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Automation and generative AI are set to have the most immediate and transformative impact on how organisations operate, reshaping roles, skills and ways of working at a pace comparable to the disruption created by computers and the internet in their early stages of adoption.
Companies across industries are expected to unlock productivity gains of more than 30 percent over the next five years due automation and generative AI.
Fashion brands are seeing benefits from early AI adoption. Automation is reshaping many routine tasks, such as customer service and inventory management, freeing up time and resources. Companies like Zalando and Nike are using generative AI across functions, from image generation to product design and personalisation. At Zalando, generative AI has reduced image production costs by 90 percent. Agentic AI is accelerating this further, offering the potential for autonomous decision making and execution.
However, progress among global organisations remains uneven. While 92 percent of companies say they will increase their investments in generative AI, only 1 percent say their deployment of AI has reached maturity. Many are stuck in pilot mode, testing siloed AI solutions with limited impact, with scaling often seen as too complex or costly.
In 2026, fashion organisations need to evolve in four key areas to capture the benefits of AI
Organisational design
AI will impact different functions and types of organisations differently, but fashion companies of the future will be more efficient, with employees focused on higher-value tasks. Some transactional functions with highly repetitive tasks — like core corporate services such as accounting and payroll processing — may become very heavily automated.
28-38% of consumer goods and retail workers’ current activities in Europe and the US could be impacted by technology by 2030
Ways of working
Cross-functional collaboration will become the cornerstone of better-informed decision making. For example, design teams using AI could access real-time material prices from preferred suppliers, enabling quicker choices in conjunction with sourcing and procurement teams. Unlocking this potential requires cultural shifts that break down silos and establish clear roles and responsibilities.
47% of US employees across industries expect more than 30 percent of their work to change because of generative AI
Roles and talent
With AI, the nature of work will shift from manual tasks, data processing and record-keeping to activities such as troubleshooting and programming. Up to 40 percent of workers in consumer goods and retail industries in developed countries may need to reskill or transition to new roles by 2030 due to technological progress. However, technology skills remain scarce and technology talent churn is high.
47% of US consumer goods and retail employees say training is the most important factor for generative AI adoption, but nearly half feel they are receiving only moderate support or less
Culture
Fashion and luxury companies have traditionally attracted talent from within the industry, thereby limiting change. Leaders must widen their approach to talent acquisition and foster cultural changes to support new ways of working. For some functions, such as customer engagement or supply chain automation, third-party solutions may accelerate adoption more than building capabilities in-house.
60% of leaders across industries say company culture is the biggest obstacle to tech-related change
In retail, automation is transforming back-office operations to the greatest degree
Global fashion and luxury players have made progress deploying automation with generative AI in select functions for routine tasks. More than 35 percent of executives report already using it in areas such as online customer service, image creation, copywriting, consumer search or product discovery.
Yet adoption is not without its barriers: Challenges include high implementation costs, fragmented legacy systems, limited in-house expertise and uncertainty around governance and ethics. Achieving full potential will depend on redesigning processes and team structures rather than simply overlaying new technology onto legacy ways of working, particularly in critical functions — often resulting in longer lead times. Adoption will inevitably lag the technology’s actual potential.
However, the efficiencies gained will enable companies to redeploy talent towards higher-value, creative and strategic work — for example, related to innovation, brand storytelling, product development and client relationships.
Generative AI is accelerating shifts in marketing, merchandising and sales
Marketing is moving from content production to curation
Around 22 percent of marketing tasks can be automated by 2030, particularly in strategy development, campaign ideation and content creation. Generative AI will allow creative teams to shift from manual production to curating and directing AI-generated assets. Advancements in this space, however, will require careful risk management around authenticity and consumer trust given the limited precedent for AI-generated assets.
Zalando: The online retailer is using generative AI to accelerate image creation for its app and website, cutting production time from six to eight weeks to just three to four days, according to Zalando’s vice president of content solutions. In the fourth quarter of 2024, 70 percent of its editorial content was AI-generated. While traditional photoshoots remain part of the process, creatives involved in the process are now expected to adapt to using AI tools.
Merchandising decisions can be accelerated and more informed
Generative AI could significantly accelerate merchandising with applications ranging from automating assortment selection to detecting microtrends via social listening and translating them into actionable guidance for stock volumes and marketing priorities.
Pandora: The Danish jewellery brand has partnered with AI software platform o9 Solutions to modernise its planning and merchandising operations. The collaboration focuses on integrating demand, assortment and merchandise financial planning into a single AI-driven platform, replacing fragmented legacy systems. Through o9’s Digital Brain platform, Pandora aims to enhance visibility, forecasting accuracy and responsiveness across its global network, enabling more data-driven, real-time decision-making.
Sales teams can be equipped with valuable customer data
Digital ordering systems and AI tools can increase sales associates’ effectiveness, from offering personalised product recommendations to improving stock visibility. This is particularly relevant for the premium/bridge and luxury segments, where such tools can elevate the shopping experience if executed well, enhancing — not replacing — clienteling or human connection.
Zegna: Zegna launched Zegna X, an AI-powered clienteling app developed with Microsoft. Using Zegna X, associates can share new arrivals, recommendations and styling ideas with customers via text, email or platforms like WhatsApp. The app consolidates customer data to refine personalisation, making recommendations smarter. The AI supports rather than replaces sales representatives, who remain responsible for finalising client interactions.
Fashion players need to reskill teams and recruit tech expertise to mature their AI pilots
Upskilling the workforce
Employees are eager for AI training, yet many organisations are not meeting this demand. More than 40 percent of US employees in consumer goods and retail say initiatives such as formal generative AI training, seamless workflow integration and incentives would make them more likely to use generative AI tools daily.
Companies should offer practical programmes — whether focused on general-purpose AI tools or domain-specific, custom-built applications. For instance, a marketing-focused programme could demonstrate how agentic AI scans consumer sentiment on social media, integrates those insights with customer data and drafts campaign concepts for teams to refine.
Attracting talent
Fashion players should be open to bringing in external expertise and attracting talent from beyond the traditional fashion ecosystem. LVMH, for instance, has built a centralised research team of data scientists and engineers to work across its brand portfolio, demonstrating the need to invest in specialised talent to accelerate enterprise-wide adoption.
However, Big Tech’s competition for AI talent is driving up costs and limiting availability for fashion players. Demand is rising for roles such as product flow engineers, integrative marketing strategists and consumer experience designers — skills in short supply. Key hiring criteria include expertise in data quality, system integration, digital twins and marketing automation. To compete, fashion companies must build a compelling employee value proposition beyond pay and compensation.
How should executives respond to these shifts?
Make AI a strategic priority and start with high-value processes
Fashion and luxury players must integrate AI into core creative and operational processes, elevating it to a board-level priority rather than limiting it to function-specific pilots. Companies will need to scale and mature these investments to lock in efficiency gains as cost pressures rise from multiple angles.
The most effective transformations balance quick wins with long-term ambition — targeting high-value tasks with relatively easier implementation in areas like marketing or back-office functions, while reinvesting gains to advance core product and customer-facing processes.
Build the talent base to support technology goals
Fashion companies should widen their recruitment net to secure capabilities outside the traditional fashion ecosystem. Limited in-house expertise and competition for scarce technology talent remain critical obstacles.
A compelling employee value proposition that highlights innovation and creativity will be critical to differentiate from companies that typically attract top digital talent.
Every investment in advanced technologies must also be matched with deliberate workforce development. This requires close collaboration between chief technology officers and chief people officers, as well as targeted upskilling and reskilling employee programmes.
Prioritise change management and culture transformations
Strong change leadership is key to realising AI’s full value. Instead of top-down directives, leaders should identify where innovation is already emerging, support teams, employees or departments that are early adopters, and scale their successes so that momentum builds and employees feel personally invested in the change.
Above all, leaders must recognise that the shift is not just about skills but about culture — reshaping how people think, collaborate and embrace change. Without strong leadership to guide both mindset and capability shifts, brands risk falling behind in an industry where AI adoption is accelerating.
This article first appeared in The State of Fashion 2026, an in-depth report on the global fashion industry, co-published by BoF and McKinsey & Company.