For decades, manufacturing industries — from fashion, to automotive, to furniture — relied on shared collective knowledge that formed the backbone of product development and execution across the supply chain, enabling brands to consistently translate creative vision into high-quality products. Today, that system is under strain.
As younger generations continue to enter the workforce, the traditional pathways for learning need to evolve. Emerging talent is expected to assimilate existing knowledge faster, and without the proper training. This creates a disconnect where shared understanding is rooted in assumptions rather than formalized knowledge-sharing.
Most companies struggle because the pace and complexity of modern operations have outgrown traditional ways of working. New hires are often expected to navigate fragmented systems, shifting consumer demand, supply chain volatility, and data-driven decision making from day one.
This is a challenge now present across the entire supply chain.
Modern manufacturing remains deeply dependent on specialized human experience. Pattern-making, fit, material usage and costing accuracy are skills refined through years of hands-on experience and contextual learning. These capabilities are highly nuanced and difficult to document, scale or replace.
As experienced professionals retire or move on, brands face a serious risk of knowledge loss. Critical decisions become inconsistent, execution varies across teams, and younger employees are left without clear guidance on best practices. In many organizations, this results in an overreliance on a few key individuals rather than resilient systems that support knowledge continuity.
At the same time, younger generations entering the workforce have different expectations. They look for transparent career pathways, intuitive tools, and environments where learning is embedded into daily work. Unfortunately, outdated workflows, fragmented systems and undocumented processes often fail to meet these expectations. When the tools feel disconnected from reality, adoption stalls and frustration grows.
Technology alone will not solve the talent gap. But, when applied intentionally, it can significantly amplify scarce expertise. The challenge is how to adopt technology. Manufacturers must integrate digital tools in ways that are accessible to new employees while respecting the experience of existing teams. Structured, intuitive platforms allow junior and senior team members to collaborate more effectively, embedding best practices directly into the process instead of relying on informal knowledge transfer.
This is already evident in areas such as intelligent manufacturing and robotics, which have shown they can reduce production time and improve consistency without eliminating the need for skilled professionals. Technology handles repetitive or data-heavy tasks, allowing human talent to focus on creativity, judgment and problem-solving.
Looking across the broader workforce, there’s a growing urgency to close this gap. Recent research from Business of Fashion and McKinsey & Company indicates that, by 2030, up to 40% of workers in developed economies will need to reskill or transition roles due to advancing technologies. Regardless of whether manufacturers are producing goods for fashion, furniture or the automotive industries, roles are evolving, and the ability to support that evolution will become an increasingly important competitive differentiator.
AI as an Amplifier of Human Expertise in Fashion Manufacturing
As fashion brands navigate this growing operational complexity, AI has proven to be a powerful catalyst for transforming the critical roles driving the industry forward. AI acts as an amplifier that enhances creative or technical expertise, and guides how that experience is applied day-to-day.
Having AI-driven solutions in place captures that invaluable institutional knowledge, standardizes best practices, and provides those real-time insights that ultimately support better decisions. By embedding this intelligence into design, development and production workflows, brands can reduce their dependency on the memories and ways of working often held by legacy workers, while creating more consistent outcomes.
For example, manufactures can use AI to analyze historical data and guide material selection, optimize fit adjustments, or flag production risks early. This allows teams, especially less experienced ones or those operating with limited resources, to move with greater confidence and learn from established practices. Senior professionals, in turn, are freed from repetitive tasks and can focus on innovation, mentorship and greater strategic direction. AI also plays a role in modernizing training. Digitized workflows, guided processes and intelligent recommendations help transform learning from an informal afterthought into a continuous, embedded experience.
The talent gap across the supply chain is a pressing, current concern. Manufacturers that ignore skill shortages risk becoming slower, less consistent and more vulnerable in an increasingly competitive market. Those that succeed will be the ones willing to invest in their people as much as their technology. This means supporting learning, digitizing training and administrative labor, and using tools as enablers for human expertise.
John Brearley is President of Americas at Lectra.
