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Artificial intelligence is everywhere. It’s how Netflix knows exactly what I want to watch, and how Audible just gets me with book suggestions that are on point. AI is taking in all of the historical data (and, admittedly, there’s a lot of Netflix data in my history) to make future predictions.
This got me thinking recently about how AI works with electronic data interchange. We’ve covered other advancements — such as API — that have not only not replaced EDI, but have enhanced it. (For a recap, see How BOLD VAN’s API Can Improve Workflow and API and EDI: Why You’ll Need Both.)
Like the union of APIs and EDI, AI can greatly enhance an EDI system as well. Let’s take a look.
AI is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience” (Brittanica.com).
Simply put, AI is a blanket term for any software program or device that performs a task that once required human input. Common examples of AI include:
Machine learning is a subset of AI where computers identify historic trends through learning and optimization methods. Algorithms are constantly analyzing datasets to define very specific trends that can be used to inform future business models. The computers are mimicking humans with their own version of deductive reasoning — making connections from the past data and then offering suggestions for future action.
Many organizations have a ton of rich data but don’t have the time or resources to turn that data into useful information. Imagine allowing the machines to do all the work.
Now that we have some background on the mechanics of AI and machine learning, how does this correlate to EDI? Here are a few ways you could enlist AI to enhance your EDI system.
“But wait,” you say. “I thought EDI already automates B2B processes. How can AI add to that?” You are correct, EDI is computer-to-computer exchange of business documents in a standard format. For decades EDI has replaced manual processes that take time and invite errors. However, traditional EDI solutions do require frequent monitoring.
When getting a set of documents up and running in an EDI system, the EDI translation software handles the bulk of the work taking the data from the original source and landing it in the appropriate, agreed-upon format between two trading partners. The process has been so valuable because it halts keying errors that occur when humans input the information into a software program.
One thing this process can’t do, however, is spot and correct errors that were in the original documents to begin with, such as when the letter “o” is used instead of a zero. Normally, this will need the eyes of a human to detect and fix. But AI is “smart” and can correct these types of errors.
Suppose many documents heading into the EDI translator have the same issue — this can happen when incorrect data is cut and pasted across several documents involved in an order. Once you correct it, AI takes note, plucks the correction into its memory bank in order to fix it in the rest of the documents.
AI teaches your software what to fix — typos involving numbers and characters, item numbers that are extinct and need to point to a different number, addresses that have changed. The program highlights issues the first time, and once you make a correction, AI acts like an elephant — it never forgets. It stores that information in it’s endless memory bank and auto-corrects the next time the same issue pops up.
EDI systems that don’t have AI enabled have to rely on humans to keep making that correction over and over again. And humans aren’t perfect; they might not fix all of the errors in time (such as before the order goes out), or may make a typo. It happens. We’ve all been there. Typos aren’t a problem with AI, though.
One skill AI brings to the table is a keen attention to detail. AI scans the data and is smart enough to flag a shipment set to go to New Albany, Ohio that usually goes to the exact same address in Albany, New York. This is a deductive reasoning insight a human looking at the orders will make, but EDI processes don’t necessarily have the capability to flag.
Retailers often use machine learning to identify consumer behavior. Which stores sell the most cycling accessories, and what time of year should you plan for high levels of inventory? When predictive models are put in place, these questions are answered and retailers can stock items based on seasonality and consumer trends. Which, of course, results in an improved ROI.
There are several types of EDI standards — ANSI, EDIFACT, TRADACOMS and ebXML, to name a few. Smaller shops that supply to the larger chains often have to work in multiple EDI languages.
While EDI-translator programs get this job done, an AI-enabled program can take it a step further, ensuring accuracy throughout the process. AI can figure out standard document fields such as item numbers or invoice numbers and place them in the appropriate EDI format. Keying errors that can be detrimental to an order (a quantity of 100 that was keyed in as 1000, for example) are no longer a concern.
The partners involved all benefit from a conversion that is accurate no matter what format the original data came from.
These are just a few ways AI can enhance your EDI system; it will be interesting to see how this partnership continues to unfold and deepen over the next decade. EDI isn’t going anywhere, and it is enriched when coupled with AI.