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AI size recommendation tools are now a standard part of fashion e-commerce. For brands dealing with high return rates and inconsistent fit, sizing technology is often one of the first solutions explored.
However, while many tools promise “AI powered sizing,” most rely on relatively similar foundations. Understanding how these tools actually work, and where their limits begin, is essential for choosing the right solution.
This article breaks down how most AI size recommendation tools operate today, what data they typically use, where they fall short, and what fashion brands should look for if they want to go beyond generic size guidance.
At their core, AI size recommendation tools aim to help shoppers select the most appropriate size for a given product. These tools usually appear on the product detail page and guide customers toward a size based on predefined logic.
Most sizing tools today are built on:
This approach allows tools to make a reasonable size suggestion for a large number of shoppers without requiring deep product or customer context.
For many brands, this is enough to reduce obvious size related friction at checkout.
The reason many AI size recommendation tools rely on generalized size charts and category level data is scalability.
Generic size logic is:
By standardizing sizing logic at the category or brand level, tools can offer quick wins without requiring changes to how products are developed, graded, or bought.
This is why most sizing tools feel similar in practice, even when branded differently.
While generic size logic works at a high level, it struggles to capture how products actually behave once worn and returned at scale.
Common limitations include:
In these cases, the size recommender adjusts the suggestion, but the underlying fit problem remains untouched.
This is why many brands see an initial lift in conversion but no meaningful long-term reduction in returns.
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Size recommendation tools operate at the end of the decision chain.
They respond to the product and sizing decisions that were already made months earlier.
By the time a size recommendation appears on the PDP:
The tool can guide the shopper, but it cannot influence the upstream decisions that caused the fit issue.
This is where sizing stops being just a UX challenge and starts becoming a product and decision challenge.
As sizing strategies mature, brands often realize they need more than category level size logic and basic customer inputs.
Key capabilities to look for include:
Brands should be able to see how fit performs at the product-specific level, not just at the category level.
Understanding which recommendations lead to fewer returns, better reviews, or repeat purchases is critical.
Fit insights should support product development, merchandising, and operations, not only PDP optimization.
The solution should help distinguish between customer preference issues and true grading or measurement problems.
Better sizing decisions upstream reduce overproduction, markdowns, and operational waste.
Generic AI size recommendation tools typically offer:
More advanced sizing and fit platforms enable:
Both approaches serve a purpose, but they address very different stages of the sizing problem.
Solutions like SAIZ are designed to go beyond generic size logic by grounding size recommendations in product-specific fit behavior, rather than relying primarily on category level size charts.
SAIZ size advice is built on two core elements that work together.
Product-specific data
Each garment is analyzed using its detailed measurements, including graded measurement tables that describe how the product changes from size to size. This allows SAIZ to translate raw measurements into an understanding of how a specific product is designed to fit, where it allows more or less room, and how it is intended to feel when worn.
Instead of assuming how a size label should fit, the system reflects how that exact product is actually constructed.
Fit-related data points
To determine how a product is likely to fit an individual shopper, SAIZ uses a focused set of relevant fit-related inputs provided by the shopper. These inputs help form an individual fit profile that can be matched against the product’s fit behavior.
By matching detailed product data with fit-related data points, SAIZ delivers size recommendations that are precise, product specific, and consistent across different styles and cuts.
Since SAIZ recommendations are rooted in product level data rather than generic size logic, the insights generated do not stop at the product page.
Over time, this approach creates visibility into how sizing decisions perform across products, sizes, and collections. Brands can see where fit works as intended, where it consistently breaks down, and which products or sizes drive avoidable friction.
This is where size advice connects to fit intelligence.
Instead of only answering which size to recommend, SAIZ helps teams understand:
Learn more about our sizing and fit solutions, and how it connects to fit intelligence.
Many brands are unsure whether their sizing challenges are caused by poor recommendations or deeper structural issues.
To help teams quickly assess this, we created the Fit Breaks Checklist, a short, practical resource designed to highlight where fit issues commonly emerge across a fashion organization.
Download the Fit Breaks Checklist
AI size recommendation tools play an important role in improving the shopping experience, but most rely on generalized size logic and limited customer inputs.
For brands looking to reduce returns, improve sustainability, and make better product decisions, sizing needs to be understood as more than a product page feature.
The right solution helps brands move from generic size guidance toward a clearer understanding of how fit performs across products, customers, and brand decisions.
