Introduced in the 1960s, EDI has undergone numerous developments, which have in turn concerned messaging standards, communication protocols, but also data formats. However, partner requirements, existing investments or even lack of resources perpetuate the use of less recent standards and protocols, as well as value-added networks (VAN). Businesses therefore need to be flexible to manage a mix of different types of connectivity in the near future.
More numerous and varied exchanges of information in the Supply Chain
As companies continue to increase the level of maturity of the supply chain, sharing new types of information is becoming increasingly necessary. This results in an increased reliance on EDI for new types of messages and the sharing of information that does not meet existing standards, such as location data, status updates and other real-time data. or in near real time. To avoid fragmentation of supply chain data flows across multiple platforms, B2B integration solutions must support these types of data flows as well as core EDI messaging.
Additionally, with the growing need to collaborate and share information, partner integration is crucial for EDI connections and collaboration tools, such as supplier portals and supply chain collaboration applications. Companies have suitable means for this, which require well-defined processes, coordination between the different collaboration tools and qualified resources to guarantee the success of activation, integration and management of partners.
The three impacts of AI on the future of EDI
Accelerate EDI data mapping: Data mapping is generally time-consuming and expensive. This can easily be optimized using AI. At this level, the major AI challenge lies in the underlying complexity of EDI and in the semantic data models used by businesses. Indeed, the same term can have a different interpretation from one company to another. Although AI is unlikely to enable complete automation of EDI mapping in the short term, it can still generate significant savings at various stages of the mapping process, from requirements gathering and to testing data on the ground.
User enablement and productivity: Transparency of EDI data flows is essential to understand processes, identify errors and anomalies, and correct them. In addition to this visibility, B2B integration solutions also provide user support tools, from setting up self-service connectivity and access to the EDI map library, to monitoring the integration process partners and community management. While these tools are often powerful, training users to use them optimally represents a real challenge. Generative AI then simplifies the user experience through interactive, even proactive, advice on how each person can best accomplish the tasks assigned to them in their work. In addition to increasing productivity, generative AI helps reduce support costs by reducing the number of tickets sent by users.
Integrate AI into analytics tools: EDI data feeds are typically used to transfer data from one business system to another. They contain a multitude of information that users use for real-time data analysis. Identifying anomalies and exceptions, aggregating visibility across multi-system IT landscapes, and analyzing partner performance are areas where analytics tools can unlock additional value from data EDI. The integration of AI, in the form of learning algorithms for example, into these analysis tools considerably improves this possibility. This becomes essential to meet emerging requirements, such as providing business context insights for supplier risk analysis or automating carbon emissions reporting.
Far from being obsolete, EDI plays, on the contrary, a decisive role in the construction of digital and automated Supply Chains. However, its complexity requires businesses to periodically evaluate the need to modernize existing connections to ensure the organization’s B2B integration capabilities meet its ever-changing business needs.
