Artificial Intelligence (AI) is everywhere now and while AI powered automation and process orchestration can play a role in everything from carrying out its own tasks, to orchestrating human tasks, to driving continuous improvement, to use AI effectively, it must be implemented purposefully.

Electronic data interchange(EDI) systems allow companies to electronically exchange documents like orders and invoices. However, EDI data can be error-prone and messy due to various issues at different stages. Integrating artificial intelligence (AI) can assist overcome these challenges and enhance the efficiency of EDI structures.  AI tools can be used to validate EDI documents before they are transmitted. They can check for simple errors like missing fields, incorrect formatting, and data type mismatches. This reduces the chances of data transmission failures and rejections.  AI structures can be trained to recognize mistakes and inconsistencies in EDI data from past transactions. They can detect errors like invalid product codes, incorrect addresses, quantity mismatches, and pricing discrepancies. This helps catch errors earlier before they impact order fulfillment.

Integrating AI into EDI solutions mapping processes can also simplify mapping complex EDI documents. AI algorithms can be trained to recommend the most accurate field mappings between a company’s data and standard EDI formats.  Companies need to support both a core set of EDI transaction types and API capabilities or risk missing out on important opportunities to drive revenue, growth, and competitive differentiation.

Integrating AI can enhance EDI systems in many ways. They can validate data, detect errors, interpret unstructured notes, analyze patterns, flag anomalies, and simplify EDI mapping. This helps improve data quality, reduce rejections, speed up order fulfillment, and strengthen trading partner relationships through more accurate and reliableEDI information exchange.

Ways to apply AI to an EDI ecosystem include:

·         Data Acquisition – Web Scraping, Sensory networks, surveys

·         Data Storage and Management – cloud platforms, data lakes

·         Data Preparation – Cleaning, Transforming

·         Data Analytics - 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.  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.

The future of EDI plays a vital role in building vital data highways and organizations should not underestimate its complexity. They should periodically assess the need to modernize existing connections to ensure that their B2B integration capabilities meet its evolving business needs.

To learn more about how you can leverage AI in your environment, please visit our website at Effective-Data.com or reach to our account team for assistance.