What Role Can AI Play in Helping Fashion Brands Reduce Financial Losses Caused by Excess Inventory and Markdowns
For fashion brands, excess inventory is more than a storage problem. Unsold clothing ties up working capital, takes up warehouse and retail space, and often ends up being sold at discounts that weaken margins. The challenge is particularly difficult in fashion because demand can change quickly based on trends, seasons, weather, consumer preferences, and economic conditions.
AI is emerging as a practical way to address this problem. By combining historical sales data with real-time demand signals, AI can help brands make better purchasing, allocation, pricing, and markdown decisions before excess stock becomes a serious financial liability.
McKinsey estimates that fashion brands produced 2.5 billion to 5 billion excess garments in 2023, representing an estimated $70 billion to $140 billion in excess stock at sales value.
How AI Helps Prevent Excess Inventory
More Accurate Demand Forecasting
One of AI’s most valuable applications is demand forecasting. Traditional forecasting often depends heavily on historical sales and manual adjustments. AI can analyze far more variables, including sales history, seasonality, customer behavior, search trends, regional demand, promotions, and other external signals.
This allows brands to estimate demand at a much more granular level, such as by product, size, color, location, and sales channel.
McKinsey notes that AI-supported demand forecasting can help brands understand which consumers are likely to buy particular products, in which markets, and at what time. Better forecasting can improve sell-through while reducing both excess stock and stock-outs.
AI can help fashion brands make fewer expensive inventory mistakes by improving how they predict demand. According to Chongwei Chen, President & CEO at DataNumen, instead of relying mainly on last year’s sales or gut feeling, AI can analyze sales trends, seasonality, customer behavior, pricing, weather, and even regional demand to estimate what is likely to sell.
The bigger opportunity is catching problems early. If a product is moving slower than expected, AI can flag it while there is still time to adjust purchasing, shift inventory between locations, change promotions, or test pricing before a heavy markdown becomes necessary.
I also see value in using AI to identify patterns across products—such as which colors, sizes, or styles consistently end up overstocked. That gives brands better information for future buying decisions. AI won’t eliminate inventory risk, but it can make those decisions faster, more data-driven, and less dependent on guesswork.
Smarter Inventory Allocation
Having too much inventory overall is only part of the problem. Fashion brands can also lose money when the right products are sitting in the wrong locations.
For example, one store might have excess medium-sized jackets while another location is selling out of the same item. AI can analyze sales velocity, inventory levels, local demand, and fulfillment costs to recommend where inventory should be moved.
This can increase the chances of selling products at full price instead of eventually sending them to clearance.
McKinsey identifies inventory allocation and management of inventory churn as important opportunities for AI and digitization in fashion.
Better Decisions Before the Purchase Order
AI can also help buyers decide how much not to buy.
This is an important distinction. Inventory optimization shouldn’t begin when products are already sitting in a warehouse. AI can analyze previous product performance and comparable styles to recommend more appropriate quantities before production or purchasing decisions are finalized.
For fashion brands, this can reduce the financial exposure created by committing too heavily to products with uncertain demand.
AI Can Make Markdown Decisions More Precise
Markdowns aren’t inherently bad. The problem is using them too broadly or too late.
A blanket 30% or 50% discount might clear stock, but it can also sacrifice margin on products that could have sold with a smaller reduction.
AI can evaluate the likely response to different prices and estimate how much inventory could sell at each discount level.
Predicting Price Elasticity
Suppose a brand has 10,000 units of a seasonal product remaining. Instead of automatically reducing the price by 40%, an AI system could analyze sales velocity, customer behavior, historical promotions, inventory age, and competitive pricing to estimate whether a 10%, 20%, or 30% reduction would be sufficient.
That creates a more controlled approach to markdowns.
McKinsey reports that poorly executed markdowns can result in unnecessarily deep discounts, while advanced markdown strategies can improve margin rates by 400 to 800 basis points in some retail situations.
From a financial perspective, Jia Lee, Accountant, TrueLedgerAccounting said the goal shouldn’t be “clear inventory at any cost.” The goal is to maximize the contribution from remaining inventory while minimizing the amount of capital trapped in aging stock. AI can help finance and merchandising teams evaluate markdown decisions based on margin, inventory age, demand probability, and cash flow rather than sales volume alone.
AI Can Identify Problems Earlier
Timing matters enormously in inventory management.
A product that is slightly behind its sales target today may become a major markdown problem six weeks later. AI can monitor product performance continuously and identify those early warning signals.
For example, it could flag:
- Products selling below forecast
- Sizes accumulating unusually quickly
- Regions with weak demand
- Products with declining search interest
- Inventory approaching a seasonal deadline
- Styles requiring increasingly aggressive promotions
This gives merchandising teams time to respond.
Instead of waiting until the end of a season and discovering that thousands of units remain, managers can intervene while there are still several options available.
Connecting AI With Fashion Merchandising
AI shouldn’t replace the people who understand fashion.
A model can identify that a particular style is underperforming, but a merchandiser may know that the product is about to receive significant social-media exposure or that a celebrity is expected to wear a similar style.
The best approach combines AI recommendations with human judgment.
According to Chris Wu, Founder of GiftisLove, Fashion is unusually difficult to forecast because products can have short lifecycles and demand can change quickly. AI should therefore support merchants rather than operate as a rigid automatic decision-maker. The strongest systems give planners clear recommendations while allowing them to apply context and creative judgment.
This human-AI combination is especially important because McKinsey’s recent research shows that many fashion companies are still struggling to move AI projects beyond the pilot stage.
AI Can Improve Profitability Without Simply Cutting Prices
One of the biggest misconceptions about AI-powered inventory management is that its purpose is simply to automate markdowns.
The bigger opportunity is preventing unnecessary markdowns in the first place.
If a brand can forecast demand more accurately, purchase closer to actual demand, allocate stock more effectively, and identify weak products earlier, fewer items should reach the point where deep discounts are required.
AI can also help brands determine when not to discount. A product that is selling close to forecast may not need a promotion at all.
McKinsey’s research on apparel pricing similarly highlights the importance of combining demand forecasting, price elasticity, inventory visibility, and real-time sell-through information when making markdown decisions.
What Fashion Brands Should Measure
Implementing AI without measuring the financial outcome defeats the purpose. Brands should track metrics such as:
Inventory Metrics
- Inventory turnover
- Days of inventory on hand
- Sell-through rate
- Aged inventory
- Stock-out rate
Financial Metrics
- Gross margin
- Markdown percentage
- Margin after markdowns
- Working capital tied up in inventory
- Inventory carrying costs
AI Performance Metrics
- Forecast accuracy
- Forecast bias
- Markdown recommendation accuracy
- Time saved in planning
- Improvement in full-price sell-through
The objective isn’t to have the most sophisticated AI system. It’s to create measurable improvements in inventory economics.
The Future of AI-Powered Inventory Management
AI is moving fashion inventory management from reactive decision-making toward continuous optimization.
Instead of asking, “How do we get rid of this excess stock?” brands can increasingly ask, “What can we change today so we don’t create this excess stock?”
That shift is significant.
McKinsey’s 2026 fashion outlook identifies AI, demand planning, inventory allocation, and pricing as major opportunities for improving efficiency, particularly as brands face higher costs and increasingly value-conscious consumers.
Ultimately, AI won’t eliminate inventory risk. Fashion will always involve uncertainty. But brands that combine AI-driven forecasting and pricing with experienced merchandising and financial oversight can make better decisions earlier—and protect more of their margin as a result.
The most valuable role for AI may therefore be surprisingly simple: help fashion brands make fewer expensive guesses.
