Automating Assortment Planning with AI: Benefits and Risks Every Fashion Brand Should Know

Assortment planning has always been part art, part science. But with increasing pressure to reduce waste, boost margins, and meet fast-changing consumer expectations, fashion brands are turning to AI to automate this critical function. Automated assortment planning promises speed, precision, and scalability—but it also introduces new risks if not carefully implemented.

In this article, we explore how AI is reshaping assortment planning in the fashion industry, what brands stand to gain, and what they need to watch out for.

What Is Automated Assortment Planning?

Assortment planning is the process of selecting the right mix of products—styles, sizes, colors, price points—for a specific season, region, or retail channel. AI-powered solutions automate this process by analyzing historical sales data, market trends, customer preferences, and real-time demand signals to recommend optimized assortments.

Benefits of Automating Assortment Planning with AI

1. Data-Backed Decision Making

AI systems can analyze millions of data points at once—from past sales to emerging trends—to create assortments that reflect real-time demand, not guesswork.

2. Faster Go-to-Market Speed

Automated recommendations significantly reduce the time teams spend building and revising assortments, freeing up bandwidth for higher-level strategic work.

3. Reduced Overstock and Markdowns

By more accurately predicting demand, AI helps prevent over-ordering slow sellers or understocking key items—protecting both margins and sustainability goals.

4. Localized Assortments

AI models can tailor assortments by region, store type, or customer segment, helping brands cater to localized preferences at scale.

5. Enhanced Cross-Team Collaboration

With AI-generated insights, planners, designers, and merchandisers can align faster—reducing friction in the planning cycle.

Real-World Example: AI in Assortment at H&M

H&M has invested heavily in AI and machine learning to support product assortment decisions. By combining historical data with current trend signals and in-store feedback, the company improved its ability to localize collections and reduce inventory waste across global stores. The result? A more responsive supply chain and leaner inventory practices (source).

Risks and Challenges of AI-Driven Assortment Planning

1. Over-reliance on Historical Data

AI models trained heavily on past seasons may underrepresent emerging trends or cultural shifts. Without human oversight, this can lead to stale or misaligned assortments.

2. Bias in Input Data

If your training data reflects past biases (e.g., only offering certain styles or sizes), AI could replicate and reinforce these blind spots.

3. Loss of Creative Intuition

AI can recommend what’s worked—but it can’t always foresee what could work. Relying too heavily on automation may stifle design innovation or brand differentiation.

4. Integration Complexity

Implementing AI tools requires clean, connected data systems. For brands with fragmented infrastructure, this can delay results or reduce accuracy.

5. Team Skill Gaps

Planners and merchandisers may need training to interpret and act on AI outputs. Without buy-in and clarity, adoption can stall.

Best Practices for Implementing AI in Assortment Planning

• Combine AI insights with human oversight: Use AI to surface opportunities, but rely on experienced merchandisers to apply context and creativity.
• Audit your training data regularly: Ensure your models reflect today’s reality, not outdated patterns.
• Start small and scale: Test AI-driven assortment planning on a single category or region before expanding.
• Integrate cross-functionally: Involve design, planning, and buying teams early to align on goals and outputs.
• Monitor and adapt: Treat AI as a living system—constantly fed with fresh data, reviewed, and refined.

Conclusion

AI has the potential to transform assortment planning from a manual, intuition-heavy process into a streamlined, insight-driven strategy. But automation alone isn’t the answer—it must be balanced with human creativity, cultural context, and strong implementation. With platforms like WovenInsights, fashion brands can embrace automation without losing what makes them distinct.