Are You Playing Catch-Up?

AI in Document Automation isn’t optional anymore and if you’re not prepared, it can cost you.

 By Steve Hatajlo

Artificial Intelligence isn’t coming, it’s already here. If your operations involve handling business-critical documents like invoices, purchase orders, shipping notices, or anything in between, ignoring that reality isn’t just risky. It’s operationally irresponsible. 

The Traditional Tools Just Don’t Cut It Anymore 

Traditional rules-based engines and OCR systems were fine when documents looked more or less the same. But when you throw in semi-structured documents, vendor-specific formats, and human quirks, the wheels start to come off. You end up spending more time managing exceptions than you do processing data. That’s not scalable. And that’s not acceptable if you’re serious about automation. 

That’s where AI—and more specifically, large language models—come into play. Not because they’re trendy, but because they finally give us a tool that can handle the ambiguity that traditional systems can’t. 

Using AI Before It Was Trendy 

At FSI, we’ve been in the business of document conversion for years. We take information in one format—fax, email, PDF, you name it—and translate it into a structured, machine-readable format like EDI or JSON so it can be used in ERP, TMS, or other enterprise systems. This isn’t new. 

What is new is the level of variability we’re seeing in documents and the complexity that comes with that. More trading partners, more formats, more exceptions—and less tolerance for errors. 

Several years before ChatGPT and Gemini dominated the landscape, we were looking at AI. We didn’t start experimenting with LLMs to impress anyone. We started using them because they solve a real operational problem. These models can look at a document that doesn’t match any known template, understand its structure, and extract the right data points with a high degree of accuracy. They don’t just see pixels—they understand context. That’s a game-changer. 

AI is Not a Magic Bullet 

But here’s the part nobody wants to admit: AI is not a magic bullet. It’s not going to “just work” out of the box. You can’t throw it at your invoice backlog and expect perfection. Like any tool, it’s only as good as how it’s applied. You need experience. You need domain knowledge. You need to understand where automation makes sense and where human review is still the right call. 

We’ve learned that AI excels when it’s guided. Give it examples, provide guardrails, and build workflows that know when to let the machine do its job—and when to flag something for a human to look at. That’s how you get value. Not by replacing people, but by reducing the noise so your people can focus on what actually matters. 

Beyond Extraction 

AI as a tool isn’t solely about extraction. AI helps us improve validation, matching, and reconciliation. It’s learning to distinguish between a unit price and a total, to identify missing data, to recognize when something doesn’t quite look right based on historical patterns. It’s the kind of situational awareness that used to require a seasoned employee with years of experience. Now, we’re building that intuition into the software. 

Why You Shouldn’t DIY This 

The other misconception? That you need a data science team to do this. You don’t. What you need is a partner that’s already doing it—who’s built the models, trained them on the right document types, and figured out how to blend AI with real-world process design. That’s not easy. But it is possible. And it’s already happening. 

And look, could you build this yourself? Sure, technically. But building is just the start. You’ve also got to train it—every document type, every format, every exception. And when the next change rolls in? You’re retraining it again—you’re always teaching it something new, that takes time and resources away from your core business. 

Internal AI Expenses Can Add Up if You Aren’t Ready 

Right now, companies planning to implement AI in Document Management, have significant expenses. It involves licensing an engine and subscribing to page consumption charges. And those costs aren’t going down anytime soon. 

On top of that, most companies are not technically ready to provide the resources required to set up and manage these systems. Implementation still takes significant technical prowess, especially in configuring workflows, scripting rules, and managing updates. Even after all that, manual intervention is still required—because documents must be trained into the engine. And that training is ongoing. 

This is where outsourcing to a company like FSI makes more sense. There are economies of scale in play—page costs can be reduced when the data capture portion is outsourced to a partner who’s already built the infrastructure and trained the models. There’s no need to pay expensive maintenance or annual subscription fees, either. That’s all absorbed in the service. 

We‘ve developed a hybrid solution that is hard to replicate, with human eyes reviewing, correcting and adjusting the AI’s logic so that it continually improves. 

So before assuming AI is a plug-and-play solution, ask yourself: are you ready to handle all of that on your own?