How to build an AI strategy for a fashion brand
An AI strategy is a set of choices about where AI should change how the business works, what data and skills that requires and how risk is governed. Here is how fashion brands can make those choices.
KEY TAKEAWAYS Summary by the editors
- An AI strategy for a fashion brand should start from business priorities such as margin, inventory efficiency or growth in a channel, and then identify where AI can change specific decisions and workflows.
- BCG's 2025 survey of 1,250 executives found that only 5 per cent of companies generate substantial value from AI, while 60 per cent report minimal revenue and cost gains.
- BCG estimates that around 70 per cent of AI's potential value is concentrated in core functions such as sales and marketing, manufacturing, supply chain and pricing, rather than in IT.
- In McKinsey's 2026 State of AI survey, AI high performers were twice as likely as others to say their senior leaders demonstrate commitment to AI and nearly three quarters had redesigned workflows.
- Governance standards such as ISO/IEC 42001, published in December 2023, give fashion companies a structure for managing AI risks without building a framework from scratch.
An AI strategy for a fashion brand is a short set of decisions: which business outcomes AI should improve, which workflows will change, what data, skills and partners that requires, and how risks will be governed. It is not a list of tools. The brands that get value from AI tie it to a few priorities and change how work is done around them.
Why do fashion brands need an AI strategy at all?
Without a strategy, AI tends to arrive in a company through individual teams buying tools, employees using public chatbots and vendors adding AI features to existing software. That creates cost, data risk and duplication without much measurable benefit. The State of Fashion 2026 by The Business of Fashion and McKinsey describes AI as reshaping the workforce, the way consumers shop and the efficiency of the fashion system, which makes it a leadership topic rather than an IT project.
The gap between companies is wide. BCG's September 2025 study of 1,250 senior executives found that 5 per cent of companies are generating substantial value from AI, 35 per cent are beginning to, and 60 per cent report minimal revenue and cost gains. McKinsey's 2026 State of AI survey reported that only 6 per cent of respondents qualified as high performers, attributing 5 per cent or more of EBIT to AI.
Where should a fashion brand focus its AI efforts?
BCG estimates that around 70 per cent of AI's potential value lies in core business functions, naming sales and marketing, manufacturing, supply chain and pricing, while IT accounts for about 13 per cent. For a fashion brand, the corresponding areas are typically demand planning and allocation, pricing and markdowns, product content and e-commerce, wholesale sales and customer service.
A useful exercise is to list the ten decisions that most influence the brand's margin, such as buy quantities, size curves, initial price, markdown timing and re-order volumes, and ask for each one where better information or faster execution would make a measurable difference. That produces a shortlist rooted in the business, not in technology.
What are the building blocks of an AI strategy?
| Building block | Key question | Typical output |
|---|---|---|
| Ambition | Which two or three business outcomes should AI improve? | Targets such as lower excess stock or faster time to market |
| Use case portfolio | Which workflows change first, and why? | Prioritised list with owners and success measures |
| Data foundation | Which data must be fixed for the chosen use cases? | Data improvement plan with named data owners |
| Technology and partners | What will we buy, build or source from partners? | Sourcing principles and a short list of platforms |
| People and skills | Who needs which skills, and how will roles change? | Training plan and team structure |
| Governance | How are risks, data use and generated content controlled? | AI policy, tool register and review process |
| Measurement | How do we know it works? | Baselines, control groups and regular reviews |
Each block should fit on a page. A strategy that cannot be explained to a store manager or a sales representative in a few minutes is unlikely to change how they work.
How should a fashion brand govern AI?
Governance covers which tools may be used with which data, how generated text and imagery are reviewed and disclosed, how customer and employee data are protected and who is accountable for each AI system. ISO/IEC 42001, published in December 2023, specifies requirements for an AI management system and is designed for organisations of any size that develop, provide or use AI. Brands do not need certification to benefit from it; its structure is a useful checklist.
Data governance is part of the same discussion. Gartner predicts that through 2026, organisations will abandon 60 per cent of AI projects that are not supported by AI-ready data. Naming owners for product, inventory and customer data, and agreeing quality standards for them, is therefore a strategic decision rather than an administrative one.
How do you sequence an AI roadmap?
- Foundation: agree the ambition, set basic governance, run a data audit for the priority areas and train leaders.
- Prove: run two or three measured pilots in areas where data is ready and a business owner is committed.
- Scale: roll out what works, integrate it with core systems and redesign the surrounding workflows.
- Extend: move to harder use cases, such as forecasting for new products or cross-channel inventory optimisation, once data and skills have matured.
Rigid multi-year plans age quickly because the technology changes fast. A rolling roadmap reviewed every quarter, with firm commitments for the next two quarters and options beyond that, is more realistic.
How do you know if your AI strategy is working?
The test is whether business metrics move, not how many pilots are running. Useful indicators include the share of AI initiatives with a measured baseline, the number of use cases in production rather than in pilot, the change in the target metrics, adoption by the intended users and the cost per use case. Reviewing these every quarter, and stopping initiatives that do not deliver, keeps the portfolio focused.
- Are the chosen use cases in production and used by the intended teams?
- Have the agreed business metrics improved against their baselines?
- Have data quality issues identified in pilots been fixed at the source?
- Do employees know which tools they may use and with which data?
- Is spending on AI concentrated on the priorities, or spread thinly?
A good AI strategy is modest in scope and demanding in execution. Few priorities, clean data, clear ownership and honest measurement matter more than the choice of any particular model.
Frequently asked questions
What should an AI strategy for a fashion brand include?
It should include a small number of business outcomes AI is meant to improve, a prioritised list of use cases with owners, a data improvement plan, sourcing principles for technology, a skills plan, governance rules and a way to measure results. Each part should be short enough to explain quickly to the people who will use it.
Who should own AI strategy in a fashion company?
A senior executive should own it, ideally someone accountable for business results rather than only for technology. IT, data, legal and HR teams play essential roles, but use cases need business owners. McKinsey's research links senior leadership commitment with stronger AI results.
How long should an AI roadmap be?
Because AI technology changes quickly, a rolling roadmap reviewed every quarter tends to work better than a fixed multi-year plan. Firm commitments for the next two quarters and options beyond that keep the plan realistic. The roadmap should move from foundations and pilots to scaling proven use cases.
Do fashion brands need ISO 42001 certification?
No, certification is voluntary. ISO/IEC 42001 sets out requirements for an AI management system, and many companies use it as a structure for governance without seeking certification. Certification can become relevant when large customers or partners ask for proof of responsible AI practices.
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