AI in supply chain: Standardize your data + other simple unlocks

We’ve all heard the story before: bookstore magnate who founded (and crashed) one of the first Silicon Valley unicorns, is reinventing said failed grocery delivery concept…but this time with robots. 

Wait, what?

If you’re not quite keeping up, you’re not alone: Fewer than half of American adults routinely use AI tools, much less robots that do their shopping. But AI is already integrated in our lives in ways we take for granted — in capabilities like facial recognition or predictive text — and the tech is only getting stronger.

For Parabola CEO Alex Yaseen, if you work in ops, supply chain, or logistics and you’re not using AI, “you’re definitely falling behind.” 

Using new technology in general can broker trust with commerce partners, and AI in particular is becoming a partner in its own right: a tool that you can bounce ideas off of, or deploy to find the proverbial needle in the haystack.

The trick is finding the right way to apply AI in your supply chain work. 

Yaseen observes that potential users often wait for “the most perfect and most complicated use case,” the irregularly shaped hole in the puzzle that only one piece can fill. But what if the perfect AI use case is something you do every day?

Yaseen named three kinds of daily operational processes that could be perfect use cases for AI in operations. If any of these resonate, there’s a chance AI could be the solution to making your job much more efficient and scalable.

1. Extract data from emails and attachments

Invoices, bills of lading, contracts, rate cards — the work of supply chain and logistics hinges on a wide range of kinds of documents that don’t exist in standard formats, or even file types. Finding the right info within this unpredictable, unstructured data is typically manual and highly time-consuming.

Perhaps you need a delivery date that’s embedded in the text of a lengthy email, or a VAT rate that sometimes lives on the second page of a PDF — or maybe on the thirty-second. Legacy tools do well when they know exactly where to look, but break easily when faced with messy data.

This is where Yaseen recommends injecting some AI into supply chain work. Vision AI can “intelligently parse these documents, put the data into a standardized format,” and even integrate with a platform like Parabola to send that clean, standardized data to the places where it can be most useful, like a WMS or an ERP.

2. Analyze unstructured data in customer feedback

Customer feedback can provide valuable insight in many areas of the business, whether your product is a durable good or a technical service. Unfortunately, this kind of feedback is often hidden in unstructured data: short-answer form responses, product reviews across platforms, or video call transcripts. Finding a marketing pull quote is one thing, but sifting through all this data to identify trends you can operate on is another thing entirely.

Fortunately, AI tools exist that can quickly scale this kind of granular evaluation. 

From sentiment analysis to trend reporting, finding the needle in the haystack is easier than ever. “You can train AI models to automatically bucket feedback into categories you define,” says Yaseen. 

Imagine the ability to focus on criticism of a certain aspect of your product. You could run a batch of sneaker reviews through AI, ask it to pull out all sentences that mention sizing, then restrict your dataset to those sentences with negative sentiment.

This is just one way to turn your customer feedback from an unstructured format into actionable data. You still have to build the buckets — you can’t expect the machine to do all the work on its own — but AI tools can handle the plumbing.

3. Enrich incomplete data

There’s one last everyday use case for AI in operations: data enrichment. 

Here, Yaseen refers to “structured information that needs to be cleaned up and completed.” Think of a data table you’ve received with blank cells or rows, or one that contains numerous typos.

Yaseen offers the example of enriching address data — for example, filling in a zip code and two-letter state abbreviation where you only have a street address and city. With AI’s increasingly encyclopedic knowledge, all you have to do is tell your model the target format: “You provide that domain expertise, and the AI can then apply it at scale.”

Say goodbye to manual data tasks

In the competitive logistics landscape, ops processes are a key differentiator — and AI can be a really powerful tool. “Gone are the days of manual data processing,” says Yaseen. Now the question becomes: How well can you use the AI in daily ops to streamline and make your work less painful? Robots might not be writing your grocery list (yet), but using AI in your supply chain work is a no-brainer. Far from replacing human endeavors, AI-assisted technology allows you to maximize the leverage of your expertise, whether you’re using enrichment to go the last mile with incomplete data sets, or turning unstructured customer feedback into actionable business intelligence.