What's the difference between AI and machine learning in EDI?

AI is the broader field covering systems that can learn or act with limited human input, while machine learning is a subset of AI that uses data and algorithms to spot patterns and make decisions with minimal human involvement. In EDI, Narrow AI, meaning AI focused on a specific task rather than general intelligence, is the type typically applied to order and invoice processing.

How does AI reduce errors in order processing?

It reduces errors by learning from how a mistake was corrected once and then automatically applying that same fix if the same error happens again, for example when a typo involving numbers or characters is entered at the point of order entry. Transalis used this approach for appliance manufacturer SMEG UK, and the same self-correcting logic is also applied to onboarding new trading connections.

What results did SMEG UK see from combining EDI and AI?

SMEG UK's Head of Business Systems reported that inbound order error rates fell from around 15% down to below 0.5% after the solution went live, with the API built, tested and put into production in around ten days. They also gained the ability to integrate orders from customers not using structured EDI, with text extracted from those documents guaranteed 100% correct.

Can AI handle unstructured or non-EDI trading documents?

Yes. AI increases automation even with unstructured data by memorising how a user has previously handled particular data fields and then applying that same handling automatically next time, effectively capturing structured data from otherwise unstructured documents such as PDFs or scanned orders. This means suppliers who are not yet trading in a structured EDI format can still be integrated.

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EDI and AI: how to enhance your EDI solution

EDI and AI can work together to bring greater automation into the sharing of essential documentation among partners in a supply chain

Transalis6 August 2021Updated 23 September 2026

How does AI enhance an EDI solution?

AI, particularly Narrow AI, works alongside EDI to automate and improve the accuracy of order and invoice processing between trading partners. It acts as a functional layer that recognises and corrects typos or other data ambiguities, such as a zero misread as the letter O, standardising information as it flows through the EDI system rather than relying on manual re-keying and checking.

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.

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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.

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.

Find out how SMEG (UK) uses AI for EDI order processing

Frequently asked questions

What's the difference between AI and machine learning in EDI?: AI is the broader field covering systems that can learn or act with limited human input, while machine learning is a subset of AI that uses data and algorithms to spot patterns and make decisions with minimal human involvement. In EDI, Narrow AI, meaning AI focused on a specific task rather than general intelligence, is the type typically applied to order and invoice processing.
How does AI reduce errors in order processing?: It reduces errors by learning from how a mistake was corrected once and then automatically applying that same fix if the same error happens again, for example when a typo involving numbers or characters is entered at the point of order entry. Transalis used this approach for appliance manufacturer SMEG UK, and the same self-correcting logic is also applied to onboarding new trading connections.
What results did SMEG UK see from combining EDI and AI?: SMEG UK's Head of Business Systems reported that inbound order error rates fell from around 15% down to below 0.5% after the solution went live, with the API built, tested and put into production in around ten days. They also gained the ability to integrate orders from customers not using structured EDI, with text extracted from those documents guaranteed 100% correct.
Can AI handle unstructured or non-EDI trading documents?: Yes. AI increases automation even with unstructured data by memorising how a user has previously handled particular data fields and then applying that same handling automatically next time, effectively capturing structured data from otherwise unstructured documents such as PDFs or scanned orders. This means suppliers who are not yet trading in a structured EDI format can still be integrated.

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