Your team is manually entering product attributes across five systems. They need to publish 2,000 SKUs next month with localized copy in six languages. Your wholesale buyers are asking for replenishment predictions. Your merchandisers are trying to predict which products will drive the most returns. 

These are AI problems, but most of what you read about AI in fashion focuses on futuristic shopping experiences, not the operational challenges you're facing today. We wanted to understand what's actually working for brands right now.

This guide draws on interviews with fashion commerce leaders and survey data from Centra clients. It covers: 

• Where mid-market and enterprise fashion brands actually are with AI

• Principles for success with AI

• Real barriers brands hit and how they're dealing with them

• Four use cases to deliver ROI in 3-6 months

Our approach: start with business problems, not technology trends. The question isn't “what AI should we implement?”- it's “where are we wasting time that AI could help with?”

If you're evaluating vendors, planning AI initiatives, or just trying to figure out where to start, this is for you.

The Current State: Optimistic but Pragmatic

Based on our research with fashion commerce leaders: "Everybody's going AI," as one leader put it, but adoption is largely internal and assistive, not customer-facing. Among Centra clients, half are scaling selected use cases, while a third are still in early exploration. As one leader said, "Nobody's broadly adopted across teams" (yet.)

Based on our research with fashion commerce leaders and Centra clients, the brands seeing measurable results from AI share five common approaches.

1. Focus on Value, Not Hype

AI success starts with measurable outcomes: faster product data readiness, higher sell-through, lower returns.

Fashion leaders tell us the brands seeing real ROI from AI started with a specific business problem, not a technology exploration. They identified workflow bottlenecks where data handoffs slow things down, then automated those first.

Tim Richardson, CEO of ROIROI, an ecommerce agency built for the AI era, suggests that brands start by looking where you're spending the most money on manual work. "Analyse your P&L for the highest cost versus highest manual effort items. Pick the top three. That's a good starting point for AI."

Among Centra clients already measuring AI impact, the top results are concrete:

• 40% report faster time to publish products

• 27% saw a reduction in customer service workload
  

What this looks like in practice:

Instead of "implement AI across product operations," successful brands ask: "Where do we spend 10+ hours per week on manual work that could be automated with 90%+ accuracy?"

For most fashion brands, that's product enrichment: writing descriptions, translating content, tagging attributes. It's not exciting, but it's valuable.

2. Empower Teams, Don't Replace Them

The best AI outcomes come from upskilled teams using tools safely and creatively, not from replacing human judgment.

The brands moving fastest have built what one leader called "safe-to-try sandboxes" - environments where teams can experiment with AI tools, learn what works, and develop their own workflows without breaking production systems.

What teams actually need:

• Access to approved AI tools integrated into daily workflows

• Training on both capabilities and limitations

• Clear guidelines on what requires human review

• Permission to experiment without needing executive sign-off for every test

Among Centra clients, 83% of ecommerce teams are actively using AI, followed by 50% of marketing teams and 42% of customer service teams. But usage doesn't equal proficiency; the skills gap is real, especially for junior team members learning both the job and the tools simultaneously.

Anton Johansson, CEO & founder of eCommerce agency, Grebban, has seen this accelerate adoption with clients. One of the most effective approaches he's seen: small internal hackathons that give teams a safe, time-boxed space to experiment together — whether that's vibe coding, automations, or rethinking daily workflows with AI. "It helps people step outside their comfort zone and quickly discover what's genuinely useful," he says.

There's also tension between what executives expect and what implementation requires. One leader described C-level leadership "expecting immediate things out of it" and thinking AI can be used "like Google search," when implementation actually requires training, workflow integration, and change management that takes months. The brands moving fastest are those where both teams have the skills they need AND leadership expectations are calibrated to reality.

3. Integrate, Don't Add Noise

The right AI lives inside your daily workflows, not as another tab or tool that breaks your process.

One of the most consistent findings from our research: fashion leaders are exhausted by tool sprawl. They don't want another point solution that requires switching contexts, manual data export, or custom integrations that break every time a vendor updates their API.

Jamie Hancox, eCommerce advisor and founder of Commerce Futures puts it this way: "The greatest reward it seems in apparel is to align product information with great copy and imagery. AI clearly has a place here, but another tool isn't the answer. Instead brands are thinking about tweaking workflows and teams—gradually getting better and better with the AI in their existing stack to bring these together."

As one merchandise operations leader described, even with sophisticated systems, incomplete product attribute data in platforms forces manual follow-up that "undermines automation potential."

The brands seeing results from AI are those who've integrated it into existing workflows, not added it on top.

What good integration looks like:

• AI-powered product enrichment happens inside your PIM, not in a separate tool

• Inventory recommendations flow into your OMS and ERP

• Customer service AI has real-time access to order status and product data

• Wholesale AI connects to actual availability and buyer history

Among brands we work with, product data enrichment is the top opportunity (75%) precisely because it sits at the center of so many workflows. Get your product data right with AI assistance, and everything downstream, site merchandising, marketplace optimization, wholesale presentations, improves automatically.

4. Organize Around Outcomes

Break down silos and focus on goals that matter, like reducing returns, launching faster, or improving sell-through.

The traditional org structure for fashion commerce – separate teams for ecommerce, wholesale, merchandising, creative, and operations – works against effective AI implementation. AI capabilities that could drive results get stuck in departmental budgets, vendor selection committees, and competing priorities.

The brands seeing the most impact have created cross-functional pods that own full outcomes. Instead of "the ecommerce team owns the website" and "the wholesale team owns B2B," they've organized around "time from design to sale" or "customer return rate" or "wholesale reorder velocity."

As Luke Hodgson, founder of Commerce Thinking, puts it: "The flashy customer-facing thing is easier to show progress on. It looks good in a deck. Meanwhile, the wins are in clear workflows and taking friction out. That's the stuff that cuts cost, moves faster, and stops trading teams firefighting."

Why this matters for AI:

AI tools are most powerful when they can access data and drive actions across traditional boundaries. A return prediction model needs product attributes (merchandising), order history (ecommerce and wholesale), customer service notes (support), and inventory position (operations). No single department owns all of that.

When brands organize around outcomes, they can deploy AI where it has the most impact, even if that crosses departmental lines.

5. Build Trust Through Transparency

Ethics, explainability, and control will define lasting brand trust in AI implementations.

Customers and buyers are getting sophisticated about AI. They know when they're talking to a bot. They notice when recommendations feel algorithmic. They care how their data is used.

Ed Bull, Director and Owner of eCommerce agency, Limesharp sees the longer-term vision: "AI will not transform fashion through efficiency. It will do so through brand-driven, agentic experiences that inspire and assist. Shopping is emotional, intelligence needs taste. That is where we believe the opportunity sits."

But those brand-driven experiences only work if customers trust them. The fashion brands that will win long-term are those building AI implementations with transparency and control:

For DTC customers:

• Clear labeling when AI is making recommendations or generating content

• Ability to opt out of AI-driven personalization

• Transparency about what data drives what experiences

For wholesale buyers:

• Understanding which forecasts are AI-driven vs. human judgment

• Control over automated replenishment suggestions

• Clear accountability when AI recommendations don't match market reality

For internal teams:

• Explainable AI outputs (not just "the model said so")

• Human override capabilities at every decision point

• Clear audit trails for compliance and learning

According to our research and experience with fashion brands, here are the four biggest barriers to AI usage and how to address them.

Barrier 1: Accuracy and Trust

The problem: Even simple outputs can be wrong. Multiple leaders mentioned hallucinations and accuracy issues with AI-generated content, one cited "some of the most simple math has been misinterpreted."

This isn't just annoying; it's existential for brand trust. One wrong product description or sizing recommendation can have real cost in the form of returns or damage brand reputation.

How brands are addressing it:

Build a human-in-the-loop QA culture. Every brand seeing AI success has explicit processes for human review of AI outputs. This doesn't mean reviewing every single output, it means:

• Spot-checking AI-generated content at regular intervals

• Having clear escalation paths when something seems off

• Training teams to recognize common AI failure modes

• Building feedback loops so the AI improves over time

As one digital leader put it: "Only a human would have been able to catch" certain errors. Accept this reality and build workflows accordingly.

Barrier 2: Security and Governance

The problem: Brands are balancing innovation with security. Leaders want teams to experiment with AI, but data security and compliance concerns mean they can't just let everyone use whatever tools they want.

How brands are addressing it:

Tool standardization with clear governance. Brands are consolidating AI usage inside approved enterprise environments. Multiple leaders described being "provided guardrails" to keep work inside approved AI ecosystems, replacing what one called "shadow AI" usage.

This isn't about being conservative, it's about being smart. Data security and governance concerns are simultaneously accelerating adoption (through standardization) and constraining it (through guardrails). Brands that get this balance right move faster.

Barrier 3: Data Complexity and Incomplete Information

The problem: AI is only as good as the data it has access to. In fashion commerce, that data is typically scattered across ERPs, PIMs, OMS platforms, POS systems, and more, often with incomplete or inconsistent attribute data.

As one merchandise operations leader described, even with sophisticated systems, "incomplete attribute data in platforms forces manual follow-up, undermining automation potential."

How brands are addressing it:

Workflow targeting + data cleanup. Rather than "implement AI everywhere," successful brands focus on discrete, high-friction workflow points where data quality is good enough to start:

• If product attribute data is 80% complete, start with AI enrichment to close the gap

• If order history is clean but customer data isn't, focus AI on demand forecasting

• If wholesale booking data is accurate, deploy predictive replenishment there first

The key insight: you don't need perfect data everywhere to start getting value from AI. You need good enough data in one specific workflow.

Barrier 4: Fashion Unpredictability

The problem: Fashion is partially trend-driven and inherently unpredictable. As one operations leader put it, even with better forecasting systems, "a meaningful portion of trend-driven demand remains hard to predict."

How brands are addressing it: Use AI for core/repeat product demand (where historical patterns matter). Keep human judgment central for trend/fashion-forward assortments. Use AI to identify early signals that inform human decisions.

You don't need to implement AI everywhere at once. Based on what's working for brands today, here are four specific use cases you can start with that deliver measurable results in 3-6 months.

Use Case 1: Generative PIM Enrichment

What it does: AI automatically generates product descriptions, translations, SEO content, and attribute tags based on product images and basic data inputs.

Expected outcomes:

• Faster translations and localized copy (in existing workflows)

• Reduced content production costs

• Faster time to publish new products

• More complete product data for search and discovery

What you need:

• A PIM with structured product data

• API access to connect AI enrichment tools

• Clear brand voice guidelines for AI to follow

• Human review workflow for QA

Why start here: Among brands we surveyed, 73% already have AI live in product enrichment, and 87% see it as their biggest opportunity. It's proven, low-risk, and immediately measurable.

How Centra enables this: Centra's PIM supports structured product data with generative extensions. The system exposes full product context, category, collection, materials, variants, through APIs, giving AI enrichment tools the information they need to generate accurate, on-brand content.

Use Case 2: Automated B2B Replenishment

What it does: AI predicts wholesale buyer demand based on historical order patterns and sell-through data, then triggers replenishment flows with approval loops.

Expected outcomes:

• Faster order cycles (buyers spend less time on manual reordering)

• Higher accuracy (AI catches stockout risks before they happen)

• Better inventory efficiency (right products, right quantities, right timing)

• Stronger buyer relationships (proactive service)

What you need:

• Historical wholesale order data

• Integration between your wholesale system and inventory/OMS

• Buyer-specific terms and pricing rules

• Approval workflows for suggested orders

Why start here: This directly addresses one of wholesale's biggest pain points: manual reorder processes that waste time for both brands and buyers. It's high-impact and relatively low-risk because humans approve everything.

How Centra enables this: Centra's wholesale module tracks buyer-specific terms, pricing, and order history. The OMS provides real-time inventory visibility. Together, these create the foundation for AI to predict optimal replenishment timing and quantities, with approvals handled in your existing planning and governance workflows.

Use Case 3: Pre-Order Cancellation Prevention

What it does: AI detects wholesale orders likely to be cancelled (based on patterns like delayed shipments, inventory shortfalls, or buyer history) and guides upsells, substitutions, or proactive communication before revenue is lost.

Expected outcomes:

• Lower wholesale cancellation rates

• Reduced margin erosion from emergency discounting

• Stronger buyer relationships (proactive communication beats surprises)

• Protected revenue in orders at risk

What you need:

• Historical data on cancelled orders and why

• Real-time order status and fulfillment tracking

• Integration between wholesale, OMS, and inventory systems

• Account manager workflows for intervention

Why start here: Wholesale cancellations are painful for everyone -  AI can spot risk signals early enough to intervene.

How Centra enables this: Centra's Digital Showroom and linesheets integrate with the OMS to track order status, identify fulfillment risks, and flag at-risk orders. Because all the data lives in one system, AI models can see the full picture and make accurate predictions.

Use Case 4: Predictive Return Mitigation

What it does: AI spots return-prone products early (based on patterns in product attributes, customer segments, and historical return data) and adjusts guidance, merchandising, or fulfillment accordingly.

Expected outcomes:

• Reduced return rates (through better fit guidance, expectations management)

• Lower reverse logistics costs

• Higher customer satisfaction (getting it right the first time)

• Better inventory planning (knowing what's coming back)

What you need:

• Return reason data tagged to products and customers

• Integration between OMS, customer service, and product data

• Ability to adjust product pages or merchandising based on AI signals

• Feedback loop so AI learns from interventions

Why start here: Returns are one of fashion's biggest margin killers, and one of the most predictable with good data. Even small improvements in return rates drive significant bottom-line impact.

How Centra enables this: Returns, product, customer, and order data all live in Centra. This creates the foundation for predictive return models, you can identify which product/customer combinations are return-prone and adjust guidance, merchandising, or fulfillment accordingly.

The AI landscape for fashion commerce is constantly shifting, but the brands who are winning aren’t waiting for perfect clarity to take the first step. They’re building a strong foundation, and learning as they go.

These brands don't just deploy the flashiest tech. They start with high-value, low-risk use cases that solve real operational problems. 

The brands seeing results are those with

• Clean, structured commerce data that AI tools can actually use

• Teams with permission to experiment safely

• Leadership expectations calibrated to reality

• A focus on measurable outcomes, not technology for its own sake

Where Centra Fits

Centra is the commerce platform built for fashion brands that sell DTC and wholesale. Our goal is simple: make AI practical by enhancing what works today and preparing you for what's next.

Today: Centra is API-first and fashion-native. Core commerce data like product relationships, multi‑market pricing, inventory availability, market configuration, and customer attributes, is exposed through well‑structured APIs. AI systems can access clean, structured, fashion‑specific information directly from Centra, so you’re not stuck reverse‑engineering messy storefront data.

Tomorrow: We're embedding AI inside our fashion-native commerce platform to turn it into a growth engine for brands. Our focus is to drive core commercial KPIs, not just add flashy features. 

Why this matters:

You don't need to rip out your commerce stack to start getting value from AI. You need a platform that makes your data AI-ready and lets you connect the tools your teams already know, while maintaining the security, governance, and integration quality that enterprise brands require.

What to Do Next

Don't start with "we need an AI strategy." Start with "we have a business problem that AI might help solve."

Pick one use case from Part 4. Ensure your data foundation is solid. Build team capability alongside technology. Measure what matters.

About this guide:This guide is based on qualitative research with fashion commerce leaders (conducted via GetWhys), survey data from Centra clients across Europe, and real-world examples from brands implementing AI in production today. It was created by Centra's marketing team in collaboration with product and customer success leaders who work with enterprise fashion brands daily.