Supplier Onboarding With AI: What You Need To Know

New products need to go online fast with accurate data that meets the requirements of customers, platforms, and regulatations. But that’s exactly where the challenge lies when onboarding new products. AI can make the process of supplier onboarding smarter and more efficient.

Quick Access:

• The Complexity Of Product Data Exchange
• Data Is The Real Challenge
• Processes And Responsibilities
• 3 Steps For Usable Product Information
• Where Can AI Add Value?
• Optimize Your Onboarding Process With AI

The Complexity Of Product Data Exchange  

Every player in the supply chain uses their own systems, data models, and has their own interests. While data standards such as GS1 and ETIM exist, adoption levels vary greatly across markets and countries. Large platforms like Amazon and Bol.com can enforce their formats on suppliers, but for most wholesalers and retailers, that’s simply not feasible.

This problem often stems not from unwillingness, but from incapability. Many suppliers still lack a centralized source like a PIM (Product Information Management) system, where product data is up-to-date, complete, and available on demand. Even when a system exists, suppliers or customers often use a different data model. This makes data exchange within the supply chain complex and inefficient.

As a result, suppliers often provide their data in their own format, like Excel files or standalone PDFs. Wholesalers and retailers, on the other hand, expect data in standardized formats such as GS1, ETIM, or BMEcat.

On top of that, commercial interests can play a role. Some suppliers deliberately choose not to share certain information, for example, when it comes to secret recipes or unique product formulations.

Shared data language  

Data exchange is not new, but it remains difficult. It’s like a conversation between people from 20 different countries, each speaking their own language: chaos! You can agree to speak a common language, but that takes time and effort. The same applies to data: every industry needs to define (international) standards and ensure they are implemented throughout the supply chain.

Local differences also matter. Laws, regulations, and cultural norms can all require localized adjustments or translations.

Without a shared data language, wholesalers and retailers are forced to do much of the work manually. This leads to errors, frustration, high costs, and delays in time-to-market.

Data Is The Real Challenge  

What makes data exchange so persistent is that it’s not a technical problem. It’s a data problem. Or more accurately, a foundation problem. Many companies simply don’t have their own data foundations in order. Without a solid base, you can’t reliably share data with others.

A solid data-foundation is essential for creating a single version of the truth in product data. It’s important to recognize that different data structures serve different purposes:

For proper data management, a structure based on standards like GS1 or ETIM is needed, along with clear agreements about mandatory and optional attributes.

Processes And Responsibilities  

A good data structure alone is not enough. The surrounding processes must also be in order. Consider:

• Collecting
• Input/upload
• Transformation
• Management
• Enrichment
• Distribution

Efficient processes require clearly defined roles (such as via the Data RACI model), KPIs, and a quality control system with dashboards and checks.

Assign the right people to these processes. Marketers and category managers are customer-focused, but for high-quality data, you need people who are focused on structure and consistency.

PIM System As A Key Enabler  

A well-chosen PIM-system is essential. It supports the processes mentioned above, facilitates collaboration, and ensures data is always current, complete, and consistent across departments and channels. In short, it’s the core of your data foundation.

From Supplier Data To Usable Product Information: Three Essential Steps  

AI can convert data faster, more accurately, and automatically into the desired format. Just like automatic translation in language, AI can “translate” data as well. The three key steps:

1. Classification: Assign the correct product type (e.g., ETIM class or GS1 brick), so it’s clear which attributes are required.

2. Attribute Mapping: suppliers often use different terms. AI recognizes and translates these into your data model.

3. Value Normalization: differences in spelling or terminology (e.g., “azure blue” vs. “cyan”) are automatically harmonized for filters and search functionality.

AI can even enrich missing attributes by extracting information from marketing texts, datasheets, or website content.

By enhancing product data with AI, supplier data can be quickly transformed into structured product information. Automatic classification, mapping, and normalization save time, reduce errors, and instantly improve data quality.

Where Can AI Add Value For Your Business?  

When onboarding supplier data, AI can add tremendous value in several ways:

• Automatically transform data between formats (e.g., Excel to BMEcat or XML)
• Identify and complete missing data
• Detect anomalies and inconsistencies in product data
• Generate content (e.g. titles and descriptions, based on previously submitted data

It’s important to use AI purposefully. You don’t need generative AI for image creation, you need a model that understands product data and prevents errors.

Implementing AI: Start Small, Think Big  

AI is powerful, but only works well if your foundation is solid. Start by focusing on a use case where time or errors are a problem. Ask yourself:

• Where are the biggest delays?
• Where do most errors occur?
• Which manual tasks are the most time-consuming?

That’s where the biggest opportunity lies for successful AI implementation.

Optimize Your Onboarding Process With AI!  

Supplier onboarding forms the foundation of a solid product data infrastructure for wholesalers and retailers. AI offers significant potential to streamline this process by automating manual tasks and improving data quality.

Curious how AI can transform your supplier onboarding? Let’s explore how smart automation and better data quality can help you build a strong foundation for success. Contact us today to take the first step toward a smarter, faster, and future-proof data ecosystem.