In fashion retail, AI tools are changing rapidly, and it's often hard for teams to decipher which ones are going to effect the right change. This mounts the pressure on retailers to choose the best tools and platforms as the industry heads further into the AI landscape.
Before finding yourself and your team in AI purgatory, discover the strategic and actionable tactics around AI best practices from retail leaders who've done the hard work to figure out what success looks like.
Taken directly from the AI is The New Black webinar featuring Style Arcade CEO and Co-Founder Michaela Wessels, CTO Tristan Hoy and 12th Tribe's Senior Director of Buying and Product, Patricia Infante, this is your cheat sheet to define the strategies and the data foundations that set your team up for sharper decision making.
Whether that’s avoiding building systems that lock them into one provider, keeping customer and product data in stable foundation layers, or creating AI models as interchangeable innovation layers, consider this your guide to navigating the AI landscape for fashion retailers in 2026.
Plus, explore the 3 actions fashion retailers can take now before scaling AI.
What fashion retailers need to know about AI in 2026
AI's biggest opportunity is improving decision-making
AI is not replacing teams. The goal isn't fewer merchandisers and planners; it's to create more strategic ones. Right now, buyers and planners spend significant time gathering data, building reports, creating SKUs, raising POs, and managing administration. Much of this work is repetitive and low-value.
The ideal future state is one where AI handles data collection, calculations, and preparation so buyers and planners can spend more time on:
- Assortment strategy
- Inventory decisions
- Cross-functional collaboration
- Commercial judgment
- Customer insights
Operational efficiency is the easiest AI win
The panel repeatedly returned to operational tasks as the most practical starting point. Before chasing advanced AI use cases, identify where teams are manually re-entering the same information across systems.
Examples included:
- SKU creation
- Purchase order generation
- Returns processing
- Refund workflows
- Product data management
Patricia highlighted a common fashion problem where a product is effectively created multiple times across disconnected systems.
For example, a product created in the planning/development platform is then re-created in Shopify and again for purchasing. This duplication creates labour costs, errors, delays and scalability constraints.
AI's real value is connecting systems, people, and processes
A particularly interesting point was that many AI opportunities sit in the "glue layer" between systems.
Fashion businesses often operate with:
- ERP systems
- Shopify
- Planning tools
- PIM systems
- Warehouse systems
- Marketing platforms
The challenge is often not the software itself but the handoffs between them. Tools like Claude can help businesses build custom integrations, workflow automations, and process-specific applications without requiring expensive consulting projects.
Fashion brands are moving from AI insights to AI-driven workflows
Patricia made an important distinction: Many businesses currently use AI for summaries, reports, and general insights. Now, the focus shifts from "What happened? To "What should we do next?"
The winners will be brands that operationalise AI into decision workflows rather than simply generating reports faster.
The next phase is:
- AI-supported inventory decisions
- AI-assisted marketing decisions
- AI-driven operational workflows
CEOs are creating pressure, but implementation maturity varies
The panel acknowledged a growing trend of CEOs asking: "What are we automating?", "How are we using AI?", and "Why aren't we moving faster?". This pressure is widespread across fashion retail, with most fashion retailers still figuring out practical applications rather than operating fully AI-enabled businesses. However, the discussion suggests many organizations are still in early stages:
- Experimenting with Claude
- Testing AI assistants
- Building internal tools
- Exploring custom models
Data security is becoming a major strategic issue
A significant portion of the conversation focused on governance and privacy, stressing that governance should extend beyond employees to technology partners and vendors.
Recommendations included:
- Creating company-wide AI policies
- Defining acceptable AI use cases
- Restricting what employees can upload
- Reviewing vendor contracts
- Ensuring third-party technology providers align with AI policies
There is a trade-off between security and cost
Fashion businesses need to decide whether their priority is cost efficiency, data security, or a balance of both. Currently, there are two broad approaches:
Managed AI platforms
Examples:
- Claude Enterprise
Benefits:
- Easier administration
- Better cost controls
- Usage monitoring
- User quotas
Trade-off:
- Lower data security
Private AI infrastructure
Examples:
- Claude through AWS Bedrock
Benefits:
- Data remains within company infrastructure
- Greater privacy
- Stronger compliance
Trade-off:
- Higher costs
- More technical complexity
- More difficult cost management
LLMs should not be used to analyze massive datasets directly
Instead of uploading huge datasets to an LLM and asking AI to analyze everything. The suggested approach is:
Step 1
Use scripts and automation to:
- Clean data
- Aggregate data
- Surface key metrics
Step 2
Use the LLM to:
- Interpret findings
- Make recommendations
- Explain trends
Benefits:
- Lower token costs
- Better performance
- Improved security
- More consistent outputs
Then your team can move on to connecting planning, inventory, ecommerce and marketing workflows, build AI into decision-making processes, and free teams from reporting and administration.
3 strategic AI actions fashion retailers can take now
Retailers often think their AI problem is a technology problem. In reality, it's usually a data and operating model problem.
1. Fix data foundations before scaling AI
The KPMG findings showed that retailers are worried about AI skills, but the panel challenged this thinking by arguing that poor data quality is the bigger issue.
The message was clear:
- AI amplifies the quality of your data.
- If your product, inventory and customer data are inconsistent, AI will produce inconsistent outputs.
- Before investing heavily in AI tools, retailers should focus on data governance, product attribution and system consistency.
Don't start with AI adoption. Start with clean, structured and accessible data.
2. Standardise product and SKU architecture across systems
Tristan's biggest practical recommendation was to make products easier for systems—and future AI agents—to understand.
Key principles:
- Ensure each product variant can be uniquely identified.
- Structure SKUs so style, colour and size can be easily separated and recombined.
- Avoid creating complex workarounds or inconsistent naming conventions.
- Create product identifiers that work consistently across Shopify, ERP, planning and inventory systems.
Why it matters:
- Reduces integration issues.
- Improves reporting accuracy.
- Enables AI agents to join and analyze data across platforms more effectively.
Treat product and SKU architecture as AI infrastructure, not just merchandising administration.
3. Invest in AI literacy at the leadership level
Successful AI adoption starts with leadership understanding the business problem first, and the technology second.
The panel recognized leadership understanding is a significant barrier for AI adoption in fashion businesses. IT leadership should be continually exposed to AI resources and tools to be able to develop and implement strong policies first, before empowering their business with AI.
This will help leadership teams identify:
- Where AI can create value.
- Which workflows should be prioritized?
- Which initiatives are realistic versus hype?
Retailers often think their AI problem is a technology problem. In reality, it's usually a data and operating model problem. In the long term, fashion retailers should be looking to create an AI-enabled operating model where buyers, planners, and marketers spend less time gathering information and more time acting on it.
The future isn't AI replacing buyers and planners. AI is preparing and connecting the necessary data information so buyers and planners can spend their time making better commercial decisions.
