Hyland Connect

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From AI Pilots to Real Execution

Many organizations have already invested in AI, automation and digital tools. Yet results often fall short.

The problem is not the technology. It is the content behind it.

Across the enterprise, information is fragmented, unstructured and disconnected from the processes it is meant to support. As a result, many organizations remain stuck in pilot mode. AI initiatives produce isolated outputs but struggle to scale into consistent, repeatable business outcomes.

AI does not scale without content and context.

Organizations are discovering that AI models alone do not run businesses. Workflows do. And those workflows depend on content to drive decisions, trigger actions and coordinate processes across systems.

At the same time, many organizations are burdened by repetitive, manual data tasks performed across disconnected systems. Employees spend significant time on data lookups, validation, entry and routing across multiple applications. This work is time-consuming, error-prone and difficult to scale, limiting the impact of both automation and AI.

The Content Innovation Cloud enables organizations to move from AI experimentation to real execution by treating content as the orchestration layer for AI-driven work. By connecting content across systems, enriching it with context and activating it within workflows, it turns fragmented information into something usable and actionable.

This allows organizations to automate repetitive data tasks, improve consistency and free employees to focus on higher-value work. It also helps coordinate work across systems, where AI agents can validate information, make decisions and trigger next steps as part of broader business processes.

As a result, organizations can move beyond one-off use cases and begin scaling what works. Instead of automating isolated tasks, they can create more consistent, reliable ways of getting work done across the business.

Just as importantly, this happens without disruption. Content remains in place, governance is preserved and existing systems continue to play a central role. Organizations can modernize at their own pace while accelerating time to value.

The result is measurable, business-level outcomes:

Faster processing and decision cycles
Reduced manual intervention
Improved accuracy, more consistent decision-making
Better customer and employee experiences

Instead of starting over, organizations can build on what they already have and begin delivering real, quantifiable results.

What This Looks Like in Practice

The examples in this post reflect how organizations are already putting the Content Innovation Cloud to work.

Some are based on real customer implementations across industries such as financial services, healthcare, manufacturing and the public sector. Others reflect how Hyland applies these same capabilities internally across HR, IT, finance and customer experience.

Where specific customer stories are not referenced, the examples represent common patterns seen across organizations facing similar challenges with unstructured content, disconnected systems and manual processes.

Together, these examples show how organizations are moving from AI experimentation to real, scalable outcomes.

Finance

Moving Accounts Payable from Manual Review to Exception-Based Work

Accounts payable teams manage a constant flow of invoices arriving through email, PDFs and supplier portals. The volume alone is not the problem. The real challenge is the manual effort required to process each invoice accurately.

Invoices arrive in different formats with inconsistent layouts. Key data must be extracted, validated and matched against purchase orders and systems of record. Duplicate invoices, missing fields and exceptions require additional review. Teams often spend hours each day on repetitive validation work, and even then, errors can slip through.

This creates delays in processing, increases the risk of overpayment or fraud and limits the team’s ability to scale as volume grows.

With the Content Innovation Cloud, organizations use Hyland Intelligent Document Processing (IDP) to capture, classify and extract invoice data automatically. This transforms unstructured invoices into structured, AI-ready information that can be validated and routed directly into ERP systems.

Customer Example:
A large U.S. commercial insurer faced more than 1,000 hours of manual invoice review each month. By implementing IDP, they reduced manual processing time by up to 60 percent while improving fraud detection and compliance.

Hyland Example:
Hyland uses IDP to monitor shared inboxes, extract invoice data and route valid invoices directly into ERP systems. Teams now focus only on exceptions rather than reviewing every submission.

Instead of reviewing every invoice, teams focus on resolving issues, improving vendor relationships and strengthening financial controls. This not only improves efficiency, but also leads to more accurate financial data, fewer exceptions and more reliable decisions across the finance function.

Additional Finance Use Case Examples:

Accounts receivable automation to match payments and reduce reconciliation time
Expense processing to validate and route employee submissions
Financial document classification to support audit readiness and compliance

Customer Service and Experience

Delivering Consistent, Connected Experiences Across Every Interaction

Organizations are under pressure to deliver fast, accurate and consistent experiences across both employee-assisted and self-service channels. The challenge is that the information required to support these interactions is fragmented across systems, documents and repositories.

Customer service teams often need to search across knowledge bases, policy documents and historical records to answer a single question. At the same time, customers interacting through digital channels expect immediate, accurate responses without needing to contact support.

This creates two sides of the same problem. Internally, agents spend valuable time searching for information and may provide inconsistent answers. Externally, customers experience delays, conflicting responses or limited self-service capabilities.

The root issue is not access to information. It is the lack of connected, governed content that can be used consistently across interactions.

With the Content Innovation Cloud, organizations use Knowledge Discovery to connect content across systems and power AI agents that deliver accurate, context-aware answers in real time. These AI agents can be embedded across customer service tools, portals and digital experiences, ensuring that the same trusted information is used everywhere.

Customer Example:
Financial services and healthcare organizations use Knowledge Discovery to connect policy, claims and customer records across systems. This allows both service agents and digital channels to access consistent, real-time information, improving response speed and accuracy across every interaction.

Hyland Example:
Hyland’s Community AI uses Knowledge Discovery to power AI agents that answer customer questions directly within the community portal. More than 26,000 questions have been answered using governed content, enabling customers to find answers instantly without searching through documentation.

Instead of searching across systems or escalating requests, both employees and customers can access the same accurate, governed information when they need it.

The result is faster resolution times, more consistent responses and a seamless experience across both human-assisted and self-service channels.

Additional Customer Experience Use Case Examples:

Customer onboarding to capture, validate and route documents for faster account setup
Claims processing to extract, classify and validate data for faster adjudication
Call center assistant to provide consistent, permission-aware answers

Human Resources

Improving Open Enrollment and Employee Support at Scale

Human Resources (HR) teams manage a constant flow of employee questions related to benefits, policies and onboarding. During open enrollment, this volume increases significantly, creating pressure to respond quickly and accurately within tight deadlines.

The challenge is that benefit information is detailed and complex, often spread across multiple documents and systems. Employees need clear answers, but HR teams must interpret policies, ensure accuracy and respond consistently. This leads to a high volume of repetitive inquiries that consume time and limit the team’s ability to focus on more complex or sensitive employee needs.

At the same time, HR teams must manage large volumes of employee documentation, including benefits forms, payroll records and onboarding materials, which require consistent classification, routing and storage.

With the Content Innovation Cloud, organizations use Knowledge Discovery to power AI agents that provide instant, accurate answers based on governed HR content.

Customer Example:
A North American food distributor used IDP to capture and process onboarding and HR documents, allowing them to scale employee onboarding without increasing staff. At the same time, HR documents are automatically classified, indexed and routed across systems like Workday or SuccessFactors.

This improves employee experience, reduces administrative burden and enables HR teams to operate more efficiently during critical periods.

Hyland Example:
Hyland deployed an Open Enrollment Agent within its HR portal. Employees could ask questions about benefits and receive immediate, accurate answers. HR reduced repetitive inquiries and focused on more complex needs while maintaining control over content and responses.

Additional HR Use Case Examples:

Employee file management for onboarding, employee self-service and secure document access
HR document classification and indexing for payroll, benefits and employee records
Benefits and payroll document processing to reduce manual effort and improve accuracy
Policy and procedure access through AI agents so employees can find trusted answers faster

Sales and Operations

Reducing Friction in Order Processing

Sales order processes often span multiple systems and require coordination between teams. Orders may arrive through email, portals or other channels, each with different formats and levels of completeness.

The challenge is ensuring that every order is accurate before it moves forward. Teams must validate data, check for missing information and manually enter or correct details across CRM and ERP systems. Errors at this stage can lead to delays, rework and customer dissatisfaction.

This manual effort slows down fulfillment and introduces risk at scale.

With the Content Innovation Cloud, organizations use IDP to capture and structure order data, then connect workflows across CRM and ERP systems to automate validation and routing.

Customer Example:
Retail and manufacturing organizations use IDP to ensure orders are complete and accurate before entering fulfillment workflows, reducing rework and improving delivery timelines.

Instead of manually checking each order, teams rely on consistent, automated validation that improves speed and accuracy from the start.

Additional Sales and Operations Use Case Examples:

Sales order processing for faster, higher-quality validation and routing
Quote-to-cash acceleration across proposals, contracts and approvals
Document routing across CRM and ERP systems
Order validation before fulfillment to reduce rework and improve customer satisfaction

Legal and Compliance

Making Contracts Easier to Understand and Act On

Legal and compliance teams are responsible for reviewing contracts and ensuring obligations are understood and met. The challenge is that critical information is buried within lengthy, complex documents.

Finding specific clauses, obligations or risks requires manual review and deep expertise. This process is time-consuming and difficult to scale, especially as contract volumes increase.

Organizations often rely on a small number of experienced employees who understand where to look and how to interpret language, creating bottlenecks and risk if that knowledge is not accessible.

With the Content Innovation Cloud, organizations use Knowledge Discovery to enable AI agents that allow teams to query contracts and retrieve key information instantly, with answers grounded in source documents.

Customer Example:
A U.S. regional water authority used Knowledge Discovery to surface insights from contracts and engineering documents, reducing reliance on long-tenured staff and accelerating decision-making.

This allows teams to respond faster, improve visibility into obligations and reduce risk.

Hyland Example:
Hyland is actively building an Enterprise Context Engine use case designed to extract and connect structured and unstructured data across more than 20 years of contracts, amendments and customer records. The goal is to create a unified knowledge graph across contract and customer data to help teams surface obligations, risks and relationship insights faster, reducing reliance on manual legal review.

Additional Legal and Compliance Use Case Examples:

Contract intelligence to retrieve key terms, clauses, obligations and risks
Regulatory compliance monitoring to identify obligations across documents
Audit preparation and document retrieval
Risk identification across contracts and supporting documentation

IT and Operations

Improving How Work Gets Prioritized and Delivered

IT and operations teams manage a wide range of requests, tickets and internal workflows. Many of these requests include unstructured descriptions that require interpretation before action can be taken.

The challenge is understanding what the request actually requires. Teams must read through descriptions, determine urgency, assess complexity and decide how to prioritize work. This process is manual, inconsistent and time-consuming.

Without clear context, prioritization decisions can vary, leading to delays and inefficient use of resources.

With the Content Innovation Cloud, organizations use Agent Builder to create AI agents that analyze request data, extract context and support prioritization.

Hyland Example:
Hyland uses Agent Builder to evaluate incoming requests and intelligently assess urgency and complexity based on the content of request descriptions. By providing earlier visibility into effort and priority, teams can respond faster, improve prioritization and allocate resources more effectively.

Additional IT and Operations Use Case Examples:

Automated request triage to classify and route incoming tickets
Workflow automation to trigger actions based on request content
Operational insights to identify trends and recurring issues
Process optimization to reduce manual effort across internal workflows

Knowledge Workers

Reducing Time Spent Searching for Information

Across every department, employees spend significant time searching for information they know exists but cannot easily find.

Content is stored across documents, emails and systems that are not connected. Even when information is available, it may not be easy to verify or trust, especially when multiple versions exist.

The challenge is not a lack of information. It is a lack of access, context and confidence in what is being used.

With the Content Innovation Cloud, organizations use Knowledge Discovery to connect content across systems and enable AI agents that provide accurate, contextual answers.

Customer Example:
A global engineering organization improved document retrieval by up to 70 percent by unifying access to technical documents and contracts.

Employees spend less time searching and more time acting on information, improving both productivity and decision-making.

Additional Knowledge Worker Use Case Examples:

Institutional knowledge assistant to surface information scattered across repositories
Policies and procedures agent to help employees query SOPs and complete tasks accurately
Research and analysis to quickly surface relevant documents and insights
Decision support to provide context for faster, more confident decisions
Knowledge retention to preserve institutional knowledge as employees transition roles

Bringing It All Together

Across these examples, a clear pattern emerges.

Organizations are not struggling because they lack AI. They are struggling because the content behind their processes is fragmented, unstructured and difficult to use at scale.

Invoices, contracts, employee records, service documentation and operational data all exist, but they are disconnected from the workflows that depend on them. As a result, teams rely on manual effort, decisions take longer and AI initiatives remain limited to isolated use cases.

The opportunity is not to replace what already exists. It is to make that content usable, connected and actionable.

The Content Innovation Cloud does this by transforming unstructured content into AI-ready information, grounding AI agents in governed enterprise data and activating that intelligence within real business workflows.

This is what allows organizations to move from isolated improvements to consistent, repeatable outcomes across the business.

The Bottom Line

AI will not transform the enterprise on its own.

Content will.

When content becomes the foundation for how work is orchestrated across systems, AI agents can operate with context, decisions can be made with confidence and organizations can move faster without sacrificing control.

That is how organizations move beyond pilots.

That is how they deliver measurable, business-level outcomes.

And that is how they unlock the full potential of the Content Innovation Cloud.