AI is everywhere these days. From online shopping to booking a taxi, artificial intelligence is quietly transforming all sorts of everyday processes. But what about in the world of B2B communication – specifically electronic data interchange (EDI)? Can AI and EDI work together to improve your business operations?
In this article, we’ll explore how AI is starting to reshape EDI processes, where it shines, where it struggles, and what you should be aware of if you’re considering making AI part of your EDI strategy.
The rise of AI across business operations
Over the past decade, the adoption of AI across business sectors has skyrocketed. From a distant concept, AI has quickly become mainstream, with a recent Accenture report finding that 84% of executives “believe they won’t achieve their growth objectives unless they scale AI”. Amazingly, the same report also states that 75% of executives “believe they risk going out of business in five years if they don’t scale AI”.
More than many other sectors, supply chain businesses have been profoundly impacted by AI, largely due to their reliance on fast, high-volume data exchange and complex transactional processes. Among other things, these businesses are now leveraging AI to:
- Predict inventory needs
- Automate procurement workflows
- Optimise delivery routes
- Detect anomalies in real-time supply chain data
EDI and AI: potential utilisations
Error detection and correction
Possibly the simplest and most powerful way that AI and EDI can be combined is by using AI to spot errors. Specifically, as AI is extremely good at being able to spot anomalies in data patterns, it can be used to flag possible issues that humans might miss.
In traditional EDI, individuals only notice something is wrong when a shipment is late or an invoice goes missing. With AI, patterns are continuously monitored. If something’s off even a little bit, AI can raise the alarm early.
For example, if you usually receive 500 purchase orders every Wednesday and one week you only get 250, this change itself is not an error. However, such a pattern change can indicate something has gone wrong, and flagging this early enables relevant teams to investigate and rectify any possible issue before it develops.
Data mapping
Traditionally, setting up document mappings requires hours of manual effort and a great deal of technical expertise. Hypothetically AI can help automate parts of this process by “learning” from existing mappings and making appropriate suggestions.
Predictive analytics
AI doesn’t just see what has happened; crucially it can also predict what might happen next. By analysing past EDI data, AI can anticipate supply chain disruptions, helping you to plan ahead and avoid potential issues before they happen.
Document conversion
Imagine feeding a paper invoice into a system and having it instantly converted into an EDI-compliant format. AI-driven OCR (Optical Character Recognition) and data structuring tools are making this a reality, eliminating the need for error-prone manual data entry.
EDI and AI: key challenges
Although the potential of AI to streamline and simplify B2B processes is huge, it’s important to stay grounded. AI isn’t a magic wand… yet. There are still several issues and potential hazards associated with AI usage, including…
Technical inaccuracies
EDI isn’t a “one-size-fits-all” world. There are countless EDI formats (like EDIFACT, ANSI X12, and XML), as well as standards that vary by industry and region. Teaching an AI to handle all these perfectly is tricky and the results typically require significant fine-tuning.
In EDI even the smallest error can have a big impact. Consequently, if you ask an AI tool to convert a document into a certain EDI format, it may do it 99% correctly… but that 1% is a problem.
Diverse protocols of trading partners (when onboarding)
Different partners prefer different EDI protocols (AS2, OFTP2, SFTP, and so on). AI can help recommend configurations, but ensuring protocol compatibility still needs human oversight to ensure success.
Legacy systems compatibility
Many companies still rely on legacy ERP systems that weren’t built with AI in mind. Integrating modern AI solutions with these older environments can feel like trying to fit a square peg into a round hole.
Data privacy
Handling sensitive business data demands strict privacy controls. Using AI can introduce compliance risks if not managed carefully, particularly where cloud solutions are involved. It’s important to ensure that any AI system you use adheres to GDPR and other relevant standards.
Lack of context
AI can be impressive, but it doesn’t “understand” your business context the way a human does. It might suggest a mapping that looks technically correct but doesn’t make sense operationally. Without context, errors can easily slip through the cracks.
Conclusion
AI and EDI are a powerful combination, offering real potential to streamline processes, reduce manual work, and spot issues before they become problems. However, like any new tool, AI needs to be applied carefully, with an understanding of both its strengths and its current limitations.
See how ecosio can help you streamline your EDI processes
At ecosio, we’re passionate about making B2B integration effortless and that includes harnessing the best of AI where it genuinely adds value. If you’d like to learn more about how we can help you future-proof your B2B integration processes, get in touch today!
