Will AI Take over EDI ?

Will AI Take over EDI ?

EDI isn't going anywhere, and it is enriched when coupled with AI.

An example of where Narrow AI can support EDI is in increasing the efficient and accurate flow of trade documents, such as orders and invoices. AI and EDI combined bring greater automation to the sharing of documentation between trading partners.

Integrating AI into business processes and systems can provide numerous benefits, including increased efficiency, better decision-making, enhanced customer experiences, and competitive advantage. Remember that AI integration is an ongoing process. It requires collaboration between business leaders, data scientists, engineers, and other stakeholders to ensure successful implementation and ongoing value creation.

Let’s look at a concrete example as the future of EDI and B2B meets AI. EDI refers to the electronic exchange of business documents between computer systems, such as invoices and purchase orders, offering a sustainable and future proof way for businesses to automate their communication and document exchange processes.

In first place, EDI and AI are unrelated. However, AI integration can help to automate EDI-based corporate processes. An EDI system, for example, may be used to automatically exchange invoices and purchase orders with a supplier, while an AI system can evaluate the data from those documents and provide recommendations for enhancing supply chain management.

Imagine this: A business receives a significant number of invoices from its vendors on a daily basis. In most cases, these invoices are delivered in the form of scanned documents or PDF files, and they have to be keyed in by hand into the accounting software used by the organization. This is a method that is labor-intensive and prone to errors.

The business could integrate an AI application with their EDI system to automate the data entry and speed up and simplify e-invoicing procedures. To begin, they have the option of utilizing optical character recognition, also known as OCR, in order to immediately extract the text from the bills. After that, they can make use of natural language processing (NLP) in order to recognize and extract important information from the invoices, such as the name of the supplier, the amount of the invoice, and the date of the invoice.

After the data has been collected and organized, it can be automatically entered into the company’s financial system through AI integration, removing the requirement for human intervention in the process of entering data. The company’s financial procedures can become more accurate and efficient as a result of this, in addition to saving time and reducing the risk of errors.

In addition, the business use an integration platform to enable AI to find patterns and trends in EDI data by using machine learning algorithms to conduct an analysis of the data. For instance, they can make use of clustering methods in order to group together invoices that are comparable to one another, or they can make use of predictive modeling in order to anticipate future expenditures. This can provide an organization with insightful information that will assist them in improving their business operations through AI integration initiatives.

These examples clearly demonstrate that AI integration in business drastically improves the EDI process in the following four areas:

Identifying patterns and significant discrepancies: Integrated AI can detect significant variations in commercial papers. For example, if you make a quotation for your client that contains values that differ significantly from those used in previous quotes, an EDI tool with AI can assist you in identifying such deviations. Such minor but significant differences may be difficult to detect if documents are manually checked by humans.

Validation: Integrating AI into business ensures that every invoice data is automatically validated by comparing it to other transaction-related documents for increased precision.

Automation: AI can identify trends in data, such as those seen in invoices, purchase orders, and other documents. In the end, it makes automatic document entry and processing possible, which minimizes manual entry and risk of errors.

The process of extracting information from documents: Integrated AI data models can be trained to automatically extract specific information from business papers. This data can then be transferred to other software applications to develop patterns and reports that businesses can use to make choices. This reduces the amount of time that would have been lost if the data had been manually extracted by humans.

In addition to being able to apply artificial intelligence to typical EDI exchanges, AI integration can also be beneficial when it comes to assuring a higher level of accuracy while converting non-EDI documents into a specific format. This is possible because AI can analyze the content of the documents it converts. In the near future, it will be interesting to witness how AI and ML enable integration platforms to seamlessly interact with a variety of contemporary technologies.

One potential use case for IoT in EDI is the automation of data exchange between businesses. IoT sensors can be used to collect and transmit data automatically, which can reduce the need for manual data entry and improve the accuracy of the process. For instance, a company can use IoT sensors to monitor inventory levels in real-time and automatically trigger an EDI transaction to replenish stock when levels fall below a certain threshold.

Another technology that has the potential to impact EDI is blockchain. Blockchain can create secure and tamper-proof records of EDI transactions, providing an additional layer of security and trust between trading partners. Blockchain can also enable real-time tracking of goods in transit, improving the visibility and transparency of the supply chain.

Artificial intelligence (AI) is another technology that could enhance the capabilities of EDI. Machine learning algorithms can be used to analyze large amounts of data and identify patterns and trends, which can help businesses make more informed decisions. AI can also be used to automate repetitive tasks, such as data entry, freeing up time for employees to focus on more strategic activities.

However, it is important to note that these emerging technologies are not necessarily replacements for EDI mapping. Instead, they may be used in conjunction with EDI mapping to improve the efficiency and accuracy of the process. EDI mapping is the process of converting a company’s business documents into a standardized electronic format for exchange through EDI. Mapping is a critical step in ensuring that the electronic documents are properly formatted and can be interpreted by the recipient’s system.

Regardless of the technologies used, the responsibility for business mapping will still fall on the businesses themselves or on their EDI service providers. The businesses will need to ensure that they have the necessary mapping tools and expertise to convert their business documents into a standardized electronic format for exchange through EDI.

In conclusion, the future of EDI is likely to be shaped by emerging technologies such as IoT, blockchain, and AI. These technologies have the potential to improve the efficiency and accuracy of the process, but they are not necessarily replacements for EDI mapping. As businesses continue to adopt these technologies, it will be important for them to work with their EDI service providers to ensure that their mapping processes are up to date and can accommodate these new technologies.