Lectra deputy CEO Maximilien Abadie has questioned whether the fashion industry has been thinking about artificial intelligence from the wrong angle, arguing in an interview with Just Style that the technology's real value lies in functioning as an additional team member rather than as a conventional tool.

Abadie, who leads strategy at the France-based industrial technology provider, framed what he calls "agentic AI" — systems capable of taking autonomous action on defined tasks — as the colleague manufacturers and brands did not know they needed. His comments shift the discussion away from the familiar industry debate over generative AI for design and marketing content, and toward operational deployment inside production workflows.

For Lectra's customer base, the distinction matters commercially. The company supplies cutting-room equipment, CAD software and analytics platforms to apparel manufacturers and upholstered-furniture producers, and its software business has been the growth engine of the group in recent years. An AI positioned as an autonomous actor within those systems — rather than as a passive analytics layer — would change how factories assign work, how they schedule capacity and how they respond to disruptions on the line.

Abadie's framing also carries implications for sourcing decisions. Fashion brands and their suppliers have spent the past several years digitising to gain visibility across multi-tier supply chains, driven by compliance pressure from due-diligence legislation and by the operational shocks of the pandemic era. If agentic AI can act on the data those systems collect — flagging a late fabric delivery, re-sequencing cut orders, escalating a quality deviation — then the return on digital investment depends less on dashboards and more on how much decision-making a manufacturer is willing to delegate to software.

That delegation question is the practical one. Abadie's characterisation of AI as a "team member" implies a shift in responsibility: supervisors will need to manage machine actors the way they manage staff, setting objectives, checking outputs and intervening when the system misreads a situation. For an industry where cutting errors and marker inefficiency translate directly into fabric waste and margin loss, the tolerance for autonomous mistakes will be narrow, and manufacturers are likely to start with tightly bounded tasks before extending AI's remit.

The timing of the argument is notable. Fashion's early experiments with generative AI concentrated on the front end of the value chain — trend analysis, design iteration, marketing copy — because those use cases carried low operational risk. Executives such as Abadie are now pushing the conversation toward the back end, where the industry's structural problems actually sit: fragmented order books, long lead times and production planning that still depends heavily on experienced individuals.

Lectra has a commercial interest in that shift, and Abadie's comments should be read partly as positioning the company's roadmap. But the underlying question he raises applies to any supplier or brand evaluating AI spend in 2026: whether the technology is being procured as a tool that speeds up existing tasks, or as an agent that takes ownership of outcomes.

For sourcing directors and factory managers, the interview poses a concrete decision. Piloting agentic AI now — on contained, measurable processes where errors are recoverable — would give manufacturers operational evidence before competitors build the same capability. Waiting leaves the delegation question unanswered at exactly the moment vendors begin shipping autonomy as a standard feature of production software.