How AI can support fashion companies

The modern fashion industry operates under constant strain, where traditional forecasting models frequently clash with unpredictable market realities. Siloed workflows, fragmented planning systems and a heavy reliance on manual processes mean that crucial information is often trapped in disconnected spreadsheets, tech packs and erratic communication channels.

When supply chain disruptions, factory shutdowns or sudden shifts in consumer demand occur, brands are left reactive, unable to pivot quickly enough to protect their margins. This structural disconnect leads to late deliveries, fractured multi-channel product information and devastating out-of-stock scenarios that erode consumer trust and wipe out full-price sell-through. To adapt better, the industry is well advised to transition from legacy guesswork to a connected, intelligent ecosystem driven by artificial intelligence (AI).

Software provider Aptean recently introduced its new tool, Aptean Fashion & Apparel, which automates decisions and unifies workflows for the fashion and apparel industry, from design to delivery, thus giving teams real‑time visibility across styles, colours and dimensions. Five online sessions by industry insiders on 14th May shed light on how the tool can help the industry across the supply chain and departments. FashionUnited has summed up how industry insiders use AI in the design phase, on the factory floor, when writing content about a product and launching it to balancing demand through smart inventory control.

Protecting revenue with style substitution

In high-volatility sectors like the fashion industry where consumer demand can instantly spike due to modern digital forces like social media influencer campaigns, traditional inventory replenishment cycles—which typically span 60, 90 or 120 days—fail to keep pace.

“If a customer's or a consumer's preferred style is out of stock and no one can quickly identify a comparable alternative, you likely have lost the sale. They have moved on, gone to your competitor, another apparel brand, found something they liked and you have lost that sale,” explains Ken Weygand, solutions architect at Aptean. He has been working with fashion, footwear and accessories brands to help them implement both Enterprise Resource Planning (ERP) and Product Lifecycle Management (PLM) solutions to improve and enhance their business operations.

When a preferred garment or size becomes unavailable, brands and retailers face a high probability of permanently losing the consumer to market competitors unless an identical or highly equivalent alternative can be immediately identified at the point of sale. This is where an autonomous “style substitute agent” comes in. “It is all about finding a substitutable style, comparable alternatives quickly so that we can service our customers. … It can do so rather quickly in real time… and can also validate against live inventory. There is no point in looking at alternative styles where we have no inventory,” states Weygand.

Designed to mitigate the immediate revenue losses associated with product stockouts, the operational integration of this technology addresses the structural limits of managing expansive corporate databases that frequently encompass hundreds of thousands of distinct Stock Keeping Units (SKUs) across multiple fabric types, categories and fits. Fragmented frontend teams—ranging from customer service representatives to e-commerce and showroom account managers—are usually forced to navigate disjointed software systems to manually identify alternative items. This data fragmentation induces critical transactional delays and financial margin exposure, such as accidentally offering a higher-cost alternative at a lower tier price point, underscoring the vital requirement for systemic automation.

Deploying an AI agent directly over core ERP and PLM architectures provides an automated infrastructure governed by strict corporate logic. The system evaluates potential substitutions by systematically parsing product attributes—including fabric composition, garment rank, silhouette and retail value—against current safety stock parameters and historic design data. By abstracting these backend data layers into simplified, low-code interface lookups through centralised navigation systems like Aptean, users can execute smooth context switches on the sales floor to save transactions.

Optimising production flow at Hanesbrands: visibility from factory to floor

Moving from blueprints to physical creation, supply chain variability frequently disrupts the operational rhythm of mid-season manufacturing. Traditional ERP frameworks struggle with the multi-curved, high-dimensional block systems unique to fashion manufacturing, resulting in fragmented metrics and delayed visibility. AI systems actively bridge this gap by establishing real-time shop floor control, connecting raw material procurement directly to machinery outputs. This holistic, interconnected oversight transforms physical data into actionable intelligence, preventing the typical bottlenecks that stall production lines.

Explaining the necessity of this shift during global disruptions, Hemant Ramaswami, VP of digital transformation at Hanesbrands, recalled how the US clothing company moved from “simply identifying supply chain exceptions to really being able to address them in near real time” during the Covid pandemic. “Global supply chains do not fail because of missing data. They fail typically because the right data does not reach the right decision makers at the right time,” he emphasised.

Talking about distribution centres no longer being reflective of what was needed to support the customers, Ramaswami identified three core issues: elongated lead times and lack of visibility, fragmented signals and having no allocation engine. “It took a lot of effort for a customer support individual to be able to actually put together a complete picture and answer questions on when product would be available,” remembers Ramaswami.

Starting with a very large volume and highly profitable business segment - men’s underwear - Hanesbrands piloted operational AI software, expanding into more complex categories once they were able to justify the value and see the potential. Predictive logic enables manufacturers to remain highly responsive rather than merely reactive to sudden factory disruptions. Instead of relying on week-old data logs or intuitive human guesswork, operations teams leverage live data streams to identify structural exceptions instantly. By automatically mapping out alternative processing paths and rebalancing workloads across active factories, AI preserves critical delivery windows and minimises margin erosion. “It changed from being in a reactionary mode to more like a fire prevention mode,” states Ramaswami.

Especially useful was container prioritisation logic: “We typically get anywhere from 30 to 40 containers a day at some of our distribution centres. So it is very important to make sure that the unloading team is focused on the highest value containers. …Unloading the right container could be the difference between making the quarter or missing it.” Assigning a dollar value to each container helped tremendously. “It is not even an abstract AI running somewhere in the background. It is actually a dock supervisor looking at the screen that says what we need to unload first and what is on it,” summed up Ramaswami

Real-time protection: navigating volatility at product launch

The transition from the factory floor to the retail market represents one of the most volatile phases in the retail lifecycle, particularly when consumer demand shifts unexpectedly. A product launch can easily collapse under the pressure of fragmented commercial signals, leading to inventory mismatches where certain distribution centres end up overstocked while others face immediate stockouts. AI algorithms dynamically intercept these retail signals at launch, continuously evaluating regional sales trends against live inventory metrics.

Reflecting on how easily a season can slip away without intervention, Aly Breeman, senior product manager at Aptean, observed: “Every season a brand somewhere does everything right. The collection is strong, the designs are sharp and the marketing is ready and then quietly things begin to slip. A delivery arrives late, a warm October stalls the winter coats.The wholesale partners move at a different pace than the webshop. None of it feels dramatic at first but by the time the numbers tell the story the margin is already gone and the only opportunity left is a markdown. This is not bad luck, it is a pattern and like most patterns, once you see it clearly, you can plan ahead for it.”

“In fashion, timing is everything. Seasons unfold through three predictable forces,” she continued. Those are supply variability, changes in demand and channel fragmentation. Producing margins begins long before collections reach the shop floor, according to Breeman, it begins with planning. “Plan the production sequence correctly and every delay has its direct ripple effect on availability and completeness. Waiting until collections are on the shelf and your only option becomes discounting. And discounting is a margin killer,” she warns.

To enable adaptability, companies first need to be able to spot margin risk as early as possible. “One of the biggest challenges for brands is identifying underperforming quickly enough to do something about it and before it starts to affect profitability,” she adds. That is where AI can add much value: It can help by flagging potential production delays, capability issues and other risks much earlier, giving the brand time to step in and take corrective actions.

Once the collection is on the market, AI can keep track of performance in real time, not just on a high level but down to style, colour, season and location. Brands get a much clearer view of what is working and what is not and where they may need to rebalance stock. It can also support smarter replenishment by recommending the right style in the right store at the right time.

“Ultimately, AI helps brands move from reacting too late to acting sooner with much better visibility control across the whole product lifecycle,” says Breeman. “The power of AI starts with the quality of its input,” she cautions. “Generic ERP solutions do not speak fashion language. Seasons, styles in multiple colours, size curves and delivery dimensions are building blocks of how fashion moves. But in a generic system, they get lost in translation.”

“Laying AI on top of a poorly quality data or fragmented systems only amplify the problem. If different teams work from different versions of the truth, if the product data is not maintained properly, AI will make things only worse and not better,” knows the product expert. “So the answer is not simply to add AI, it is to ensure that businesses are ready to use it well. And that means focusing on data quality, governance and consistency first. The more aligned your systems are to the industry, and the more disciplined your data management is, the more effective the impact.”

Content automation: crafting accurate, targeted and compelling copy

As items hit the digital shelf, the demand for rich, accurate product data becomes paramount for conversion; data from the Salsify Consumer Research 2024/2025 indicates that a staggering 88 percent of shoppers say that product content is extremely or very important to their purchase decision. Despite this, fashion brands regularly leak revenue because of incomplete attributes, with half of consumers admitting to abandoning online shopping carts due to poor product descriptions.

“When content is missing or off-brand, it does not just create extra work. It loses sales,” confirms Alain Tessier, director of product management at Aptean. He explains that there are mainly four steps of how AI can fix this: Step one is reading the source; step two is picking what matters; step three is writing the content and step four is review and publish.

“AI takes in whatever you have, PDFs, spreadsheets, images that are from your system, and reads it all. This alone normally takes a writer 30 to 60 minutes per product before a single word is written. AI does it in seconds,” emphasises Tessier. In terms of content, AI figures out what to highlight based on where the content is going and adapts it for each audience: “The same product data becomes a product description for the website. It becomes a summary for buyers, a listing for a magazine and maybe a caption for social media. Each one the right length and tone for that channel,” states Tessier.

But that does not mean that the team does not stay in the loop. Instead of writing from scratch, team members review, adjust, and approve. “The taking stays with the people; the writing work moves to the AI side. What used to take three to five days now takes probably under 30 minutes,” sums up Tessier.

This automated approach maintains strict global consistency while eliminating the manual errors that frequently plague high-volume item setups. Rather than allowing product data to drift loosely across different retail channels, AI systematically enforces corporate style guides, localised terminology and exact brand definitions. It automatically flags critical construction anomalies—such as a jacket being listed as insulated down when the tech specifications dictate a synthetic build—thereby protecting the brand from costly returns and compliance penalties. By trimming content generation timelines from days to mere seconds, brands accelerate their time-to-market and ensure listings remain perfectly accurate across all digital touchpoints.

Fast-tracking production decisions at NSA

The final pillar of a resilient fashion ecosystem lies in sophisticated, automated production environments and inventory control.

Kelly Deady, senior director of Chicago Operations at US apparel manufacturer National Safety Affair (NSA) talked about how AI helped streamline data at the company’s four manufacturing sites (in California, Illinois, Kansas and Ohio). “Every single site had a different efficiency system, and none of them talked to each other. We finally are all on the same ERP but it does not have the data that we need to really drive down into efficiency and just optimise our online in any way possible,” recalls Deady. “We also send the same reports to the same person but we have to edit things all the time in order … to [get] an apples to apples comparison of the data. So going with Aptean has been a huge game changer for NSA,” adding that predicting late orders or balancing multi-sites by either looking at cost or at efficiency has been the biggest gain.

Making sure to have the data to back up promises in regards to being able to function quickly, function effectively, and assess what type of bottlenecks they could run into is what helps maintain a “Made in USA” brand. “Anything that can predict the unexpected is awesome because manufacturing every day is unexpected,” adds Deady.

Traditional inventory management relies on historic seasonal patterns, which leaves brands highly vulnerable to unpredictable market shifts, late logistics arrivals and sudden regional demand drops. AI breaks this rigid loop by continuously running complex predictive scenarios, calculating precise trade-offs between localised stock levels, shipping overheads and promotional markdowns.

This systemic oversight translates directly into automated, real-time inventory rebalancing across diverse direct-to-consumer and wholesale networks. Instead of forcing regional teams to manually sifting through massive spreadsheets to locate missing size curves, the AI autonomously calculates the exact metrics required for precise, calculated replenishment. It dictates exactly when to move sluggish stock from underperforming brick-and-mortar floors to high-velocity e-commerce hubs, ensuring maximum full-price sell-through. By executing these tiny, continuous operational adjustments across the product lifecycle, fashion enterprises can drastically lower total days unsold and maintain exceptionally lean, highly profitable supply chains

“Having an AI agent is like having another set of eyes that are always looking at it, waiting for whatever it is that you have trained it to come up. So you are going way beyond a report that is pulling data or a tool that is showing dashboards or screens. You actually have something that is looking at it that has more of like a human-type of brain aspect to it in the way it is looking at the information,” concludes Deady.

Conclusion: synthesising the automated fashion enterprise

Embracing AI across the fashion lifecycle is no longer a futuristic experiment; it is a fundamental commercial necessity for brands looking to protect their margins in an unforgiving market. By linking design, production, launch, copy creation and inventory control into a single, cohesive intelligent ecosystem, fashion enterprises eliminate the disconnected data silos that historically stall growth. The results are profound: dramatically shorter development cycles, minimal stockout friction, flawless data integrity and highly optimised stock allocation. Stakeholders who implement these integrated AI solutions effectively future-proof their operations, replacing legacy operational guesswork with a precise, highly responsive architecture built to thrive on market volatility.