What skills and team structures do fashion companies need for AI?

KEY TAKEAWAYS Summary by the editors

1. Fashion companies need three layers of AI skills: broad AI literacy for all employees, applied skills for the teams whose workflows change, and specialist skills in data, engineering and governance.
2. The World Economic Forum's Future of Jobs Report 2025 expects nearly 40 per cent of job skills to change by 2030 and lists AI and big data among the fastest-growing skills.
3. McKinsey's January 2025 Superagency research found that 13 per cent of employees said they used generative AI for at least 30 per cent of their daily work, while C-suite leaders estimated 4 per cent.
4. Under the EU AI Act, as amended by the AI Omnibus in 2026, providers and deployers must support the development of AI literacy among staff, rather than guarantee a specific level.
5. Most mid-sized fashion companies do best with a small central AI team that sets standards and supports business teams, which own the use cases and the results.

Fashion companies need three layers of AI skills: basic AI literacy for every employee, applied skills for the teams whose work changes most (such as merchandising, product content, customer service and sales), and a small group of specialists in data, engineering and governance. The most effective structure is usually a compact central team that sets standards and supports business teams, which own the use cases and are accountable for results.

How is AI changing jobs in fashion?

The broad direction is well documented. The World Economic Forum's Future of Jobs Report 2025, based on a survey of more than 1,000 employers, expects 170 million roles to be created and 92 million displaced globally by 2030, and nearly 40 per cent of job skills to change. It lists AI and big data among the fastest-growing skills, alongside analytical thinking, resilience and leadership. According to the WEF, 77 per cent of employers plan to upskill their workforce.

In fashion, the effect is less about whole jobs disappearing and more about the tasks inside them shifting. Copywriters spend more time editing generated text and less time writing from scratch. Planners review forecast exceptions rather than building every number manually. Customer service agents handle the complex cases while routine questions are answered automatically. The State of Fashion 2026 by The Business of Fashion and McKinsey describes this as a workforce being rewired.

What skills do fashion companies need for AI?

Domain knowledge remains essential. A model can suggest a size curve, but only an experienced planner knows that a particular store was closed for refurbishment. The most valuable profiles combine fashion expertise with enough AI understanding to question the output.

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Why do leaders need AI literacy first?

Leaders often underestimate how far AI use has already spread. McKinsey's Superagency in the workplace research, published in January 2025 and based on a survey of 3,613 employees and 238 C-level executives, found that 13 per cent of employees said they used generative AI for at least 30 per cent of their daily work, while C-suite leaders estimated the figure at 4 per cent. The report concluded that the biggest barrier to success was leadership rather than employee readiness. It also found that 48 per cent of employees wanted more formal training.

Leaders who understand the basics make better decisions on use cases, budgets and risk, and their visible use of AI tools signals that experimenting within the rules is welcome.

What does the EU AI Act require on AI literacy?

The AI Act's provision on AI literacy, Article 4, has applied since 2 February 2025. The AI Omnibus, published in the Official Journal on 24 July 2026, changed the wording: providers and deployers must now support the development of AI literacy, and the revised text clarifies that it does not require them to guarantee a specific level, according to the Future of Privacy Forum. For fashion companies, short, role-specific training on approved tools, data rules and typical errors is a practical way to meet that expectation and to reduce risk.

How should a fashion company organise its AI team?

Three models are common. In a centralised model, one team owns all AI work; this builds expertise quickly but can become disconnected from the business. In a decentralised model, each function runs its own AI projects; this is close to the business but leads to duplication and uneven standards. In a hub-and-spoke model, a small central team owns standards, platforms, governance and training, while business teams own use cases and results, often with embedded AI champions.

For most mid-sized fashion companies, the hub-and-spoke model is the most practical. The central team might consist of a lead, one or two data or AI engineers, a data governance role and close links to IT, legal and HR. Each business area names an AI champion who knows the processes and helps colleagues adopt new tools.

How do you upskill fashion teams for AI?

1. Start with leaders and AI champions, so that they can guide others.
2. Offer short, practical sessions tied to the tools and data people actually use, rather than general courses.
3. Teach error checking explicitly, with real examples of wrong composition data, invented facts or biased imagery.
4. Create a place to share prompts, examples and lessons learned within each function.
5. Revisit training when tools or rules change, and measure adoption alongside results.

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What change management makes AI adoption stick?

Adoption fails when people fear for their jobs, do not trust the output or find the tool slower than the old way. Honest communication about how roles will change, involvement of employees and works councils early, and pilots that make people's work easier rather than adding steps all help. In McKinsey's 2026 State of AI survey, nearly three quarters of AI high performers reported fundamentally redesigning workflows because of AI, a sign that lasting value comes from changing how work is organised, not from installing tools on top of old processes.

• Explain what will change in each role and what will not.
• Involve the people doing the work in designing the new workflow.
• Celebrate measured improvements, not just launches.
• Keep humans responsible for decisions with legal, financial or reputational consequences.

Frequently asked questions

What AI skills do fashion employees need?

All employees need basic AI literacy: understanding what AI can and cannot do, using approved tools safely and checking output. Teams whose work changes most, such as merchandising, product content and customer service, need applied skills with specific tools. A small group of specialists is needed for data, engineering and governance.

Does a fashion company need a Chief AI Officer?

Not necessarily. What matters is that a senior executive is accountable for AI and that a central team sets standards and supports the business. In smaller companies this role is often combined with digital, data or IT leadership. Each use case should still have a business owner.

Is AI training mandatory under the EU AI Act?

The AI Act's AI literacy provision has applied since February 2025. After the AI Omnibus amendments published in July 2026, providers and deployers must support the development of AI literacy among their staff, without having to guarantee a specific level. Role-specific training is a practical way to meet this.

Will AI replace jobs in fashion?

The World Economic Forum expects both job creation and displacement globally by 2030, with nearly 40 per cent of job skills changing. In fashion, AI mostly shifts tasks within roles, for example from writing to editing product copy. The impact depends on how companies redesign work and retrain people.

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