Alex Lipinski
Solution adoptions and upgrades can be expensive, but so can servicing and supporting aging technology when a better alternative not only means a significant time improvement, but a cost decrease.
Modern intelligent document processing (IDP) wields the same characteristics as traditional optical character recognition (OCR) capture, and then some, by leaning on natural language processing and machine learning to offer flexibility beyond rigid rules and templates to tackle unstructured data.
Those additional benefits come with additional costs. But so do the costs of maintaining and constantly servicing OCR capture.
Takeaways
- OCR captures letters. IDP captures meaning.
- Traditional capture struggles to hone data that’s necessary to thrive in an AI-forward market.
- The best practices five years ago are not necessarily needed today.
- The right time to transition is when the cost of services/support, consistent manual training/re-training, and missed opportunity outweigh an IDP investment.
Watch the Mostly Unstructured Podcast!
- IDP improves unstructured data and unlocks insights.
- IDP is a keystone tech for AI pipelines.
- Assess your current-state unstructured document processes before agentic.
Most enterprise data is unstructured
Traditional OCR (optical character recognition) document capture technology helps organizations to successfully hone their high volumes of forms, spreadsheets, financial data, and other vehicles of structured data – reducing manual keying and moving content through the document lifecycle. No one is taking that away. But today, most enterprise data is not structured.
Today, your data comes from way more than just documents, but an entire content ecosystem. Content is a free-form email containing rich media or burying important financial data in no standardized format. It’s a customer review scattered across a web of social media comments and posts. It’s data that needs to not only live in a repository, but thrive in a queryable data lake of relational and non-relational data. And that’s where traditional document capture starts to show its age.
What is Intelligent Document Processing?
Intelligent Document Processing (IDP) takes the core of traditional document capture and OCR and evolves it:
- With machine learning – enabling a capture system to learn over time, reduce manual intervention and retrain on new data sets.
- With natural language processing – enabling the system to understand the context of content and in doing so, stand a chance against unstructured content.
- With AI and analytical readiness of outputs – providing new formats to files to make them ingestible by data lakes, and easily parsed by AI projects, of which everyone is after.
If your content capture solution can’t do those things today, it has unfortunately become legacy software. And while that doesn’t necessarily mean you’re in trouble, you are indeed missing out on opportunity.
Legacy advice for legacy capture
Some time ago, KeyMark put together several recommendations for improving and maintaining an existing capture solution, featuring such advice as:
- Periodically retrain your system to properly incorporate fresh sample documents collected during processing.
- Identify and resolve training conflicts within sample sets.
- Generate and configure new extraction knowledge base.
- Configure new file layouts to address degradation in automated extraction.
- Identify new business requirements resulting in increased manual validation efforts.
These recommendations also defined services and help that KeyMark offered for capture users. But the addition of AI-based machine learning and natural language processing to modern capture IDP solutions has made several of our recommendations and services either a little bit redundant, or efforts for things like retraining, conflict resoultions, and file reconfig are significantly easier.
Legacy drawbacks
To that point, your legacy solution could be receiving expensive service (as much as we love being of service) when there is a modern alternative that can handle much of the pre and re-training with much less aid. And while we insist that 100% accuracy is impossible without human-in-the-loop, zero-shot/near-shot, which is the system’s ability to view a document type and layout it’s never seen before and make a perfect/near perfect analysis on the first go, significantly reduces the amount of exception handling required. Other drawbacks:
- Higher manual workload to classify documents, validate extracted fields, and handle exceptions.
- Required template redesigns for new forms, fields, and files.
- Less plug-and-play with modern workflow, ERP, or analytics platforms.
- Data often needs to be exported manually or through custom scripts for data lake integration.
- No contextual understanding and insufficient unstructured data performance.
- Requirements for frequent updates, retraining and manual fine‑tuning.
- Opportunity cost of a modern AI solution.
IDP as a fast way forward
IDP classifies a much wider range of content by analyzing what the document says, how it is laid out, and what it scans means in its context. In this way, IDP separates, extracts, and classifies data from the unstructured, as well as multiples of the same form or document coming from different vendors in different formats. And while a level of exception handling still exists, those exceptions are fed to a model that remembers the correction and improves performance over time.
Finally, and critically for AI projects, the best IDP solutions output captured data in formats that data lakes and AI queries depend upon, allowing your data analysts and RAG, a GenAI’s way of improving response accuracy by retrieving key business data, to do their thing.
Your signs an upgrade is needed
So, when is it time to trade legacy capture for modern IDP? When:
- You want gen AI capabilities, but your current platform can’t convert the quantity and quality of data to support expensive AI services.
- You rely on separate tools with custom code connecting them for scanning, classification, data extraction, routing, and analytics,
- IT or a significant investment in services is needed to code changes and perform maintenance.
- You’re preparing to expand capabilities to new departments and processes, but the required resources to configure the solution in new ways is a roadblock.
- You desire captured content in a database but can’t output content into the correct formats without lengthy and manual custom scripting.
The right time
Updating a legacy document capture system is an undertaking. It requires budget, change management, and a clear understanding of over 450+ vendors and what is right for you. The downsides of staying with a solution that hasn’t evolved are paying for services that with a modern solution could be pared down, more manual work when updated technology now exists, a need for stronger data analysis than what is currently capable, and the opportunity cost of a database that’d otherwise be viable for GenAI interactivity.
That right time to transition is when those downsides outweigh the investment of a shift to modern.
Find the right IDP tool for your upgrade.
Download the due diligence calculator to spot tools that
- Read many file types (scans, PDFs, emails, images).
- Sort documents and pull out fields you care about.
- Understand both tables and long complex documents.
- Flag uncertain items for review.
- Format data into standard and necessary formats.
- Isolate, track, and protect data. Avoid data pooling for training.
