From Document Capture to Intelligent Automation: How AI Is Changing Enterprise Content Management

The next evolution in enterprise content management is not simply digitizing and storing documents. It is making the information inside those documents understandable, actionable, and capable of driving the business process.

We Didn't Get Here Overnight

There has been a natural progression in this industry.

First, we needed to convert paper into digital images.

Then OCR gave us the ability to recognize the text contained within those images.

Document capture technology took us further by allowing organizations to identify document types, capture index information, and deliver documents and data into other business applications.

Enterprise Content Management platforms such as Hyland OnBase®, FileBound® by Upland, and Microsoft SharePoint® provided us with the infrastructure to manage that information — security, retrieval, workflow, records management, governance, case management, and integration with other enterprise systems.

All of those capabilities remain important.

The next step is making the information itself more intelligent and actionable.

Document Intake  →  Capture  →  OCR  →  Content Management  →  Workflow  →  Intelligent Document Processing  →  Intelligent Automation

One technology does not necessarily replace the one before it. Each builds upon and enhances the capabilities already in place.

Recognizing a Document Isn't the Same as Understanding It

This distinction becomes particularly important when talking about OCR and AI.

OCR is very good at answering a basic question: What characters and words are on this page?

That's important, but businesses usually need answers to much more meaningful questions: What kind of document is this? Who sent it? What information in the document is important? Where should that information go? What business process should happen next?

That is where Intelligent Document Processing begins to extend traditional document capture.

IDP uses AI-assisted technologies to help classify documents, extract relevant business information, validate the results, and route the information into downstream systems and processes.

We'll take a much deeper look at Intelligent Document Processing in a subsequent article. For purposes of this discussion, however, the important point is that AI is adding another layer of intelligence to the document capture and content management environment.

Think About an Invoice

Accounts payable provides a good example because most organizations understand the process.

An invoice arrives as an email attachment.

Getting the invoice into an electronic system is relatively easy. We have been doing that for years.

But what happens next?

Someone — or something — needs to recognize that the attachment is an invoice. The vendor needs to be identified. The invoice number, date, purchase order, totals, and other relevant information may need to be extracted. That information may need to be validated. The invoice may need to be associated with an existing transaction and routed for approval. The information may ultimately need to reach an ERP system, while the document itself is retained in the organization's content management system.

Historically, many of those steps involved people.

AI gives us an opportunity to automate more of them.

And that, in my view, is where this gets interesting.

The real value isn't simply that AI can extract an invoice number from a document. The value comes when that information can be trusted, passed to another system, and used to move the business process forward.

For me, the important point is that extracting the data is only useful if we can do something with it. The real value comes when that information can be trusted, delivered to the right system, and used to move the business process forward.

AI Doesn't Mean Starting Over

This may be one of the most important points for organizations that have already invested significantly in content management and document capture.

You don't necessarily have to tear everything out and start over.

Many organizations already have very good systems in place. They have mature Hyland OnBase environments. They have document capture solutions. They have established workflows, security models, retention policies, ERP integrations, and years of valuable business content.

There is another reason I believe those existing ECM investments are becoming even more important in the age of AI. A very large percentage of the information organizations depend upon is unstructured — documents, PDFs, emails, images, contracts, correspondence, reports, and other content that does not conveniently reside in the rows and columns of a traditional database.

Gartner has estimated that 70% to 90% of organizational data is unstructured. Gartner also says that almost every Generative AI use case requires organizations to extract, qualify, and govern significant volumes of unstructured data. That is an important point because AI cannot create business value from enterprise information it cannot reliably find, access, understand, and govern. Gartner source

Jitesh Ghai, Hyland’s CEO, sums up the opportunity well. Generative AI gives organizations new ways to bring structure and meaning to content that historically has been difficult to use at scale. Hyland similarly reports that 80% to 90% of enterprise data is unstructured, reinforcing just how much potentially valuable business information sits outside traditional structured databases.

This is why I believe understanding how AI improves Enterprise Content Management (ECM) is becoming more important, not less. An organization may have tremendous amounts of valuable information, but AI cannot effectively use information it cannot find, access, trust, understand in context, or appropriately govern. ECM provides much of that foundation by bringing enterprise content under management — applying security, permissions, metadata, retention, governance, and integration while making that content available to the business processes and technologies that need it.

Put another way, before we can talk seriously about making enterprise content AI-ready, we first need to know where that content is, what it is, who should have access to it, and how it relates to the business. That has always been part of the value proposition of ECM. AI simply makes the value of that foundation much more apparent.

Sources: Gartner - Unstructured Data Is the Missing Ingredient to Prepare AI-Ready Data  |  Hyland - AI Readiness for Organizations  |  Hyland Content Innovation Cloud overview

The opportunity is to determine where AI and IDP can enhance those investments.

At IDT, this is how we think about the modern content ecosystem.

CAPSYS CAPTURE™ and CAPSYS CAPTURE ONLINE™ can provide the capture and ingestion layer, bringing documents and information into the enterprise from distributed sources.

Our IoT Smart Connected Scanning™ technology can extend that capture process directly to the point of origin, allowing documents to enter CAPSYS CAPTURE ONLINE and downstream business systems without requiring a dedicated PC at the scanning location.

CAPSYS AcuityAI™ can introduce AI-assisted document classification, data extraction, validation, and intelligent processing for structured, semi-structured, and unstructured documents.

Hyland OnBase®, FileBound® and Microsoft SharePoint® can continue to provide the enterprise content management foundation for managing content, processes, cases, records, governance, and integrations.

And Hyland's Content Innovation Cloud™ represents another evolution toward bringing enterprise content, applications, processes, AI, content intelligence, and Intelligent Document Processing together.

These technologies do different things, and that is precisely the point. IDT's role is to bring the appropriate technologies together around the customer's business process rather than suggesting that any single product is the entire solution.

Where does a person add value, and where are we asking a person to perform work that technology can reasonably do for them?

If an employee is spending hours opening attachments, identifying document types, manually entering information, renaming files, indexing documents, or moving information from one system to another, we should be asking whether technology can handle some of that work.

I've always believed technology is at its best when it takes repetitive work off people's desks and allows them to spend more time on exceptions, customer interaction, analysis, and the decisions where their experience actually matters.

That is a much more practical way to think about AI.

Start With the Business Problem, Not the AI

Organizations interested in AI frequently begin by asking: Where can we use AI?

I think there is a better question: Where are people spending time manually interpreting documents and moving information between systems?

Start there.

Look at where documents originate. Look at the different document types. Look at the amount of variability. Identify the information employees are manually extracting. Determine what system ultimately needs that information. Understand the workflow that follows and where exceptions occur.

Then determine whether capture, IDP, workflow, content management, integration, AI — or some combination of them — can improve the process.

Security, governance, human validation, cloud versus on-premises requirements, integration requirements, and measurable business outcomes all need to be part of that discussion as well.

That's why I would start with the business problem and then determine where AI can realistically improve the process — not the other way around.

From Managing Content to Putting Content to Work

For decades, our industry has talked about managing enterprise content.

That remains important. Organizations still need to capture information, secure it, retain it, govern it, find it, and make it available to the right people and systems.

What AI-powered enterprise content management changes is what we can potentially do with that content.

We can increasingly move from simply storing a document to understanding it. From understanding it to extracting the information that matters. From extracting information to initiating a workflow. And from workflow to a much more intelligent and automated business process.

I don't view that as the end of Enterprise Content Management.

I view it as the next evolution of Enterprise Content Management.

The organizations that benefit most from AI may not be the ones chasing the newest AI technology. They may be the organizations that look carefully at the content-intensive processes they already have and ask a relatively straightforward question: Where are the bottlenecks, and what can we realistically automate or improve?

At Integrated Document Technologies, that is the conversation we believe organizations should be having. The opportunity isn't AI for AI's sake. It is bringing together capture, Intelligent Document Processing, enterprise content management, workflow, integration, and human expertise to make information more useful and business processes more intelligent.

And after more than three decades of helping organizations manage documents and content, that feels less like abandoning the technologies that got us here — and much more like the natural next step.

Ready to explore where intelligent automation can add value to your existing content environment?