EDI (Electronic Data Interchange) has been in existence for a quite some time. However, with technological advances, such as Artificial Intelligence (AI), EDI and AI can work together for an enhanced and more automated business solution.

The blog below describes the relationship between EDI and AI and how they work together to optimise business processes.

Get online in 24 hours

As simple as 1,2,3. Buy eDI Instant online and connect with your trading partner today


What is the difference between EDI, AI and Machine Learning (ML)?

Electronic Data Interchange (EDI)

EDI is categorised by the digital exchange of standardised business documentation between trading partners. This can include, but is not limited to; order, invoices, shipping notices,

Artificial Intelligence (AI)

AI stands for Artificial Intelligence.

Typically there are two main camps of AI: General AI and Narrow AI. So what is the difference between General AI and Narrow AI?

General AI, also known as Strong AI, is highly sophisticated artificial intelligence that is closest to human intelligence. It is able to determine, learn, and complete actions without human intervention.

Narrow AI is the term used to describe artificial intelligence that is focused on handling a specific or limited task. It is an approach steadily finding its way into the automation of order and invoice processing alongside EDI.

Machine Learning (ML) is another subset of AI. ML uses data and algorithms to identify patterns, learn and make decisions with minimal human intervention.

How they work together

AI and EDI combined bring greater automation to the sharing of documentation between trading partners.

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.

The problem with manual document processing

Businesses send and receive documents in multiple formats. This continues despite the trend towards more automated and digital processes. For example: an Accounts Payable (AP) department could be processing invoices sent as scans, PDF attachments, or even by post.

These different documents need to be re-keyed manually into the chosen business application. Manual processing of large volumes of data has an associated risk of high error rates. The letter ‘O’ can be confused with ‘0’ zero, or the letter ‘S’ can be mistaken for ‘5’ five, for example.

Manual verification may be feasible where the document volume is low. However, it is very costly and time-consuming when processing larger document volumes.

How AI supports EDI with managing errors

Many EDI solutions can struggle to recognise and correct typos, or other message ambiguities.

AI can support EDI in this scenario by acting as a functional layer. This facilitates timely processing whilst standardising data.

Example of EDI and AI

Supply chain technology provider, Transalis, helps clients leverage EDI and AI.

Domestic and industrial appliance manufacturer, SMEG (UK), is among them. Their trading network generates hundreds of orders each week across different systems and protocols.

Narrow AI technology supports their EDI solution. It ‘teaches’ the system what needs to be fixed in the data. This includes; typos involving numbers and characters at point of entry.

The solution for SMEG essentially communicates how an issue was solved previously within the EDI system. This enables the error to be auto-corrected if it happens again. The same approach is also auto-applied to the onboarding of new trading connections.

SMEG (UK) combines EDI with AI

Confidence in data processing

Help is at hand if you’ve been mandated by your retail partner to implement an EDI solution:

Cerie Paton, Head of Business Systems at SMEG (UK), said the Transalis solution has significantly reduced inbound order error rates from around 15% to below 0.5%:

“Transalis built, tested and put live an API in around ten days. They worked with us to understand our challenges. This included knowing where issues were coming from, why data was failing, and how mistakes could be resolved. They have the development and technical support skills to overcome data accuracy issues.

Their solution has given us complete confidence that order processing is accurate. There are two main benefits for us. Firstly, we can easily integrate orders from different customers who are not using structured digital data or EDI. Secondly, we have information from retrieved text files that is guaranteed 100% correct.”

Benefits of combining EDI and AI

The experience of SMEG (UK) shows that EDI and AI combine to further optimise supply chain management.

The trend for combining EDI and AI is due to a couple of factors:

  • advances in computer processing capacity
  • continual refining of algorithms
  • ever greater access to data volumes

AI paves the way for automated document processing. This is achieved by creating a ‘neural network’ to capture structured data from documents.

EDI with AI can also increase automation even when presented with unstructured data. This is achieved by memorising user actions in response to different data fields.

We have seen how AI supports critical applications in autonomous vehicles to medical research. Now, the relationship between EDI and AI is set to bring greater benefits in automating document processing.