added another layer of acceleration, allowing designers to move quickly from a preliminary sketch or even a verbal description to fully visualized concepts ready for merchant review. Hoover was careful to frame this not as AI supplanting the designer’s role but as a powerful tool in the designer’s hands — one that dramatically speeds up iteration and improves the likelihood that a concept will make it into a final line.

The Raw Materials Problem: A Gap Technology Cannot Close Alone

Where the conversation shifted was on the subject of raw materials — and here, the limitations of technology became clear. Hoover laid out the upstream supply chain reality plainly. While the United States is the third largest cotton producer globally, approximately 75 to 80 percent of that cotton is exported offshore. Polyester, the single most widely used fiber in apparel, is manufactured predominantly in Asia. Domestic knitting capacity is limited, woven mill infrastructure is sparse, and dye houses are similarly scarce.

The upstream components of a functional, end-to-end apparel supply chain are, in large part, absent from the U.S. domestic market. This is not a gap that AI or automation can fill on its own — it reflects decades of industrial offshoring that left the domestic manufacturing ecosystem incomplete.

Alexander Zar, CEO of Lalaland Production and Design — Downtown Los Angeles’s largest leather goods producer — reinforced this point from a manufacturer’s perspective. The limited availability of raw materials domestically is both a constraint on capacity and a significant cost burden for any producer serious about scaling within the United States. Lalaland manufactures luxury footwear and handbags for some of the world’s most recognized labels, operating in a segment where premium pricing can absorb the higher cost of U.S. labor and quality materials.

Luxury vs. Mass Market: A Fundamental Divide in the Onshoring Debate

Zar drew a sharp distinction between what fashion onshoring can realistically deliver for the luxury segment versus the mass market. For luxury brands, domestic production is achievable and even commercially viable. The premium price point accommodates the higher cost structure. However, any attempt to pursue domestic production at mass-market scale — particularly in categories like footwear — runs into a wall.

Many have already tried and failed by attempting to replicate the labor-intensive factory model common in Asia, hoping to approach similar margins or price points. Zar was unambiguous: that approach has not worked and will not work, primarily because labor costs in cities like Los Angeles make it economically unviable to compete on price with offshore producers.

His conclusion was that the industry must look beyond existing models. Rather than trying to adapt a system designed for low-cost, high-labor environments, U.S. manufacturers need to leapfrog to a fundamentally different production paradigm — one built around automation technologies like 3D knitting, 3D bonding, and 3D printing.

Rethinking Production Architecture for Domestic Scale

Zar offered a concrete illustration of how this rethinking might apply in practice. In footwear manufacturing, the upper is typically the most time-consuming component to produce. By incorporating automated solutions — such as a 3D knitted, technical-knit full-sock upper — manufacturers can eliminate what is often the biggest bottleneck in the production process, reducing both time and labor requirements.

The scale of the challenge, however, is not lost on him. The United States imports approximately 2.3 billion pairs of shoes annually. Building a domestic infrastructure capable of replacing that volume entirely is, by Zar’s own assessment, not realistic. But capturing even 10 to 20 percent of that market through domestic production could be achievable — provided manufacturers are willing to accept the capital-intensive nature of the investment, or embrace new technologies that remove the high-touch, labor-heavy aspects of production.

The goal Lalaland and others are working toward is a fully automated, continuously operating production environment — what Zar describes as a “dark factory” capable of running 24 hours a day without the dependency on large human labor pools. In his view, this is the only model through which scalable, domestically produced fashion becomes commercially possible.

The broader takeaway from the panel is that fashion onshoring, while not a near-term mass-market reality, is not an impossibility — provided the industry accepts that it cannot simply reproduce offshore systems on American soil. The path forward, if there is one, runs through a wholesale reimagining of how garments are designed, planned, and manufactured, with AI in fashion and advanced factory automation serving as the foundational building blocks of whatever comes next.

“The future of European footwear and leather technology will largely depend on how successfully companies combine automation, digitalization and new business models,” said Elgar Straub, Managing Director of VDMA Textile Care, Fabric and Leather Technologies. “The study identifies where the greatest opportunities for innovation and growth lie and highlights the strategic decisions companies need to take today.”

What the Gherzi Study Covers

The study examines the long-term development of three core markets: footwear manufacturing, automotive interiors, and leather goods. Its key focus areas span automation solutions, digital technologies, artificial intelligence, and new data- and service-based business models.

The analysis finds that European manufacturers can strengthen their competitiveness particularly through technological leadership, specialisation, and delivering high customer value. Connected production systems and digital services across the value chain are identified as increasingly important enablers of this competitiveness.

Beyond the traditional application fields, the study also points to meaningful growth potential in adjacent segments, including automotive interiors and leather goods, broadening the strategic outlook for companies operating within the footwear leather technology space.

Rising Demands on Machinery Suppliers

Changing customer requirements, shorter innovation cycles, and the accelerating digitalization of manufacturing processes are collectively raising the bar for machinery suppliers. The study makes clear that future success in this space will require integrated solutions that intelligently combine machinery, software, data, and services rather than treating these as separate offerings.

European machinery manufacturers are finding that speed of innovation, quality, application expertise, and proximity to customers are becoming decisive competitive differentiators. Companies that can effectively blend traditional engineering strengths with digital technologies and automation are well positioned to hold and grow their market standing over the long term.

Positive Economic Signals from VDMA TFL

The latest economic data from VDMA TFL points to a healthy growth trajectory for the sector. Between August 2025 and July 2026, order intake rose by 5.5 per cent in real terms compared to the previous year, while sales grew by 5.4 per cent over the same period.

The momentum was even more pronounced in the three-month window from May to July 2026, during which order intake climbed 14.3 per cent above the prior year’s level, while sales surged by 26.1 per cent — figures that reflect strengthening demand for footwear leather technology solutions across key markets.

Global Export Landscape in 2025

On the global market for footwear and leather technology, China remained by far the largest supplier in 2025, recording exports totalling €454.1 million (approximately $522 million). Italy followed in second position with exports of €245.6 million (approximately $282 million), while Germany recorded exports of €51.1 million (approximately $58.7 million), remaining largely in line with the previous year’s level.

Italy’s Key Export Markets

Italy’s most important destination for footwear and leather technology exports in 2025 was China, which received imports worth €25.5 million (approximately $29.3 million). France ranked second at €20.8 million (approximately $23.9 million), with Spain and the United States each accounting for approximately €16.7 million (approximately $19.2 million).

Germany’s Key Export Markets

German exports were primarily directed toward the United States, which accounted for €10.9 million (approximately $12.5 million), followed by Mexico at €6.8 million (approximately $7.8 million). France, Italy, Switzerland, and the United Kingdom also featured among Germany’s important export destinations in 2025.

The Strategic Imperative Ahead

The global competitive conditions for European machinery manufacturers have shifted considerably in recent years. The Gherzi study reinforces that the path forward for companies in the footwear leather technology space lies in combining automation, artificial intelligence, and digitalization with deep domain expertise and strong customer relationships. Those that can deliver integrated, intelligent solutions across machinery, software, data, and services will be best placed to capture the growth opportunities the sector presents.

The launch comes at a time when fashion companies are under increasing pressure to accelerate collection cycles, manage growing regulatory demands, and respond to rapidly shifting consumer behaviour. Lectra positions Apogy as a direct response to the persistent industry challenges of data silos and process fragmentation that continue to slow product development across the sector.

Bringing All Parties Into One Digital Workspace

Lectra Deputy CEO Maximilien Abadie explained the intent behind the platform: “With Apogy, we are launching the first and only solution designed to bring together all involved parties, data and processes that shape a product. By breaking down silos and streamlining interactions across the entire ecosystem, we enable fashion companies to develop desirable, manufacturable products that meet consumer demand more quickly. Apogy ushers in a new era of product development, where artificial intelligence turns product data into a competitive advantage for fashion players.”

By focusing specifically on the transition stage between design and production — often the most intricate phase of the product lifecycle — Lectra believes the Apogy fashion platform can meaningfully enhance both speed and transparency in bringing new collections to market.

How Agentic AI Powers the Platform

At the core of Apogy lies agentic AI, which supports teams throughout the product lifecycle by automating repetitive tasks and providing intelligent decision support. Key capabilities of the platform include concept generation from prompts, sketches, or images, real-time collaborative editing with full traceable histories, and support for digital prototyping through 3D simulations and photorealistic renderings — all accessible from a single interface.

Delivered as a SaaS fashion platform, Apogy ensures that users continuously benefit from the latest updates and new features as they are developed and released, without the need for complex system upgrades.

Early Adopters Report Measurable Improvements

Irish sportswear manufacturer O’Neills is among the first companies to have integrated Apogy across its teams. David Towell, Senior Manager at O’Neills, shared the impact the platform has had on the organisation: “For the first time in our history, all our teams are using a single solution. This harmonisation eliminates errors linked to data transfers, increases transparency and now enables us to accelerate operations, improve productivity and effectively support our growth.”

In addition to O’Neills, early customers reportedly include Intimissimi and Siena, with Abadie noting the breadth of adoption across both design-focused and garment production-oriented users.

Onboarding in Two Weeks and a 30% Productivity Gain

One of the more notable aspects of Apogy’s design is its accessibility to new users. Speaking directly, Abadie highlighted that early testing has shown new users are able to fully master most of the platform’s value within just two weeks of onboarding. “That’s a game changer,” he said, “because it means anyone with a pattern design mindset or design mindset alone, garment making mindset — as soon as they’ve understood the logic of Apogy, it’s pretty much plug and play.”

On the question of efficiency, Abadie pointed to a reported 30% increase in productivity for users of the Apogy fashion platform. He elaborated on the wider implications: “When you compute that into the number of days or months it takes to build a full collection, and on the opposite side you’ve got fashion companies putting garments on the market every week, here, we are talking about multiplying by a factor of two to three times the number of products. There’s no back-and-forth discussion across multiple locations for weeks on end. That’s a huge time saving, not to mention the environmental and cost benefits of less waste.”

Building on Decades of Industry Expertise

Lectra is no newcomer to fashion technology, having previously introduced widely used software tools including Modaris and Gerber AccuMark. The company notes that its long-standing history of supporting the digitisation of pattern making — spanning more than 50 years — forms the foundation upon which Apogy has been built and refined.

Lectra also highlights the platform’s capacity to preserve institutional knowledge within organisations by making expertise and information readily accessible, thereby reducing over-reliance on a small pool of technical specialists. Integration for existing Lectra customers, as well as new users, is described as straightforward, given the company’s deep familiarity with industry best practices.

Looking ahead, Lectra plans to continuously expand Apogy’s functionality, with the aim of further automating processes and unlocking new efficiencies for users across a fashion sector that continues to adapt to rapid and ongoing change.

The trend is already visible in Jiangsu province, where a textile equipment manufacturer reportedly received 13,000 orders in a single month for specialised soft materials used in robot coverings. These materials are designed to conduct heat, reduce static electricity and cushion impacts—functions that go far beyond traditional textile applications.

Unlike conventional fabrics, robot skin textiles must be engineered to work around robotic joints and moving components. The materials need to combine strength, elasticity and flexibility while supporting heat dissipation and protecting internal systems. Frequent movement at shoulders, elbows and knees requires coverings that can bend repeatedly without losing performance.

The growing demand is closely linked to the expansion of China’s humanoid robot industry. Companies such as ENGINEAI, XPENG and Leju Robotics are establishing automated production lines capable of manufacturing robots at higher volumes. As production scales up, manufacturers require standardised materials that can be integrated into efficient assembly systems.

This shift is opening new possibilities for the textile sector. Textile manufacturers are adapting expertise in technical fabrics, coatings and functional materials to meet the needs of robotics. Advanced fibres, conductive fabrics and flexible composite materials are becoming part of a broader supply chain that includes electronics, sensors and automation technologies.

Flexible sensing layers are another area where textiles play an important role. Thin-film pressure sensors integrated into robot coverings allow machines to detect touch and adjust gripping force. Such technologies can enable robots to handle delicate objects and interact more naturally with people.

Industry observers believe robot skin is emerging as a distinct category within technical textiles. Suppliers are increasingly combining textile engineering with electronics and smart material technologies to create products designed specifically for humanoid robots.

As robot manufacturing moves toward mass production, consistency and quality will become increasingly important. Textile companies capable of delivering durable, high-performance and standardised materials may find new opportunities in this evolving market.

The rise of humanoid robotics highlights how the textile industry is expanding beyond traditional sectors into advanced manufacturing. By applying expertise in functional fabrics and material innovation, textile companies are helping shape the next generation of intelligent machines.

The numbers behind Grupo Beta’s use of the CreateOne platform were notable: over 1,600 questions answered, more than 100 applications generated, and an adoption rate that surprised even experienced technology professionals. Daren Hull, CEO of CreateOne, acknowledged that the pace was unlike anything previously observed with a client.

The Core Problem: Do Factories Really Know Their Costs?

The platform, developed by Resonance, is designed to sit as an economic decision layer atop existing supply chain or ERP systems, running real-time calculations to determine whether a given order or set of orders is actually profitable. Factories today are committing to materials and labor before knowing whether an order will return a margin — a structural challenge made worse by tripling container costs and shifting tariff environments.

Lawrence Lenihan, Resonance’s executive chairman and co-founder, framed the problem in direct terms. The issue is not simply a lack of data — it is a lack of operating truth. A factory may expect 100-yard material rolls and receive 50-yard rolls instead, instantly invalidating its cost assumptions. Traditional systems often require workers to export data into dozens of spreadsheets — in one cited case, as many as 90 — which makes timely, accurate decision-making nearly impossible.

Lenihan and his team drew on nine years of directly running a plant, brands and a closed-loop manufacturing system. That firsthand experience shaped the architecture of CreateOne, which combines AI with swarms of agents, a memory system and a hybrid temporal graph network sitting atop a directed acyclic graph — producing what Lenihan described as deterministic outputs that reflect real costs in real time.

Profitability Is a Portfolio Problem

One of the key insights driving the platform is that factory profitability is not determined order by order — it is a portfolio question. Two individually unprofitable orders may become highly profitable when combined due to shared operations and maximized efficiency. The system accounts for this complexity, enabling factories to reorganize their book of orders to optimize margins across the whole.

The granularity extends to depreciation, maintenance costs and even thread expenses. For manufacturers competing in the Western Hemisphere, the platform’s ability to identify idle labor during changeovers as a primary cost driver reshapes how factories measure and pursue efficiency.

In one live example shared by Hull, a customer with nine active orders was shown a blended margin of 2.1 percent across the group, a 2.5 percent margin on the most profitable single order and a flagged sewing efficiency issue potentially tied to missing payroll data on the production floor.

Grupo Beta’s Path to Deployment

Rod Rozell, Chief Operating Officer of Grupo Beta, reconnected with Hull and Lenihan after recognizing that the platform had matured into something capable of transforming operations at scale. He emphasized that AI’s value is diminished when different departments use separate tools without integrating insights — and that CreateOne’s unified approach addresses this limitation directly.

Rozell described the leap in capability as moving from steam locomotives to a fundamentally different mode of transportation entirely. He also highlighted the speed of onboarding, noting that Grupo Beta was wired on July 24th and within weeks was ready to move from proof of concept to live implementation.

The platform is being used not by engineers but by staff in planning, finance and operations — and observations from frontline workers are feeding directly into the system’s evolving model. For an industry navigating AI apparel manufacturing adoption without a clear roadmap, CreateOne’s rapid, floor-level deployment offers a concrete illustration of what a working strategy can look like.

The report provides a qualitative assessment of AI and ML adoption trends, investment activity, emerging technologies, and competitive dynamics across the global technical textiles value chain.

A Structural Shift, Not a Cyclical Trend

BCC Research’s findings make clear that the combined pressure of labour market challenges, ESG compliance requirements, and expanding digital infrastructure is building a structural case for AI adoption across technical textiles — not simply a passing technology wave.

Limited availability and high cost of qualified textile workers are pushing manufacturers towards AI-powered sewing robots and automated production systems. These systems are being adopted to reduce dependence on manual labour, lower training costs, and improve output consistency at scale.

At the same time, end-use industries that have already deployed AI in their own manufacturing are requiring upstream suppliers to follow suit, accelerating adoption across the supply chain. Advances in cloud computing infrastructure and big data analytics are also lowering deployment barriers, while AI-powered supply chain management systems are improving demand projection accuracy and reducing overstocking and stockouts in raw fibre and polymer procurement.

Regional Maturity Across Five Stages

BCC Research classified regional adoption across five distinct maturity stages, offering a clear picture of where different parts of the world currently stand on the AI adoption curve.

North America and Europe are in the integration phase. North American producers are prioritising domestic technology implementation for supply chain resilience, while European manufacturers are deploying AI and ML to meet European Union Green Deal and Circular Economy Action Plan requirements.

Asia-Pacific is at the early adoption stage, with China targeting production efficiency and Japan using AI for property enhancement and catalyst formulation.

South America, including Brazil, and the Middle East, including Türkiye and Saudi Arabia, are at the exploration stage. Africa remains at the awareness stage, with domestic producers beginning to collaborate with foreign technology partners.

Sustainability Regulation as a Compliance-Driven Accelerant

Sustainability regulation is acting as a powerful driver — particularly in Europe — where monitoring of water consumption, energy use, carbon emissions, and textile waste is pushing manufacturers toward AI and ML systems that optimise resource use. This intersection of ESG compliance and smart manufacturing is increasingly central to how technical textiles producers are positioning themselves competitively.

Ekoten achieved an A evaluation from the Carbon Disclosure Project (CDP) Climate Change assessment in 2025, following AI-driven reductions in unnecessary water consumption. Yeşim Group reduced defects by 70 per cent in lycra jersey technical textiles production using optical sensors combined with machine learning algorithms.

Emerging Applications Identified by the Report

The BCC Research report identified a range of emerging applications currently reshaping how technical textiles are manufactured and managed. These include AI-powered defect detection using optical sensors, AI-powered sewing robots, AI-driven material selection and screening, AI optimisation of the polymerisation process, AI-driven supply chain and logistics intelligence, and AI integration in 3D weaving and advanced textile manufacturing technologies.

AI and ML systems are also demonstrating the ability to predict tensile strength, abrasion resistance, thermal resistance, flame retardancy, and electrical conductivity during the initial production phase — offering manufacturers greater precision and quality control earlier in the production cycle.

Companies Leading AI Adoption in Technical Textiles

BCC Research named several companies advancing AI adoption across the technical textiles value chain, with multiple completing or planning AI system implementations between October 2025 and April 2026. These include Aitu (Zhejiang) Intelligent Sewing Technology Co. Ltd., Yeşim Group, Ekoten, Ahlstrom, Freudenberg Group, RAGHUVIR EXIM LTD., Makalot Industrial Co. Ltd., UNSPUN, DAGA Group, and ZYOD.

UNSPUN’s planned April 2026 implementation of AI-enabled 3D weaving systems has been highlighted as a near-term commercialisation milestone.

Barriers to Adoption Remain in Emerging Markets

While momentum is building, adoption remains uneven — particularly in emerging markets. BCC Research identified shortages of skilled AI professionals, inadequate digital infrastructure, high upfront capital requirements, and dependence on foreign technology vendors as key constraints.

Data quality and privacy concerns, along with unresolved governance and ethical frameworks in some jurisdictions, are adding further complexity for producers attempting to scale AI systems beyond pilot phases.

Positioning and Risk Outlook

For companies already in the integration phase of AI in technical textiles — particularly those demonstrating results in defect reduction, ESG compliance, and supply chain optimisation — BCC Research notes they are best positioned to capture margin expansion and secure preferential supplier status with AI-enabled end-use industries.

Near-term catalysts include anticipated foreign investment into African markets during the forecast period and the commercialisation of AI-enabled 3D weaving systems. Key risks identified include uneven infrastructure readiness, vendor lock-in exposure in emerging markets, and regulatory uncertainty around AI governance.

The report’s overall conclusion is that AI and ML adoption in technical textiles is being shaped by converging forces — and for manufacturers at every stage of the maturity curve, the question is no longer whether to adopt, but how quickly and at what scale.

Hansae Partners with Robotics Firm to Explore AI on Production Lines

Apparel manufacturer Hansae has signed a contract with Realworld, a robotic gripper technology firm, and is currently reviewing practical applications of AI and robotics within its apparel production lines. Realworld specializes in robotic-hand AI technology capable of gripping and moving objects with the same precision as a human hand. Industry sources indicate that interest in automating apparel production gained noticeable momentum after Hansae Vice Chairman Kim Ik-hwan personally examined the technology.

Hansae’s engagement with AI in textile manufacturing is not new. After establishing a dedicated AI team in 2023, the company began applying artificial intelligence to design development and product manufacturing processes. More recently, Hansae has been running a proof-of-concept project alongside system software developer CA Planet, focused on technology that automatically adjusts a single pattern to fit multiple sizes.

“We are expanding the application of AI technology to various areas, including production, development and quality control,” a Hansae official stated.

Domestic Manufacturers Lead the Push for AI-Driven Automation

The adoption of manufacturing AI is especially pronounced among companies maintaining production bases within South Korea — a direct response to decades of skill-dependent manual processes that are increasingly difficult to sustain.

Bokwang INT, which produces specialized garments for the military and firefighting sectors, is applying AI to gauge fabric thickness and tension in real time, enabling automatic adjustment of sewing conditions and instant detection of minute surface defects. The objective is to standardize sewing and quality inspection processes that have traditionally varied depending on individual worker skill levels.

Korean Friends is developing technology that converts generative AI-created designs directly into the work orders required for actual garment production — reducing the need for staff to manually redraw design images into production blueprints, thereby cutting both time and manpower in product development.

Meanwhile, Georim Trend, F&I Korea, and Good Trust are collaborating with AI data company SIJE to collect and analyze real-time sewing workloads and equipment data, with the goal of identifying and eliminating bottlenecks that emerge during production. These efforts collectively reflect a growing industry-wide commitment to K-fashion production becoming less reliant on manual labor.

Government Backs the AI Transition with a ₩310 Billion Investment Plan

The South Korean government is actively supporting the integration of AI in textile manufacturing. The Ministry of Trade, Industry and Energy has surveyed corporate demand to advance an “advanced future textile development project,” which plans to invest a total of 310 billion won (approximately $224 million USD) between 2028 and 2034. The project is designed to boost productivity by incorporating AI across the entire textile manufacturing chain — spanning yarn and spinning through to dyeing, processing, and sewing. More than 200 companies responded to the demand survey, exceeding initial expectations.

U.S. Fashion-Tech Also Eyes Full Garment Automation

In the United States, where production facilities were offshored decades ago, there is a parallel push toward fully automating garment production. American fashion-tech company CreateMe is developing a production system that tracks fabric position in real time and automatically assembles complete garments. The company holds over 95 related patents and has recently launched a project to build a domestic garment production ecosystem in partnership with AI agriculture firm Avalo and fabric manufacturer Laguna Fabrics.

A Sense of Crisis Drives South Korea’s Automation Urgency

Behind the Korean textile industry’s intensifying focus on textile automation lies a deeper sense of urgency about a weakening domestic production base. South Korea’s textile sector was a pillar of national exports during the 1960s and 1970s, but production facilities migrated rapidly to China, Vietnam, and other lower-cost destinations as labor and manufacturing expenses rose. This trend, compounded by the aging of Korea’s domestic workforce, has made it increasingly difficult to find skilled workers for labor-intensive tasks such as sewing.

“If only design and planning remain in Korea while the production base disappears, there are bound to be limits to the growth of K-fashion,” an industry official said. “If we can automate even labor-intensive processes such as cutting and sewing with AI and robots, it will help make up for the labor shortage and maintain the domestic manufacturing base.”

The convergence of robotic gripper technology, AI-driven quality control, government funding, and cross-sector collaboration signals that South Korea’s textile industry is making a calculated and coordinated effort to use AI manufacturing not just as a productivity tool, but as a means of preserving the country’s industrial identity in apparel.

The physical backbone of the AI boom consists of complex hardware, including high-performance servers and the networking equipment that links them. This hardware requires high-quality printed circuit boards and high-capacity cables. At the core of these components are specialized glass fiber fabrics, which offer the durability and signal integrity required for modern digital communication. As the need for faster processing speeds grows, the production of these fabrics has become a high-priority sector for textile machinery manufacturers.

Advanced Warp Preparation for High-Speed Connectivity

The AI Warp Prep Tech provided by the Karl Mayer Group is specifically engineered to handle the technical challenges associated with processing glass fibers. These materials are notably brittle and sensitive to tension, making the warp preparation process a highly delicate operation. To address this, the company utilizes its PROWARP sectional warping machine, which has been adapted to meet the stringent quality standards of the electronics industry.

By ensuring precise tension control and uniform yarn placement, this technology allows manufacturers to produce the high-density glass fiber fabrics used in copper-clad laminates. These laminates are fundamental to the construction of the servers housed within global data centers. The efficiency of these machines is currently a driving factor for manufacturers, particularly in regions like China, where the build-out of digital infrastructure is accelerating at a rapid pace.

Meeting the Demands of Modern Fiber Optic Cables

Beyond the internal components of servers, the industry is seeing a massive uptick in the requirement for high-durability fiber optic cables. These cables are essential for the long-distance transmission of data between processing hubs. The integration of the AI Warp Prep Tech ensures that the reinforcement layers within these cables are manufactured with the highest degree of structural integrity.

As the digital landscape evolves, the synergy between heavy industrial machinery and cutting-edge software development becomes more apparent. The role of the Karl Mayer Group highlights how traditional textile expertise is being repurposed to solve the logistical and hardware challenges of the next generation of computing.