Latest News

OVERVIEW

Enterprise automation was once focused on eliminating repetitive tasks. Scripts, macros and robotic process automation (RPA) helped organizations streamline workflows and reduce manual effort across departments. But as digital environments get more complex, automating tasks alone is no longer enough.

Now, enterprise leaders want to know: How can we intelligently automate decisions, not just processes?

Interest in AI agents is rising as organizations explore systems that can go above and beyond to interpret context, analyze enterprise knowledge and take next-step actions within defined guardrails. According to a recent Forrester Consulting study, 45% of decision-makers say their organizations already use AI agents, while 25% are piloting them.

Let’s explore how enterprises are shifting from task automation to agentic-decision systems, how to identify the right workflows for AI agents and why governance and enterprise data are critical for scaling intelligent automation.

H2: Task automation vs. decision automation

Traditional automation technologies (RPA, scripts and macros) can execute predictable tasks with structured workflows and clear rules, such as:

• Routing documents through established workflows

• Transferring data between systems

• Creating reports or alerts

• Triggering approvals in business processes

However, the reality is that many enterprise workflows involve contextual decision-making and exception handling, requiring systems to interpret information, evaluate conditions and determine the next course of action.

Moreover, lean IT headcounts mean that the hands-on work that makes automation effective — configuring decision logic, handling exceptions, validating outputs — gets deprioritized, and automation stalls before it delivers any real value.

This is why decision automation plays a vital role. Context-aware AI agents go beyond mere execution to support complex workflows and enhance decision-making, with Forrester noting that 76% of decision-makers believe context unlocks the true power of AI agents. By deeply analyzing documents alongside associated tasks and workflows, organizations can enable intelligent decision-making to better meet their business needs.

H2: Identifying the best workflows for agentic automation

Not every business process needs autonomous decision systems. The most effective agentic automation opportunities often share one key characteristic: large volumes of unstructured information. In practice, this includes enterprise data such as documents, emails, PDFs, reports, internal messages and more.

The Forrester study found that 73% of enterprise data is unstructured or semistructured. This data is a critical input for AI agents to interpret content, extract key business insights and connect information across systems.

By transforming raw data into actionable insights, AI agents enable organizations to identify patterns, surface risks and trigger next-step actions. This transforms previously inaccessible information into decision-ready intelligence that supports complex workflows.

As a result, workflows that depend on interpreting high volumes of content are especially well suited for agentic automation. Examples of strong agentic automation use cases include:

• Extracting insights from unstructured content such as documents, emails and chat logs

• Monitoring enterprise data to identify risks, trends or operational issues

• Supporting decision-making by connecting information across systems and workflows

These use cases highlight how AI agents move beyond task execution to deliver context-aware insights and actions across the enterprise.

H2: Empowering the CISO: supervised autonomy and risk thresholds

As more organizations adopt AI-driven decision systems, governance becomes increasingly critical. However, many enterprises still lack a strong foundation — an estimated 41% report ad hoc or inconsistent governance practices.

To mitigate this, organizations are increasingly relying on supervised autonomy, which:

• Enables AI agents to make decisions within defined confidence thresholds

• Routes higher-risk decisions to human experts

• Amplifies security, compliance and transparency

This approach allows organizations to scale automation while maintaining credibility and accountability. These safeguards make it possible for CIOs and CISOs to experiment with agentic automation without creating risk.

H2: Real-world agentic systems in action

Agentic decision systems already have quite a presence in industries where workflows depend on enterprise knowledge.

Many of these use cases build on intelligent document processing and extending automation with AI-driven decision-making.

For example:

• Finance teams use AI workflows to process invoices and financial documents — extracting data, validating inputs and routing exceptions for review.

• Healthcare and insurance organizations apply AI agents to analyze claims, verify information and accelerate case processing, or to escalate complex cases.

• Customer service teams use it to classify requests, extract key details and route cases to the right teams, improving response speed and consistency.

• HR and operations teams automate document-heavy processes by identifying missing information and keeping approval processes moving forward.

In more advanced scenarios, multiple AI agents can also work together across systems to complete entire workflows, accelerating delivery lifecycles with speed and precision.

H2: Preparing your enterprise for the agentic future

The shift from task automation to agentic decision systems marks an important milestone in the realm of enterprise technology.

However, success depends on getting the fundamentals right.

To scale agentic automation, organizations need:

• Connected enterprise data, not siloed content

• Clear governance frameworks to manage risk and compliance

• AI-ready foundations that support automation and decision-making

• Equally important, successful adoption requires a strategic partner capable of connecting and leveraging enterprise content, systems and industry context. Many organizations rely on external expertise to accelerate implementation and scale AI initiatives effectively.

The question is no longer whether to use AI agents, but how quickly organizations can build the foundation to scale them effectively.

To explore how organizations are approaching this transformation, read the full Forrester Consulting report. Learn how Hyland helps organizations in their intelligent automation journey — enabling teams to scale innovation with confidence.