Imagine browsing an online store where half the products don’t have proper descriptions. Sizes are unclear, colors are inconsistent, and search results feel irrelevant. For customers, this is frustrating. For retailers, it’s costly. As per Baymard Institute’s Cart Abandonment Rate Statistics 2025, nearly 70 percent of shoppers abandon their carts due to incomplete or unclear product information.
Product data has become the foundation of digital commerce. In a market where millions of SKUs compete for attention, brands and marketplaces cannot afford to rely on poorly structured or outdated catalogs. This is where AI-powered product attribute enrichment is making a decisive impact.
By using advanced AI models, businesses can automatically generate detailed, accurate, and trend-aware product attributes that enhance search relevance, drive personalization, and improve customer experience. For organizations, this shift means a chance to scale smarter and unlock new revenue opportunities with minimal resource overhead.
Product attribute enrichment is the process of improving product data so that catalogs become more searchable, accurate, and engaging. Attributes include everything from color, size, and material to context-specific tags such as “party wear,” “beach-friendly,” or “winter essentials.”
Traditionally, attribute tagging was a manual, error-prone, and resource-heavy process. Teams spent hours reviewing spreadsheets, images, and supplier feeds, often leading to inconsistencies across channels. This slowed down product onboarding and limited personalization opportunities.
AI changed this equation significantly. By analyzing product images, descriptions, and customer behavior, AI systems can automatically generate missing attributes, standardize formats, and even suggest trend-based labels that align with customer intent.
Gen AI brings two major capabilities to the table: automation and context awareness.
With tools like Google Cloud multimodal AI and Vertex AI Search, enrichment becomes a continuous process. As trends shift and new micro-occasions arise, AI can dynamically update product data to match evolving customer expectations.
AI-driven enrichment goes far beyond filling gaps in spreadsheets. It creates a stronger foundation for digital commerce.
At BBI, we’ve developed AI-driven workflows designed to tackle real-world retail challenges. Our solutions focus on:
This integrated approach combines cutting-edge AI models with retail expertise, enabling brands to accelerate growth without compromising accuracy.
Here are some use cases of how retailers and marketplaces are already applying AI-powered product attribute enrichment:
BBI’s product enrichment approach stands apart. Apart from automating data, we also help businesses build smarter catalogs that drive measurable outcomes.
Our clients have seen measurable improvements in catalog efficiency, customer experience, and operational cost savings. Get in touch with us to schedule a demo.
AI product attribute enrichment is becoming a cornerstone of modern retail strategy. From improving search accuracy to enabling personalized recommendations, AI delivers tangible results that impact both the top line and bottom line.
Retailers and marketplaces that invest now will gain a competitive edge by creating smarter, trend-aware catalogs that resonate with customers.
Connect with BBI to explore how we can help your organization take the next step in digital commerce innovation. Join us for our upcoming webinar on AI Product Attribute Enrichment to see how our solutions can transform your catalog management and accelerate growth.