Global e-commerce has been on the rise since it gained momentum in the last decade. Slowly but steadily, its share in the retail revenue is increasing.
A Mordor Intelligence research note states that the global e-commerce market is worth $36.21 trillion in 2026. At a CAGR of 16.46%, it is expected to grow to a worth of $77.58 trillion by 2031. The rise of digital wallets further accelerated its growth. They contributed to 43.92% of the payments made.
Global e-commerce has now entered a phase where product data carries as much weight as pricing or logistics. A single product listing may need to appear across dozens of marketplaces.
Managing that level of complexity through manual processes is no longer realistic. Artificial intelligence (AI) is stepping in to redefine how brands structure, enrich, distribute, and optimize their product information at scale.
The Growing Complexity of Global Product Data
As brands expand across borders, their product ecosystems grow more complicated. Each market may demand different attribute requirements, formatting standards, measurement units, or legal disclosures.
For instance, the European Union introduced a Digital Product Passport (DPP) under its Ecodesign for Sustainable Products Regulation in 2024. This requires most products sold in the EU to carry a digital record. It should detail their origin, material composition, environmental impact, compliance information, and disposal guidance.
The initiative aims to close gaps in product transparency by making standardized product information accessible across supply chains. This will enhance regulatory compliance and support sustainable practices.
Product information management systems are built to centralize and standardize data. They have become an essential foundation for e-commerce success. They help improve data accuracy and centralize it.
However, the rapid expansion of digital channels has pushed traditional systems beyond their limits. Without automation, errors accumulate quickly, affecting search rankings and customer trust.
AI is changing this equation by introducing intelligent classification, automated tagging, and real-time validation capabilities. Instead of relying on manual audits, brands can now use machine learning models.
Intelligent Data Enrichment at Scale
One of AI’s most powerful contributions to product data management is enrichment. Data enrichment has shifted from static, manually maintained records to dynamic, AI-driven systems that constantly update for more accurate decision-making.
Modern enrichment technologies leverage artificial intelligence and machine learning to create real-time, unified customer profiles. This improves lead scoring, enables predictive customer segmentation, and detects behavioral patterns that help businesses identify high-value prospects.
AI-driven natural language generation tools can analyze existing product specifications and automatically create clear, marketplace-ready descriptions. These systems also adapt tone, terminology, and measurement units based on target markets.
It is also important to market these products in physical channels to implement phygital marketing at scale. Many e-commerce companies rely on catalogs because they are easy to share in both digital and physical forms. According to J.Schmid, the right catalog can transform how products appear. This can elevate an e-commerce brand in the marketplace.
AI can generate product descriptions and even automate additional workflows to print catalogs. However, not all companies have expertise in creating catalogs, even with AI. Such companies collaborate with specialized partners like a catalog agency to ensure accuracy and marketplace compliance.
Automation and Error Reduction
Data errors in global e-commerce can be costly. Incorrect dimensions, missing compliance information, or inconsistent pricing can lead to returns, penalties, or account suspensions on major marketplaces. Human teams often struggle to keep pace with rapid product updates and policy changes.
AI-powered validation engines monitor product data streams and flag anomalies instantly. If a new listing deviates from established patterns, the system can correct the issue automatically or escalate it. This reduces the likelihood of widespread errors affecting thousands of SKUs.
Consider Shopify’s use of AI agents to build product taxonomies at scale. The system addresses challenges in keeping tens of thousands of product categories and attributes up to date.
Rather than relying solely on manual curation, Shopify has developed a multi-agent AI architecture. It detects gaps and inconsistencies in classification hierarchies and analyzes real merchant product data to improve and expand taxonomy labels.
This approach allows structural and product-driven analysis to work together, supported by automated quality checks. Thus, the taxonomy stays consistent and scalable without slowing innovation or shopper experience.
Personalization and Dynamic Content Optimization
Beyond accuracy and compliance, AI enables dynamic product content optimization. Product data is no longer static. Algorithms adjust descriptions, feature highlights, and even image sequencing based on user behavior, demographics, and regional trends.
For example, a product marketed in North America may emphasize certain technical specifications. The same product in Europe may highlight sustainability credentials or warranty details. AI systems analyze engagement data and adapt content accordingly without requiring full manual rewrites.
Traditional AI supports recommendation engines that use purchase history and predefined segments to personalize shopping experiences. Generative AI builds on this foundation by enabling real-time customer profiling, dynamic segmentation, and tailored product suggestions.
It enhances product experience management through automated content creation, hyperpersonalized offers, immersive product visuals, and intent-driven search. This helps brands deliver more engaging experiences that increase conversions and customer loyalty. Therefore, it is one of the biggest use cases of AI in e-commerce.
Search engine optimization also benefits from AI-driven insights. These tools monitor keyword performance across marketplaces and search engines, then update product metadata to improve visibility. Instead of relying on periodic audits, brands maintain continuous optimization cycles driven by performance data.
Frequently Asked Questions
What role does AI play in improving collaboration between global teams managing product data?
AI-powered platforms centralize product information and provide real-time updates. This improves coordination among marketing, compliance, merchandising, and IT teams. Intelligent workflows automatically route tasks, notify stakeholders of changes, and track version histories. This helps reduce communication gaps and prevents duplicated work.
Can small and mid-sized e-commerce brands benefit from AI in product data management?
AI is no longer limited to large enterprises with significant budgets. Many cloud-based solutions offer scalable pricing models that make advanced automation accessible to smaller retailers. These tools help growing brands manage expanding product catalogs, enter new marketplaces, and maintain consistent data quality. As a result, smaller companies can compete more effectively with larger global players while maintaining operational efficiency.
How does AI contribute to better sustainability tracking in product data?
AI systems can aggregate and analyze environmental data, including material sourcing, carbon impact, and recycling information, across supply chains. They help structure sustainability attributes in standardized formats that align with reporting requirements and marketplace guidelines. This improves transparency for both regulators and consumers.
Artificial intelligence is transforming product data management from a manual, error-prone task into an intelligent, performance-driven discipline. Global e-commerce brands now rely on AI to enrich listings, reduce compliance risks, localize content, and optimize distribution.
As e-commerce expands across regions and platforms, the ability to manage product data intelligently will define long-term success. Brands that integrate AI into their data ecosystems gain operational efficiency, stronger marketplace performance, and deeper insight into customer behavior.
