Deploying and scaling automation with agentic AI

• Agentic AI is an artificial intelligence system designed to autonomously plan, reason and execute complex workflows within defined business boundaries.

• Agentic AI surpasses legacy automation by autonomously planning, reasoning and executing complex workflows within defined business boundaries.

• Connecting structured data with unstructured content provides AI agents with the enterprise context needed to deliver actionable insights.

• Embedding AI agents into existing processes empowers employees to step away from repetitive data work and focus on strategic decision-making.

• Hyland Content Innovation Cloud™ offers a secure, unified foundation to build, orchestrate and deploy tailored AI agents across your organization.

Despite heavy investment in artificial intelligence, many initiatives fall short of expectations. According to a recent report by Forrester Consulting, while 45% of decision-makers say their organization is using AI agents, most deployments remain small and isolated. Organizations often lack the cohesive foundation that allows AI agents to understand enterprise context. Fragmented operational processes, unstructured content and disconnected systems prevent these agents from recognizing how documents, workflows and decisions relate to one another.

Agentic AI promises smarter decisions, faster innovation and more personalized experiences by interpreting and acting on the context embedded in enterprise content. However, less than one-third of organizations report strong capabilities in essential areas like governance, integration and context awareness.

This guide outlines how modern enterprises must rethink how people, processes and information work together. By bridging the readiness gap for unstructured data, reimagining work coordination and leveraging a unified foundation, organizations can deploy and scale agentic AI safely and effectively.

The move toward agentic AI is not simply a technical upgrade. It is an organizational transformation. Early automation and even AI efforts often prioritized standalone models or individual use cases. These tools assisted with simplistic tasks, like predictable workflows or summarizing content, but they remained separate from end-to-end governed business processes and lacked orchestration capabilities.

Beyond automation 1.0

Legacy automation technology struggles to meet the demands of today’s rapidly shifting market and enterprise needs. These older systems often lack 1) The flexibility to adapt to increasingly complex workflows and, 2) The scalability required to handle growing volumes of unstructured data. Additionally, they tend to operate in silos, limiting integration with modern tools and creating inefficiencies in cross-departmental processes.

With the advent of agentic AI and data-driven decision-making, businesses need solutions that not only automate tasks but also provide intelligent insights, enable seamless collaboration and ensure regulatory compliance. Legacy automation simply cannot deliver on these fronts, leaving organizations at risk of falling behind more agile competitors with advanced, AI-enabled platforms.

Next up: Agentic AI

Modern enterprises must challenge this status quo. Consider a work environment where AI agents autonomously plan, reason and execute complex tasks in dynamic environments. In this setting, intelligent systems do not merely generate text. They coordinate tasks, identify dependencies and initiate actions within defined boundaries. This fundamentally redefines how work gets done.

Agentic AI systems operate inside essential business processes. They expand capability and risk simultaneously, which means they require robust governance and bounded autonomy. Organizations must build structures that ensure responsible operation, allowing these agents to act as reliable participants in daily workflows rather than peripheral novelties.

Organizations face significant foundational challenges when adopting AI. A recent Harvard Business Review Analytic Services survey found that 94% of respondents believe having well-connected data, processes and applications is highly important to successful AI adoption. However, only 27% say these elements are currently well connected at their organization.

"The real power is marrying the unstructured data with the relevant structured data sets in your ERP, CRM, HCM or favorite system of record,” said Hyland CEO Jitesh S. Ghai. “There's so much that has not been gleaned and tapped out of this vast, enormous amount."

While structured data is often prepared for AI use, unstructured data lags far behind. Forrester reports that 73% of enterprise data is unstructured or semistructured, representing a largely untapped source of insights. This includes text, images, video, emails and PDFs. When this information is trapped in silos or managed inconsistently, even the most advanced AI models lack the context needed to deliver meaningful results.

Addressing data silos, format issues, security concerns and governance is crucial for scaling agentic AI. Organizations must transform unorganized information into AI-ready assets. Without a cohesive foundation, AI models cannot understand the enterprise state — the evolving condition of documents, cases, workflows and decisions. Ensuring that unstructured data is accessible, compliant and contextualized is the first step toward building a trustworthy agentic architecture.

The real gains from agentic AI come from reimagining how knowledge is synthesized and applied across the enterprise. This represents a shift from simple task acceleration to a fundamental restructuring of work coordination.

Content Innovation Cloud includes agentic automation tools that enable organizations to lower operational costs, reduce errors and improve productivity by removing repetitive data work. When AI agents handle structured, repetitive tasks, organizations experience fewer errors and reduced risk. They benefit from improved information governance, security and auditability. For example, Baptist Health saved staff 68 days per year, and the Virginia Community College System drastically improved its information accuracy by automating heavy data-entry processes.

Embracing human-AI collaboration

Agents act like knowledge workers within governed frameworks. They can retrieve documents, administrate referrals, surface exceptions and escalate tasks to human decision-makers. This allows employees to focus on higher-value, strategic decision-making. The goal is to maximize the total value of the human-machine partnership, empowering professionals to frame problems, interpret edge cases and exercise complex judgment.

Scaling automation using agentic AI requires a deep understanding of how these systems operate. Autonomous agents use a sense-plan-act cycle to execute complex workflows. The agent:

1. Perceives its environment by gathering data.

2. Formulates a strategy using its reasoning layer to analyze the information.

3. Carries out the strategy using integrated tools.

4. Reflects on the outcome and adapts its future plans.

Trending upwards: Enterprise AI agents at work

Agentic automation accelerates business cycles, reduces manual touches and delivers measurable return on investment. Gartner reports a 1,445% surge in client inquiries about multiagent systems that collaborate to complete complex workflows. Forward-looking organizations are prioritizing this adaptive process orchestration to maintain a competitive advantage.

As Hyland customers pioneer their use of enterprise AI agents, we’ve been collecting their stories. Here are some ways five anonymous Hyland customers are winning with their agents:

A fashion brand used Hyland IDP™ with AI-powered OCR and agentic automation to automate invoice extraction, validation and exception-handling to reduce manual intervention.

A leading U.S. printing company used Hyland Agent Builder™ to handle validation, matching and exceptions, as well as standardize invoice processing to accelerate turnaround time and reduce manual effort.

A national credit guarantee institution used agent-driven workflows to replace manual decision-making and improve consistency.

A large commercial insurer used Agent Builder and Hyland Knowledge Discovery™ to detect duplicates, flag anomalies and validate policy coverage—reducing manual processing time by 60% and lowering exception rates.

A global aerospace manufacturer enabled agent-driven workflows to automate and trigger maintenance and training actions, which reduced manual efforts and cut process latency by 40%.

While offering immense opportunities for improving business processes and decision-making, any AI system also presents unique risks and challenges to address. Agentic AI, specifically, operates with a higher degree of autonomy and decision-making capabilities that previous AI systems. While this autonomy can enhance efficiency, it also introduces certain complexities.

Challenges

One significant risk lies in unintended behaviors. When AI systems are designed to operate independently, they may interpret goals or data in ways that lead to unforeseen consequences. For example, misaligned objectives can result in AI agents pursuing actions that don’t align to a company’s broader business strategy or ethical commitments.

Another pressing challenge is ensuring transparency and accountability. The complexity of agentic AI algorithms can make it difficult to fully explain the reasoning behind an AI’s decisions. This "black box" effect could reduce stakeholder trust and complicate compliance with regulatory standards that require clear audit trails.

Security is another critical concern. Agentic AI systems often gather and process a vast amount of sensitive data, making them attractive targets for cyberattacks. A breach could not only expose confidential information but also compromise the AI’s decision-making integrity, leading to potential operational failures.

Operational challenges also arise regarding scalability and integration. Deploying agentic AI often requires significant resources to synchronize with existing systems and workflows. The ongoing monitoring and adjustment of such systems can strain IT and administrative teams.

Finally, ethical considerations must be addressed. Ensuring agentic AI operates responsibly — without perpetuating existing biases or making unethical decisions — is key to fostering legitimate and fair outcomes.

By acknowledging and addressing these risks early, businesses can harness the potential of agentic AI while minimizing disruptions and ensuring trustworthy outcomes for their organizations and stakeholders.

To bridge content silos and ensure governed access, organizations require a robust content foundation. The Content Innovation Cloud, Hyland’s AI-native platform, provides a uniquely qualified platform for deploying and scaling agentic AI. At a glance, it combines a host of Hyland AI tools to allow you to:

• Connect multiple content repositories in one place.

• Provide a secure, contextual view across the enterprise.

• Unlock the intelligence and context living in your content.

• Deploy use-case-specific enterprise agents and agentic AI solutions that leverage your content foundation.

• Enable the execution of intelligent processes across your entire organization.

Hyland powers the agentic enterprise:

Hyland’s AI suite of tools incorporates several technologies that link content, processes, people and applications. This gives your AI agents the enterprise context they need to operate securely and accurately:

Organizations must move beyond generic AI experiments to deliver agentic capabilities that act on governed enterprise content in the flow of real work. Practical deployment strategies are essential for driving immediate value without disrupting daily operations.

Hyland provides the core tools for designing, deploying and orchestrating enterprise-grade AI agents. Using Agent Builder, organizations can create specialized agents tailored to specific domains, such as a claims research assistant for insurance or a transcript insight advisor for higher education. Hyland IDP features large language model capabilities that eliminate the need for extensive machine learning training, streamlining document capture and data extraction. Automate ties these elements together, orchestrating workflows across the organization from simple tasks to end-to-end business processes.

Key point: AI agent assimilation into workflows

To succeed, leaders should embed these intelligent agents directly into existing workflows. Employees should not have to context-switch or learn completely new interfaces. By grounding AI agents in a strong information architecture and integrating them via REST APIs and prebuilt connectors, organizations can shift from assistants that merely suggest to agents that securely plan, decide and act across complex processes.

The return on investment (ROI) of agentic AI can be substantial. By automating repetitive tasks and enabling faster, data-driven decision-making, agentic AI provides direct cost savings and efficiency gains. For instance, organizations often experience reduced operational costs due to fewer human errors and faster processing times in key workflows like claims processing, invoice management or customer inquiries.

Additionally, agentic AI unlocks opportunities for innovation by allowing employees to focus on strategic initiatives rather than manual, time-consuming tasks. This leads to measurable productivity improvements and higher workforce satisfaction, which positively impacts organizational growth. These agents also enhance data accuracy and compliance adherence by ensuring processes align with regulatory standards, reducing costly penalties.

Beyond cost reduction, agentic AI contributes to revenue growth by improving customer experiences. Intelligent agents engage with customers more efficiently and accurately, leading to faster resolution times, higher retention rates and ultimately stronger revenue streams. With scalable implementation through REST APIs and prebuilt integrations, organizations can see significant ROI within months. When the benefits of increased efficiency, reduced costs and enhanced customer satisfaction are combined, agentic AI becomes not just a tool but a strategic investment that delivers long-term value.

The agentic enterprise is here. When grounded in a structured information architecture, governed by clear accountability and embedded in operational workflows, AI agents will augment human capability and elevate workforce productivity.

With the full power of AI and a unified content strategy, you have the keys to drive transformational outcomes. Scalable agentic automation allows you to add capacity, expand into new markets and accelerate decision-making without increasing overhead. You can transform unstructured content into connected pipelines that fuel innovation and growth. 

Do not let the noise paralyze you. Get started with Hyland.