ITTech Pulse Exclusive Interview with Michael Campell, Chief Product Officer, Hyland

Michael Campbell, Chief Product Officer at Hyland, chats about leading AI-driven content innovation, cloud strategy, and agentic automation for regulated enterprises.

Hi Michael, what inspired you to be CPO at Hyland, and how has the company evolved since its inception?

At my core, I’m a builder. My career has been about shaping enterprise platforms that combine cloud, data and AI in ways that move the needle for customers. The chance to do that on with an incredible platform such as the Content Innovation Cloud is what attracted me to Hyland. Here, we have decades of experience in helping organisations make the most of their content, and in today’s innovation landscape, that couldn’t be more important. Hyland has evolved from traditional document management into unified content, process and application intelligence. And now we’re in the next chapter: using agentic innovations to help organisations extract far more value from their content and data while we collectively redefine what content management means in an AI-first world.

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How does Hyland balance innovation with regulatory compliance, especially for industries like healthcare and financial services?

For us, innovation and compliance aren’t in tension — they’re interdependent and each strengthens the other. Hyland has spent decades serving customers in highly regulated industries, so security, privacy and governance are engrained into our platform. We’re building on a long track record of successfully helping a myriad of sectors manage sensitive content responsibly, with strong controls on how information is accessed, retained and monitored.

As we continue to advance our agentic capabilities, including the Enterprise Context Engine and Enterprise Agent Mesh, we apply that same ‘trust first’ mindset. Customers decide which systems and datasets are in scope, what agents can control, and when and where a human stays in the loop. Our ultimate goal is to help organisations leverage their existing content and strengthen their workflows without forcing them to rebuild their compliance posture.

Can you elaborate on how the Enterprise Agent Mesh orchestrates AI agents across multiple platforms without disrupting existing workflows?

The Enterprise Agent Mesh works with the Enterprise Context Engine, which creates a living record of what’s happening across organisations by connecting content, processes, people and applications — from ERP and CRM systems, to EHRs and line-of-business tools. Using that foundation, the Enterprise Agent Mesh is a network of specialised agents, tuned to real industry workflows in healthcare, financial services, government and beyond.

This allows our customers to define and deploy agentic automation that can be invoked from existing workflows. Our Content Innovation Cloud platform also supports external agents that may be needing data or context from our systems.

Crucially, we don’t ask customers to ‘rip and replace’ what they’ve already built. The Content Innovation Cloud as a platform is designed to leverage the systems people are already using day-to-day and extends the value of the investment they have made in the past. This means organisations can incrementally layer enterprise agents over existing workflows, with clear guardrails on what agents can do autonomously. Implementing AI shouldn’t force enterprises to rebuild themselves but should instead unlock incremental value from their current environments.

How do you see agentic AI changing the role of human employees in enterprise workflows?

Agentic AI shifts us from tools that only respond to questions, to automated workflows that understand context, anticipate work and coordinate activity across systems. In practical terms, that means more of the repetitive, cumbersome tasks that require reading and interpreting documents for completeness or correctness can be handled by enterprise agents. And as enterprises mature in their use of agentic automation, enterprise agents will start acting as an advisor that raises the decision intelligence within a company. Meanwhile, employees can focus on high-value work that requires judgment, empathy and creativity.

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Importantly, we want to empower teams to do more, not replace them. The Content Innovation Cloud scales institutional knowledge, so employees get richer context and smarter recommendations at the moment of decision. Eventually, I expect most roles to become more strategic and more customer centric as enterprise agents take care of the busywork, leaving humans to spend more time solving complex problems and engaging directly with their customers.

Can you give an example of measurable business impact from Knowledge Enrichment?

The biggest benefit of Hyland’s Knowledge Enrichment is that it structures data and makes it AI-ready from the start. Enterprises have huge amounts of valuable, unstructured data across multiple repositories, meaning content like emails, documents, or images that lack a predefined format. This content is not generally in a cohesive format to inform AI models and to make an impact. Individual documents could be fed to AI every time an answer is needed, but then contextual information across documents would be missing. This also means that security permissions from the original source and governance policies for access are also missing when fed directly to models.

With Knowledge Enrichment, we curate this data across repositories, and store it in an AI-ready form, including the relevant security and governance policies for access by users through AI tools. This allows businesses to finally start leveraging their data, rather than being burdened by it. For an industry like healthcare, for example, this saves employees from hours of time-consuming, repetitive work. Healthcare providers are constantly grappling with unstructured data in the form of hand-written notes, test results, and medical imaging. However, instead of having to manually sort through this information, Knowledge Enrichment automatically extracts key details and makes this critical patient data easy to access and readily available for agentic orchestration.

Can you share other real-world examples where Hyland boosted efficiency or customer experience?

A great example is Liberty Mutual Insurance. They set out to replace their outdated, siloed technology and build a less paper-intensive business that could scale more efficiently. The team also needed a cloud-based solution to ensure consistency across their global departments.

Hyland’s Alfresco solution was the only technology that met their needs. By integrating seamlessly into the company’s insurance applications, they were able to roll the solution out across multiple offices and product lines quickly, automating everything from document retrieval to records management, while also allowing employees to continue to use the same front-end applications. What’s more, they expect to save more than $20 million over five years due to a reduction in paper, printing, and storage costs, and they predict they will soon manage 300 million documents in Alfresco.

These improvements don’t just help Liberty Mutual’s employees but also result in direct benefits for their customers. Faster processes in the back end save employees time and allows them to focus on higher-value work, ultimately resulting in faster claims processing for their customers.

What emerging trends in AI and enterprise content management do you see shaping Hyland’s roadmap in the next 2–3 years?

We will continue to see expanding development and deployment of agentic AI; and we’re already realising the potential of what this technology is capable of when implemented properly. The need for context-aware, enterprise-grade AI agents to drive true adoption and real outcomes will only grow.

It’s important to remember that content management and AI are intrinsically linked. For any type of AI model to operate effectively, it needs AI-ready data. The more context and information you can give your AI, the better the outcome; and that becomes essential when you’re talking about enterprise agents that act autonomously.

We are at an exciting inflection point for enterprise AI innovation, but that also means it’s easy to get distracted by all the flashy potential of AI. Before organisations even think of introducing AI into their workflows, they need to make sure they have the right foundations in place to prevent more work in the future. And that all starts with content management.

Thank you, Mr. Michael, for taking the time to share your insights with us.

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