The Future of AI Isn’t the Model. It’s the Context.

Why Content & Context Matter in the Age of AI

Machines can reason like knowledge workers now. While this is impactful for most industries, it is especially significant for the Enterprise Content Management (ECM) industry. The content we store has always been important, but this new technology unlocks it in an entirely different way: business intelligence and automation at scale.

Before everything changed on November 30, 2022, when OpenAI released ChatGPT, documents primarily supported a step in a workflow. Organizations delivered documents to applications for users to read, analyze, and use in making decisions. In either case, whether supporting process automation or decision-making, work stopped while individuals reasoned over documents and determined the next step.

That is no longer the only paradigm.

Today, machines can participate in many of those reasoning tasks. As a result, ECM systems play an increasingly important role in helping organizations reach the next level of automation and intelligence.

In these scenarios, your ECM has always done the heavy lifting behind the scenes, ensuring the right document is presented to the right person at the right time. Imagine if a judge referenced a court order that was not the most current version and enforced an order that was no longer valid. Even worse, from a governance perspective, what if a complaint containing confidential information became public?

These are the challenges ECM platforms have solved for decades. The difference is that they are amplified today because organizations can point large language models (LLMs) at documents and request intelligence, insights, and automation at scale.

If you want to do this safely and efficiently across large collections of information, you must first establish a strong content foundation. That is where OnBase plays a critical role.

However, having your documents in good order is not enough to automatically realize the benefits of these new tools.

For that, content must be transformed into context.

This concept is commonly referred to as context engineering, and it is top of mind for many organizations implementing intelligent applications. Simply put, context engineering is the practice of providing a model with just enough relevant information to generate the correct response.

Too much information can create confusion. Too little information means the model was never given everything it needed to arrive at the correct answer.

The goal is to provide the right context for the task at hand.

This is where Hyland’s Knowledge Discovery, enabled by the Enterprise Context Engine, comes into play.

Whether the task is answering a straightforward question such as:

“Which plaintiff is on this court order?”

or a more complex question such as:

“Which attorneys have filed a motion to dismiss that was denied and then gone on to lose the case at judgment more than once?”

Knowledge Discovery can identify and return the appropriate context, governed first by your OnBase system, to support both human and agentic decision-making.

For decades, ECM platforms have excelled at managing content.

In the age of AI, their value increasingly lies in transforming that content into context.

The organizations that succeed with AI will not simply be the ones that manage content effectively. They will be the ones that can transform content into context and context into action.