The AI Outlook With Maximilien Abadie of Lectra

Released in The Interline’s AI Report 2025, this executive interview with Lectra is one of an eight-part series that sees The Interline quiz executives from companies who have either introduced new AI solutions or added meaningful new AI capabilities into their existing platforms.

For more on artificial intelligence in fashion, download the full AI Report 2025 completely free of charge and ungated.

---

Where do you believe we currently are on the progression curve from AI as an extremely broad set of capabilities and promises, to AI as the foundation for applications and services that can deliver a measurable return on investment in well-defined areas?

We are entering a pivotal phase in the evolution of artificial intelligence. Lectra has been embedding AI into its solutions since the 1990s, long before it became a mainstream topic. Early implementations included optimization algorithms and expert systems in technologies such as Diamino and Vector, designed to maximize fabric yield and automate complex cutting decisions. For decades, AI algorithms have supported process optimization in our industry. Today, we are witnessing a major inflection point. Breakthroughs in data processing and reasoning are unlocking new strategic opportunities across the entire fashion value chain. At Lectra, we are harnessing these advances to deliver AI-powered solutions that address concrete business challenges. Our objective is clear: to transform data into actionable insights that enhance decision-making, boost agility, and drive measurable improvements in performance and return on investment.

One of the key use cases for AI is extracting meaningful insights from large, complex datasets at the kind of speed and scale that human workflows can’t match. People often talk about that in the context of forecasting, or risk modelling, but there’s also a much broader perspective that takes in all the different variables and datapoints that fashion generates and consumes, to create a complete set of “fashion data”. Given Lectra’s perspective across all the different domains of fashion – what you refer to as create, manufacture, and market – how do you see AI being applied at that whole-industry level, and what value can it create?

AI can deliver its full potential when it connects the entire fashion value chain—from design to production to marketing. At Lectra, we integrate AI into solutions like Valia Fashion, Launchmetrics, Retviews and TextileGenesis to transform data into actionable insights. This enables brands to anticipate demand, optimize production, and benchmark performance in real time. By breaking down silos, AI helps fashion companies gain agility, improve margins and operate more sustainably.

Thinking about applying AI to the whole scope of fashion data also raises some deep questions about enterprise-wide data governance, ethics, and security that Lectra, with close to a decade of making data from across the connected value chain usable, has probably had time to think about. If the near future is going to see brands and their partners applying a relatively new class of technologies and ideas to whole-industry problems, what is it going to look like to do that in a way that’s efficient, ethical, interpretable, and useful?

There are no shortcuts, scaling AI in a meaningful way requires solid data foundations, transparent logic, and strong governance. At Lectra, we’ve spent the past decade connecting more than 8,000 of industrial systems and structuring data flows across the fashion value chain. This long-term commitment ensures data quality, traceability, and secure access, cornerstones for responsible and efficient AI deployment.

Solutions like Valia Fashion and TextileGenesis exemplify this approach. Valia Fashion not only provides real-time visibility into production performance, but also enables advanced simulations, accurate fabric consumption estimations, and automated production allocations based on predefined parameters. By connecting equipment, teams, and systems across geographies, it supports agile, scalable, and sustainable operations. 

TextileGenesis applies rule-based AI to ensure end-to-end traceability from fiber to finished product, offering full transparency and auditability at every step of the supply chain.

At Lectra, AI is designed to remain interpretable and enhance, not replace, human expertise. Ethics, explainability, and accountability are embedded by design, supporting both operational excellence and long-term trust.

If we look past the big, sweeping, questions, most AI strategies – and most technology initiatives in general – have a more pressing near-term goal: to help deliver transformation in areas that the industry has identified as priorities for change. Across the value chain, Lectra has some examples of this kind of focus in action, so walk us through, for example, where AI is contributing to transformation in product design, textile quality control, material yield optimisation, and measuring brand performance.

AI is already driving concrete improvements across the value chain. In manufacturing, Valia Fashion enhances planning and fabric optimization. Recently, Lectra has signed a strategic partnership with AQC which develops AI vision systems to automate and improve defect detection. On the marketing side, Retviews AI-based solution supports real-time competitive benchmarking and Launchmetrics uses AI to add insights into brand performance. These offers are operational today and delivering measurable results.

There are very few companies that have the same scale of direct presence in fashion manufacturing as they do in design, development, and other domains. That reach matters for reasons we’ve already talked about, but it could also put a lot of power into the hands of companies that are actively working to reduce their impact, track their product journeys, and comply with disclosure requirements and vehicles like Digital Product Passports. If you think about the production-level visibility you’re building with the Valia Fashion platform, and the fibre-foward traceability you’re architecting with TextileGenesis, what role do you think AI has to play in the new toolkit for sustainability.

AI plays a pivotal role in making sustainability measurable, traceable, and actionable. With TextileGenesis, we leverage rules-based AI to tackle the challenge of fiber-to-retail traceability, ensuring data integrity and enabling compliance with evolving regulations. Valia Fashion enhances this by providing granular, real-time visibility into production processes. Together, these solutions empower brands to not only substantiate their sustainability claims but also to continuously refine and improve their practices. AI facilitates a shift from reactive compliance to proactive impact management, which is increasingly becoming a key competitive differentiator.

What do you believe are the next steps for how AI is deployed and used? Is it more likely that AI will solidify its place as a new human interface paradigm the frontend of tools and workflows? Or is its future closer to what cloud infrastructure has become today – a quieter commodity that is still the foundation for the next generation of applications, but in a less obvious way than what we’ve seen over the last couple of years? Or is it both?

AI will play a dual role in the future. On one side, it will remain a visible interface, enhancing decision-making and creativity by providing real-time insights and augmenting human intuition, especially in areas like design and planning. On the other side, AI will continue to serve as the invisible backbone, driving backend automation, analyzing large datasets, and enabling greater interoperability across systems. This dual nature, visible intelligence for user interaction and invisible AI for data processing, will make AI an integral part of technology stacks, much like cloud computing has become today, driving both efficiency and innovation in the background.