Artificial intelligence and GS1 standards

Artificial Intelligence and data reliability go hand in hand. AI can bring businesses unprecedented efficiency, personalisation, and speed, but its performance depends entirely on the data it uses. AI does not “understand” products, companies, or locations—it simply processes data. For example, if the same product appears in different systems under different names or identifiers, AI cannot reliably link the information.

In such cases, analyses may be distorted, search results may point to the wrong targets, and automated decisions may be based on an incomplete picture. Furthermore, incorrect or ambiguous data can be amplified in the responses generated by AI. Without a common structure, data exchange between systems also slows down, integrations become more complex, and international scalability suffers. Ultimately, this is not just a technical challenge, but a business risk: the more inconsistent the data, the less reliable the value generated by AI.

GS1 standards improve the reliability and effectiveness of artificial intelligence

GS1 standards provide global identification keys that enable products, packages, locations, processes, and more to be uniquely identified and recognised across all systems. The more structurally standardised, machine-readable, and reliable the data is, the better, safer, and more accurate the results AI can produce.

GS1 standards provide AI with:

• A structurally standardised and unambiguous way to identify objects
  When products, locations, actors, and other objects are identified using GS1 identification keys, the data is consistent and comparable. This creates a reliable foundation for AI analyses and conclusions.
• Machine-readable codes and standardised data structures
  GS1 standards define how information is presented and transmitted. This allows different systems to process and exchange data automatically without manual interpretation.
• Global interoperability
  GS1 standards are used worldwide. This enables data to be shared and utilised across different countries, industries, and systems without discrepancies in meaning or interpretation issues.

Retrieval-Augmented Generation (RAG)

When AI retrieves answers from separate data sources, unambiguous identification keys ensure that the correct product and its associated information are found. This improves the accuracy of the answers.

Model Training and Fine-Tuning

AI learns from data. Standardised and consistent data structures improve the quality of model training and reduce misinterpretations.

Semantic search and comparison

Once products and other items have been consistently identified, AI can find and compare genuinely comparable alternatives, such as similar products, packaging, or suppliers.

Data integration and data networks

Using GS1 identification keys, AI can combine data from different sources: products, manufacturers, certifications, supply chain events, and historical data are reliably linked together.

Verifiable claims and data provenance

When data has a clear identifier and a defined structure, its provenance can be traced. This increases transparency and trust—AI not only generates answers but can also justify them with reliable source data.