The Work Isn’t the Problem. The Waiting Is. 

Most organizations are not drowning in documents because they lack systems like OnBase. They are drowning because too much of the process still depends on people stopping what they are doing to review, validate, search, compare, confirm, and move information forward. A workflow may technically be automated, but if a packet still waits in someone’s queue for review, the business is still operating at human speed. That distinction matters more now than ever. 

Across industries, operational pressure is rising from every direction. Customers expect immediate responses. Teams are being asked to do more without adding headcount. Processes span multiple systems, multiple departments, and increasingly complex compliance requirements. Meanwhile, the volume of information continues to grow. Friction rarely comes from the document itself. It comes from the pauses between decisions. 

That was the central theme during a recent DataBank and Hyland webinar focused on agentic document processing and the Hyland Content Innovation Cloud (CIC). Rather than presenting AI as a futuristic replacement for enterprise systems, the conversation focused on something far more practical: reducing the manual burden that still slows down critical business operations. 

Not replacing workflows. Removing the drag inside them.  

So where does the process usually break down? 

Enterprise workflows often look efficient from a distance. Documents are digital. Records are centralized. Routing rules exist. Content lives inside structured repositories like OnBase. On paper, the process appears to be modernized. Then reality shows up. 

Someone still has to: 

• confirm a form is complete 

• check whether a signature is missing 

• compare documents against policy requirements 

• reorganize files before routing them downstream 

• search across systems for missing context 

• validate information before work can continue 

Individually, those tasks feel minor. Together, they create operational lag that compounds across the organization. 

The webinar demonstrated this through a lending workflow used by credit unions processing indirect auto loans. Dealerships submit large document packets on behalf of borrowers. Those packets can contain dozens of pages, different document types, varying formats, and inconsistent ordering depending on the dealership. What made the workflow notable was not just the AI itself, but how it extended the existing OnBase environment instead of replacing it. The content, routing, governance, and downstream processes remained connected to the systems teams already relied on. 

Historically, teams manually reviewed every submission. If something was missing, a signature, a disclosure, a supporting document, the issue might not be discovered for hours. Sometimes days. By then, the borrower had already left the dealership, forcing everyone back into a process nobody wanted to repeat. 

The operational cost was not just slower processing. It was interruption. Employees were spending their time policing documents instead of moving business forward. 
 
This is where we need to notate the difference between automation and intelligent action. 
 
Traditional OnBase automation has always been effective at routing work once the rules are clear. The challenge is everything that happens before the workflow can confidently move forward.  That is where agentic document processing changes the equation. 

Instead of relying on rigid templates or narrowly structured extraction models, AI agents can evaluate content contextually. In the webinar example, the system was able to: 

• separate mixed document packets 

• classify documents automatically 

• identify missing information 

• validate signatures 

• extract relevant data 

• generate exception reports 

• prepare packets for downstream systems 

Instead of creating disconnected AI workflows outside the enterprise content ecosystem, these agents were operating alongside existing OnBase processes and repositories. 

What stood out was not the extraction itself. It was the ability to handle variation without constant reconfiguration. Different dealerships submitted different packet structures. Documents appeared in different orders. Forms varied slightly from source to source. The agents still understood what they were looking at. 

That flexibility is important because most enterprise bottlenecks are not caused by standard processes. They are caused by edge cases, inconsistencies, and the endless stream of “almost right” submissions that require human attention. Organizations have spent years trying to standardize inputs. AI agents are beginning to make that dependency less critical. 

The lending example resonated because the operational pattern is familiar almost everywhere. Healthcare teams manage intake packets, referrals, authorizations, and release-of-information requests that require layered validation before action can happen. Manufacturers process supplier documentation, quality records, shipping paperwork, and compliance files across disconnected systems.Government agencies work through case files, citizen submissions, and records requests that often depend on manual review before decisions move forward. The industry changes. The bottleneck does not.  

Many of these organizations already manage these processes inside OnBase today. The challenge is not document storage. It is reducing the manual review layers surrounding those workflows. Somewhere in the process, people are still acting as human middleware between systems. That is where organizations are now focusing their AI strategies. Not on replacing expertise, but on removing repetitive intervention points that drain time and attention. 

AI Adoption Is Becoming an Operational Decision, Not a Technology Decision. 

One of the more grounded points from the webinar was that organizations are not looking for wholesale reinvention right now. Most enterprises have already invested heavily in platforms, workflows, governance structures, and content management systems. The hesitation around AI is rarely about curiosity. 

It is about disruption. 

Leaders do not want to: 

• migrate massive repositories 

• rebuild workflows from scratch 

• create governance blind spots 

• introduce uncontrolled automation 

• destabilize systems employees already rely on 

That is why the positioning around the Content Innovation Cloud matters. The approach discussed was not centered on replacing OnBase, but on layering intelligence into existing environments. That lowers the barrier significantly. 

Organizations can start with: 

• one workflow 

• one validation step 

• one repetitive review process 

• one operational pain point 

Then expand from there. That incremental model is likely why these conversations are moving from experimentation into production planning. The focus is no longer “Where can we test AI?” It is becoming “Where is manual effort slowing the business down the most?” 

Speed is the easiest result to measure, but it may not be the most important one. 

The deeper operational value comes from consistency. When repetitive review work is handled intelligently: 

• employees spend less time triaging 

• exceptions surface earlier 

• decisions happen closer to real time 

• customers experience fewer delays 

• teams gain operational breathing room 

In the webinar, one comment stood out: “The one thing I really hate is waiting for stuff.” That sounds simple, but it captures the real business issue perfectly. Most operational frustration comes from waiting: 

• waiting for approvals 

• waiting for validation 

• waiting for missing information 

• waiting for someone to review a queue 

• waiting for work to move 

Agentic document processing is ultimately about reducing those pauses. Not by eliminating people, but by reducing the amount of routine analysis humans need to perform before work can continue. 

The most practical guidance from the webinar had little to do with AI models or technical architecture. 

It came down to asking better operational questions. 

Where are employees still manually entering information? 

Where are people spending time validating completeness? 

Where do workflows stop simply because someone has to check something? 

Where are employees searching for answers across systems? 

Those are usually the first signs that a process is ready for intelligent automation. For many organizations, the next evolution of OnBase will not be rebuilding workflows from scratch. It will be adding intelligent decision support directly into the processes they already trust. 

And increasingly, that is the pressure point organizations are trying to solve.