Why Distributors Need Better Product Data with Caroline Ernst

Watch on YouTube

About This Episode

Caroline Ernst, leading ecommerce solutions at AD (the largest North American buying group representing 1,400 independent distributors), reframes product content as foundational business infrastructure, not merely ecommerce enablement. With 7.6 million SKUs in the AD Digital Catalog and 13 million updates in 2024 alone, AD solves the many-to-one aggregation problem that besets independent distributors: collecting product data from 2,500+ manufacturers, normalizing heterogeneous formats (the same products arriving in five different data structures), eliminating duplicates, and standardizing fields so that sales teams and digital channels can discover accurate product information. Distributors working alone would spend months or decades normalizing their internal data; AD aggregates this work and leverages AI to achieve 60% automation of the entire process while humans validate critical elements.

Product data's value extends far beyond ecommerce into ERP operations, warehouse management systems, invoice generation, and increasingly into AI systems that require structured input to avoid hallucination. Caroline notes that the evolution of product content for a distributor begins with foundational ERP data (often messy, duplicated, using internal acronyms), then progresses through standardization (consistent titling, brand hierarchy, at minimum one image), enrichment (shipping weights, dimensions, SKU associations like repair parts and functional equivalents), and customer-facing optimization. The distinction between internal-facing ERP descriptions and external-facing content matters: good product data improves both. What distinguishes AD's approach is its position between manufacturers and distributors—AD can aggregate at source and push standardized data to the entire member community, dramatically accelerating distributor digital readiness.

Caroline emphasizes that AI is not a replacement for product content expertise; it is an accelerant. AD uses AI for mapping, categorization, enrichment, and translation (expanding multilingual content), but humans remain essential for validation, business rule definition, and domain expertise. The broader opportunity Caroline identifies is AI literacy within the distributor community: not engineering-level AI adoption, but operational understanding of how to use tools like ChatGPT to improve marketing, summarize data, edit content, and conduct competitive analysis—all multiplying workforce productivity. Manufacturers have shifted dramatically: a decade ago, less than 5% used enterprise PIMs; now over 40% of AD's supplier partners do, signaling industry recognition that controlling brand through high-quality product data is a competitive necessity.

Key Themes

Notable Quotes

More Episodes

All episodes