If you scroll through LinkedIn today, you’ll be led to believe that generative artificial intelligence has solved every business problem imaginable. We’re told that large language models can write our emails, code our software and, according to many vendors, manage our supply chain documentation.

For logistics leaders, the promise is seductive. The industry is drowning in unstructured data: PDFs, scanned bills of lading, messy commercial invoices and email attachments. We know the status quo is broken; industry data consistently shows that human error is the leading cause of fulfillment issues. The idea that we can simply feed these documents into a "magic AI box" and get perfect data out is the holy grail of automation.

However, there’s a hidden danger in handing over mission-critical data entry to GenAI.

While LLMs are miraculous at creative tasks, they suffer from a fundamental flaw when applied to logistics: They’re probabilistic rather than deterministic. In an industry where a single wrong digit on a Customs form can result in a $10,000 fine or a container stuck at port for weeks, "probably right" isn’t good enough.

It is time we stopped confusing "intelligence" with accuracy.

The Hallucination Risk

To understand why GenAI fails at data extraction, you have to understand how it works. An LLM doesn’t "read" a document the way a human or an AI optical character recognition (OCR) engine does. Instead, it predicts the next most likely word in a sequence based on patterns it learned during training.

When you ask an LLM to extract data from a commercial invoice, it isn't looking up the facts; it is making a statistical guess based on the context.

In creative writing, this "guessing" is a feature; it’s what makes the AI sound human. In logistics, this feature becomes a bug known as hallucination.

An LLM might confidently and repeatedly extract only three line items from a table that clearly shows four lines, while at the same time reliably extracting all line items from tables with 100s of rows. This is simply because the model saw a pattern in its training data that associated that specific supplier with larger shipments. AI may invent SKUs that look plausible but don’t exist.

The AI doesn't know it’s lying. It is simply completing the pattern. However, for a customs broker, that "pattern completion" is a compliance violation waiting to happen.

The 95% Accuracy Trap

Many AI vendors will argue that their models are 95% accurate. If Spotify guesses the wrong song 5% of the time, nobody cares. In the supply chain, 95% accuracy is a disaster.

Consider a freight forwarder processing 1,000 bills of lading a week. A 95% accuracy rate means that 50 documents every week contain critical data errors.

This creates a paradox. If you can't trust the AI, you have to verify everything. You haven't automated the process; you've just added a step. You’re still paying for human labor, but now you’re also paying for the AI software.

LLM errors are often subtle. A traditional "dumb" parser might fail to read a blurry field and return an error message. This is a "safe” failure, in which the human knows they need to intervene.

GenAI, however, is prone to "silent” failures. It will extract the wrong container number with 100% confidence. These errors are the most expensive kind, as they’re often only caught when the truck arrives at the wrong warehouse, or the shipment is rejected by Customs.

The Case for ‘Boring’ Automation

So if GenAI isn't the silver bullet for document processing, what is? The answer lies in a technology that’s far less hype-driven but far more reliable: deterministic parsing.

Unlike probabilistic AI, it relies on rules, templates and zonal extraction. You teach the software exactly where to look on a page to find, say, the invoice date or total weight. The software extracts the data from those specific coordinates every single time.

It doesn’t guess. It doesn’t hallucinate. It either finds the data exactly where it should be, or it flags the document for human review.

For structured and semi-structured documents, which make up the vast majority of logistics paperwork, this "boring" automation approach is vastly superior. It offers the one thing supply chain managers crave more than innovation: predictability.

This isn’t to say that AI has no place in the supply chain. The most effective workflows in 2026 and beyond will be hybrid.

GenAI should be used for what it’s good at: handling the messy unstructured work, including:

• Summarizing long email threads between brokers and carriers;
• Normalizing data (such as converting "ten kilograms" to "10 kg"), and
• Classifying documents (such as identifying that an attachment is an invoice and not a packing list).

When it comes to the actual data extraction from supply chain documents, which pulls the specific SKU, weight and price from the page, we must rely on deterministic engines.

As we build the supply chains of the future, we must resist the urge to apply “magic button" technology to every problem.

GenAI is a powerful tool, but it’s a creative engine, not a precision instrument. Using it to read a bill of lading is like using a poet to do your accounting: The results might look convincing, but the numbers won't add up.

For the heavy lifting of logistics data entry, specialized parsing tools remain the industry standard. They might not be as trendy as the latest chatbot, but they have one distinct advantage: They get the numbers right.

Sylvestre Dupont is co-founder and chief executive officer of Parseur.