During a recent internal knowledge session, our consultant Arne De Wilde mapped out the five main ways to approach IDP, from traditional regex to popular tools like UiPath, ABBYY, Azure, and the Power Platform. Let’s see what works, for which kinds of organizations, and in which kinds of projects.
Let's start with the absolute basics. Regular expressions might be the oldest trick in the book, but they still hold a lot of value today. Regex is highly effective when your documents are digitally generated and consistent. The setup runs fast and costs very little.
But there is a catch, and quite an important one. Even a tiny format change will break the system and create major maintenance headaches. Use this approach for fixed systems where your layouts literally never change.
If you want something that is quick to set up visually, this is your go-to method. It shines when you deal with a limited number of suppliers and highly predictable layouts.
The downside usually shows up when new layouts pop up or when tables change dynamically in size. Maintaining those templates quickly turns into a complex chore when your document variety increases.
Machine learning models actually learn patterns and provide a confidence score. This helps to automatically route questionable documents to a human for a quick review. You can also use pre-built templates for common documents like invoices to heavily decrease your development time.
The main trade-off here is the massive labeling and training effort you need for custom templates across many different layouts.
This is the new and popular kid on the block. Generative AI is incredibly fast to start and highly flexible across diverse formats. It steps in when training a custom ML model per template becomes completely unrealistic.
But you have to watch out for the black box effect, because GenAI can hallucinate information. You absolutely need strict guardrails, extra validation steps, and a close eye on your running costs.
As we mentioned earlier, newer doesn't always mean better. The smartest strategy often combines the power of multiple technologies.
For example, you can use strict, deterministic approaches for your standardized workflows, because the input is consistent and predictable. Then, you can deploy generative AI to handle the weird exceptions and highly variable documents that would otherwise cause issues.
So, how do you pick the right path? It all depends on your document variation, total volume, maintenance capacity, and privacy rules.
Our advice is quite simple: ignore the AI hype for a minute. Instead of running to an AI company, run a short proof of concept with your own documents to see what fits your operational reality. For us, IDP is just one part of a broader automation ecosystem. There are plenty of other options out there that might be better suited for your particular case, so it pays to have an expert guide.
Not sure where to start? Bring us a small sample set of your documents and a list of your must-have fields. We’ll map your exact needs to the right technological approach(es), so you know exactly what you need.
