How to Use AI for Assortment Planning: A Complete Retailer's Guide

AI assortment planning is flipping the script on how retailers manage inventory across online stores and physical locations. Most retail teams are still playing guessing games with product selection while their competitors use data to nail exactly what customers want, whether they're shopping online or in-store.

Here's what happens when retailers wing it: online shoppers find "out of stock" messages on trending items while physical stores have empty shelves where popular products should be. Meanwhile, warehouses and stockrooms overflow with products that nobody is buying.

That's the cost of ignoring smart inventory management across all channels.

Let's explore more in the blog to understand how AI fixes this mess. And it's not rocket science.

Assortment planning is picking which products to sell when to sell them, how much stock to keep, and where to place them across online and offline channels. Traditional planning goes like this: look at last year's numbers, make informed assumptions for each channel, and hope things work out. AI reverses this approach completely.

Then it predicts what customers will buy. Not what retailers think they'll buy.

According to Business Insider (2025), Target now uses AI across over 40% of its assortment, generating billions of weekly demand predictions to improve stock availability.

Good AI assortment planning starts with solid data from all channels. No shortcuts here.

Most retailers have information scattered everywhere. E-commerce platforms track online behavior. Point-of-sale systems capture in-store transactions. Inventory management tracks stock across warehouses and stores. Customer service platforms log cross-channel interactions.

Step one: bring everything together for a unified view.

Quality beats quantity every time. Clean data with gaps works better than messy data with everything.

A 2024 Infosys report estimates the global Assortment and Space Optimization market size at $2.1 billion, projected to reach $5.1 billion by 2033 (10.4% CAGR).

The retail planning software marketplace offers solutions for various budgets and business scales, but incorrect selection can undermine your entire project.

• Enterprise-Scale Platforms: Large retailers typically require comprehensive systems managing thousands of products across hundreds of locations. They deliver powerful functionality but demand substantial implementation resources and technical expertise.
• Mid-Market Retail Solutions : Medium-sized retailers often benefit from specialized planning platforms that balance functionality with implementation complexity. Seek solutions offering:

• Small Business Alternatives : Smaller retailers can begin with accessible solutions providing:

Demand forecasting in retail is where AI shows its muscle across all channels.

Traditional forecasting takes last year's sales and adds a growth percentage. AI examines hundreds of variables simultaneously across online and offline touchpoints.

Machine learning models spot cross-channel patterns humans miss. Like how online searches for winter coats predict in-store sales two weeks later. Or how social media buzz drives both e-commerce traffic and foot traffic to physical stores.

Omnichannel analysis reveals how customer behavior differs between channels. Some products perform better online due to detailed product descriptions. Others need an in-store trial before purchase.

Time series analysis finds seasonal patterns across channels. Ensemble methods combine multiple approaches for better accuracy, whether forecasting online demand or store-level needs.

Want to see how AI can improvise product selection? Book a free strategy session to explore Couture.ai’s solutions that fit specific business needs and market trends.

Different products need different approaches across online and offline channels. Smart retailers optimize product assortment by treating categories and channels separately.

Core products stay in stock always, both online and in physical stores. These are the basics customers expect to find regardless of how they shop. Running out kills trust.

Online-first products might include technical specifications, detailed size charts, or items that benefit from customer reviews. These work better in digital environments where shoppers have time to research.

In-store experience products need physical interaction before purchase. Think clothing that requires fitting, furniture that needs touching, or electronics that benefit from a demonstration.

Seasonal items need perfect timing across channels. Online allows earlier launches and longer tails. Physical stores need precise timing for peak selling windows.

Trend products require quick reactions across all channels. Social media can create instant demand that needs fulfillment both online and in-store.

New products present unique challenges. Online provides detailed performance data quickly. Physical stores offer immediate customer feedback and testing opportunities.

Create channel-specific rules for each category. Let AI address the specific product choices and allocation decisions within those guidelines.

The assortment strategy needs constant tweaking based on results and market changes across all channels.

Review fast-moving categories weekly across all channels. Check standard merchandise monthly for each channel. Do strategic cross-channel reviews quarterly.

AI models get smarter with more data. However, they need human oversight to catch unusual situations and adjust to the business context.

Retailers using AI-powered smart inventory management across online and offline channels see consistent improvements:

Inventory levels drop 15-25% as systems optimize stock allocation between e-commerce fulfillment centers and physical stores based on actual demand patterns.

Cross-channel margins improve by 5-10% from optimized pricing strategies and reduced markdowns across all touchpoints.

Operational efficiency increases as staff focus on customers instead of managing inventory spreadsheets for multiple channels.

Clients are seeing 1-5% profit increases and 5-10% sales growth across their omnichannel operations. Mid-market retailers using specialized platforms achieve similar percentage improvements whether they're primarily online, offline, or truly omnichannel.

AI-driven assortment planning enables smarter business decisions with enhanced information across all customer touchpoints.

The retailers winning today use smart inventory management to stock what customers want. They use demand forecasting in retail to spot trends before competitors catch on and optimize inventory allocation across all channels.

Build your assortment planning with Couture.ai. Schedule a personalized free demo session with our AI experts to discover how AI can optimize product selection, slash inventory costs, and drive profitable growth.