Can AI finally free us from the busywork of reading documents?

You’re probably familiar with traditional Information Management (IM) systems: indexing, scan-to-text, automated retention. These are proven and are already in widespread use, helping us to scan, archive, index and recall when needed. But what about the day-to-day business of summarizing and validating everything from contracts and invoices to massive market-research reports?

Are there ways AI can make this busywork easier?

How good is AI at parsing documents really?

Short answer: yes, and this software is already being used to interesting effect. Recent studies in digital forensics positions AI as a decision support tool. Evaluations of tools like ChatGPT‑4, Claude, and Gemini in image-based forensic tasks scored around 7.4 out of 10, with AI analyses completing in under two minutes, far faster than human review, which averaged over 25 times the time (42 minutes). AI-driven document analysis excels at rapid screening (especially at the sort of scale most medium to large sized organizations need).

This isn’t surprising. Even among AI bears, document analysis is something it’s really, really good at by design.

Let’s break down the advantages in really simple terms, by looking at what LLM AIs can do, beyond just reading the document.

1. Records management becomes more trustworthy

With AI document analysis built into whatever workflow you’ve got set up, the system spots odd files early, catching strange metadata, wonky formatting, or print quirks.

That extra layer of AI checks makes your records management more reliable. Now think of this AI power when it’s paired with a clear understanding of compliance rules and processes. One 2024 study drew a direct link between the financial losses associated with “unstructured data” and human error, for example, proposing AI as a solution.

2. Oversight is far easier and more rigorous

This sort of “forensic AI” replaces one-off reviews with automatic checks and alerts as documents are captured and stored:  such as an invoicing management system for example. It tracks every change and gives you a clear metadata trail if ever you need to audit. It’s as if there’s a watchdog on duty around the clock, always alert and ready to flag anything suspicious.

3. Less manual work

When AI spots something odd, it pulls up only the suspect files. Employees save hours and get to focus on the real work. It functions as a digital assistant that filters information, allowing you to focus on the most relevant details.

4. It’s no longer a black box

When AI flags a document, it points out exactly what looked off, maybe an odd date or a font mismatch, so you’re never left guessing. A human employee in your organization is still making the final call when the AI flags say, a particular contract for legal reasons, and everything the AI did is saved in a clear log you can review anytime. “Transparent AI” and “Reasoning models” are becoming more popular for this exact reason.

This is (gradually admittedly) helping to address earlier concerns that AI arrives at conclusions in a way that isn’t really auditable. The aforementioned “black box” example.

What about archived documents?

Running your archives through a document analysis orientated AI system usually involves scanning a lot of physical records first. This historical data actually helps to optimize the analysis moving forward.

Every document gets auto appraised and slotted into the right category the moment it’s added. This means you’ve got the metadata you need to keep records authentic and reliable over decades, no more guessing if you’ve missed something important. A really simple way of thinking about this is like having your own personal librarian, just for your organization. There are so many use cases here that it’s difficult to give a comprehensive overview for all of them, but one particularly intriguing example would be going back through historical documents to spot suspected fraud or corruption within huge government agencies.

Initiatives like the InterPARES Project, which emphasises authenticity, reliability, and standards for archives are a big part of helping all of us work towards the same guidelines .

How can I implement all of this?

1. A human-led approach: AI flags suspect items; qualified professionals make final determinations. This maintains a human gatekeeper and helps to alleviate fears around structural redundancies.
2. Governance design: Clear procedures for model training (how you “train” your implementation of GPT, Gemini or another model to understand your business, essentially, which is very important. This helps in identifying what exactly looks “odd” within the framework of your business. Manufacturers will have different sorts of idiosyncrasies in financial reporting to say, professional service firms.
3. Integration: Crucially, embedding tools into existing workflows so you’re not replacing everything all at once, scanning pipelines, ECM or DMS systems. In general, a lot of this starts with scanning.

As the technology improves, AI forensic document analysis offers a rational next step: a means to build trust in the unseen details of physical and digital records management.

Want to start scanning and analyzing your own document flows? Get in touch with a Crown Information Management expert today and help you get started from step one!