Unlocking the hidden power of unstructured data with AI

Hyland is helping enterprises turn their fragmented, unstructured data into governed, AI-ready intelligence

Sponsored Feature Enterprise AI is rapidly reshaping regulated industries like healthcare, banking, financial services, and insurance, taking on the complex, high-stakes work that once slowed clinicians, claims teams, and administrators to a crawl. From accelerating diagnostics to automating entire claims journeys, AI and agentic workflows act as digital coworkers that are redefining what operational excellence looks like in two of the world’s most burdened industries.

The biggest roadblock to successful enterprise AI deployment isn't software; it's data. Fragmentation is the cause of the failure of so many AI pilots, and the true barrier to reaching meaningful agentic automation. Up to 80 percent of enterprise information is locked in unstructured formats across disconnected systems. This fragmentation of data is the primary obstacle to feeding AI initiatives the coherent, contextual information they need to succeed.

Healthcare, insurance, financial services, and manufacturing all rely on content-rich, compliance-heavy documents. Unlocking their value requires understanding context, relationships, governance, and meaning. That's why forward-thinking companies are turning to Hyland to make their unstructured data AI-ready without massive migrations or replatforming. Hyland Content Innovation Cloud solves this unstructured data challenge without extensive migration processes.

The catch-22 of data distribution

Companies have accumulated massive amounts of content with potentially great value. The challenge is that the data within this content is scattered across repositories, trapped in PDFs, buried in emails, and isolated in department silos. That makes it impossible for these enterprises to successfully apply AI to process complex workflows and expedite informed decisions.

Mike Campbell, chief product officer at Hyland, sums it up in two sentences: "All this digital information represents the collective institutional knowledge of an organization. But it can't be leveraged when it's fragmented."

The costs of lost value from data fragmentation amount to more than inefficiency. In the healthcare industry, it can mean the difference between life and death. Scattered patient data can delay treatments and increase the risk of wrong decisions or decisions made too late.

How agentic automation is rewriting the rules

 In financial services and insurance, fraud represents hundreds of millions of dollars in losses. When context is missing, these industries also struggle with proving compliance during auditing processes. Hyland's approach allows organizations to successfully deploy enterprise agents that can make decisions based on the full context of an enterprises’ content, unifying, enriching, and validating data to reduce risk and improve outcomes.

Many organizations have over 20 separate content repositories. HR departments alone may maintain more than seven. This creates the ultimate catch-22: enterprises need AI and agentic automation to make sense of their content, but their content is too fragmented to feed into these systems effectively.

Evaluating AI readiness

How can enterprises determine whether their content infrastructure is genuinely AI-ready? Campbell frames it around two key questions.

First: Do you have the ability to access and leverage all of the different content in the different repositories? Second: Do you have that rich enterprise context that enables institutional knowledge? You need both to be able to tap the value held by unstructured data.

Consolidating everything into a single repository is not a feasible solution for two reasons. One is that it is both too costly and time consuming to work through what amounts to millions of documents. The second is that they can pose a threat to regulatory requirements.

Maintaining governance while enabling AI

In regulated industries, specific content repositories are set up for compliance reasons. In some cases, documents pertaining to a lawsuit may have to be retained together, or in healthcare, certain records must be retained for seven years.

Even outside such industries, Campbell points out, "you wouldn't want to take all documents in an HR system and feed them to a large language model (LLM) and ask questions about it, because there's a lot of confidential and private information."

The good news is that Hyland has extensive experience managing sensitive business content. Now it's bringing that capability into the agentic age with a federated, context-aware approach.

The enterprise context engine turns chaos into context

Instead of trying to migrate all data, Hyland's Enterprise Context Engine AI draws on context and reference points to build understanding for AI. In that way, it works in the same way humans do when making countless daily decisions. As Campbell explains: "It identifies relationships between different pieces of content so the engine can provide context. It understands the relationship between content, workflows, and applications."

The engine identifies relationships between documents, workflows, and applications, establishing context that AI can use to deliver more accurate insights. It leverages metadata, industry-specific structures, and knowledge graphing to conceptually connect data scattered around files in various systems. This unification represents what Campbell calls "the institutional knowledge of the organization."

As organizational knowledge is always evolving and growing, this is not merely a one-and-done activity. New content is always added and updated to correspond to what goes in through the various departmental repositories. As Campbell notes, "There also could be changes in retention policies. A document that is valid only until December 31, 2025, for example, shouldn't be included as valid beyond that date."

The system continuously curates these relationships, links, and dependencies to ensure the collective institutional knowledge stays up to date and reflects the current state of the organization. Once that is set up, it's possible to scale up processes, no matter how many repositories an organization maintains.

Advancing beyond traditional document processing

Traditionally, intelligent document processing relied on OCR and rule-based extraction. These methods could tell organizations what was on the page but could not tell them where on the page a particular piece of information was or what insight it conveyed. AI transforms that process.

"With AI, you can extract semantic meaning and pull out all the insights you need," Campbell says. For example, a system might be able to tell whether a handwritten document included a guarantor signature. Traditional intelligent document processing would have required a lot more manual intervention to arrive at that kind of answer.

Agents that get the job done with transparency

The Content Innovation Cloud includes Agent Builder, a tool that allows organizations to create context-aware automated enterprise agents. Users just have to specify what the agent should do, the information it should use, and the expected outcome. The agents then find the best way to complete the task.

Users are already making use of these agents in their workflows. For instance, in patient billing automation in healthcare, there might be a point where a human needs to review a collection of content to verify that a person is who they say they are or that they're eligible for a benefit.

Looking ahead, Hyland is developing what Campbell calls an agent mesh. It's a coordinated set of enterprise agents that can handle end-to-end workflows. Consider unemployment benefits processing. A caseworker normally must review documents, check for fraud, assess eligibility, and recommend related benefits. An agent mesh could collect, analyze, and verify documents, while also surfacing other programs an applicant may qualify for.

Campbell stresses that these are being built for transparency to meet the needs of regulated industries. "You have to be ready for an audit to substantiate why you arrived at a decision, especially if someone contests a denial of a claim." Rather than act as a black box, Hyland's agentic system will have the ability to explain why a decision was made, maintaining human in the loop at all times.

Unlocking data and new AI opportunities

 “More and more, enterprises are realizing the goldmine hidden in unstructured data," Campbell explains. "In the past, trying to make sense of it meant sifting through millions of documents by hand; a task no one had the time or patience for. But now, with AI in the mix, we’re cracking open those insights, giving businesses the tools they need to make smarter decisions and hit their goals faster than ever before.”

This is the way forward for enterprises looking to gain the competitive advantage of AI at scale.

Sponsored by Hyland.