How AI is shaping the future of EDI

Blog: OpenText

Electronic Data Interchange (EDI) remains crucial to supply chain collaboration today. As with all other IT solutions, artificial intelligence (AI) developments shape the EDI world. Yet, while the technologies used for EDI connectivity continue to evolve, misconceptions and a general lack of understanding around EDI and B2B integration persist.

EDI technology continues to evolve, but companies need to deal with a complex mix

Over the several decades since companies implemented the first EDI connections in the 1960s, pundits have pronounced EDI dead several times. Yet, the EDI message standards, communication protocols, and data formats have come a long way. For example, API-based connectivity is becoming increasingly common when building new EDI connections due to the adoption of cloud-based ERPs and other business systems that offer public APIs.

However, partner requirements, existing investments in EDI connections, lack of resources, and other reasons perpetuate the use of older standards, communication protocols, and data formats—as well as Value-Added Networks (VANs)—which means organizations must have the flexibility to deal with a mix of different connectivity types in the foreseeable future.

Supply chains require more and different types of information exchange between partners

As organizations look to increase the maturity of their supply chain operations, the need for sharing more and different types of information with business partners increases. This is likely to increase the use of EDI connections for new message types and the sharing of information that does not follow existing EDI standards, such as location data, status updates, and other real-time or near real-time data. B2B integration solutions must support these kinds of data flows and core EDI messaging to avoid fragmentation of supply chain data flows across multiple platforms.

Considering the increased need for collaboration and information sharing between supply chain partners, partner onboarding becomes increasingly crucial for EDI connections and other collaboration tools, such as supplier portals and collaborative supply chain applications. Companies can support this by tooling, which requires well-defined processes, coordination across different collaboration tools, and skilled resources to ensure successful partner activation, onboarding and management.

With AI, everything must change – Three impacts of AI in the future of EDI

Like with other technical developments from XML to APIs, the rapid evolution of AI tools and technologies impacts EDI operations in several ways. The three key areas to highlight include:

Speeding up EDI data mapping

Data mapping has traditionally been the most time-consuming and expensive aspect of setting up EDI connections. AI can speed up the mapping process and improve the business case for EDI connectivity. The key challenges in using AI for EDI data mapping relate to the underlying complexity of EDI in general and to the semantic data models used by different organizations. For example, the same term can be interpreted differently by other organizations. While AI is unlikely to automate EDI mapping in the near term fully, it can still provide significant cost savings in different parts of the mapping process, from requirements gathering to data field mapping and testing.

User enablement and productivity

Visibility into EDI data flows is essential to understanding business process health, identifying errors and exceptions, and fixing them. In addition to process visibility, B2B integration solutions often provide other types of user enablement tools, from self-service connectivity setup and EDI map library access to partner onboarding process tracking and community management. These tools are often powerful, but training users to use them effectively presents challenges. Generative AI, in particular, has the power to streamline user experience through interactive—and even proactive—guidance on how the user can best accomplish tasks that support their job role. In addition to increasing users’ productivity, this will likely lower the support costs for B2B integration solutions by reducing the number of support tickets users need to raise.

Embedding AI in analytics tools

EDI data flows are traditionally used to move data between two business systems. However, the EDI data flows contain a wealth of information that users can leverage for in-flight data analytics and, for example, combined with data from IoT solutions across different use cases. Identifying anomalies and exceptions, aggregating visibility in multi-system IT landscapes and analyzing partner performance are some areas where analytics tools help gain additional value from EDI data. Embedding AI capabilities, such as learning algorithms, in these analytics tools greatly enhances this opportunity. This can be crucial in meeting emerging requirements, such as offering business context insights for supplier risk analysis or automating reporting of Scope 3 carbon emissions information.

Not only are the rumors of the death of EDI greatly exaggerated, but the future of EDI plays a vital role in building digital and automated supply chains. Yet, organizations should not underestimate its complexity. They should periodically assess the need to modernize existing connections to ensure that the organization’s B2B integration capabilities meet its evolving business needs.

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