Leonard shares insights on AI, unstructured data, and the future of innovation through smarter content solutions
Hi Leonard, welcome to AITP. Could you provide our readers with a brief overview of your professional journey and the factors that led you to become the Chief Product Officer at Hyland.
I’ve spent my career leading product innovation and technology transformation across industries including Human Capital Management, Financial Services, and Enterprise Software. Prior to joining Hyland, I led Human Capital Management solutions at ADP, where I focused on delivering next-generation, cloud-native experiences for over one million customers worldwide. Throughout my career, I’ve gravitated toward opportunities that sit at the intersection of technology, data, and customer experience—whether modernizing global payroll systems, reimagining enterprise platforms, or unlocking new revenue streams through digital transformation. Over time, I realized my passion lies in building products that solve deeply complex problems at scale, which ultimately led me to the role of Chief Product Officer, where I can bring together strategy, technology, and innovation to drive meaningful impact.
What drew me to Hyland was the immense opportunity to shape the future of digital transformation through unstructured and semi-structured data, which remains one of the most underutilized assets in enterprise technology. Healthcare, financial services, government—these industries generate vast amounts of untapped content trapped in documents, records, and workflows. With the Content Innovation Cloud, we’re creating a platform that doesn’t just manage this information but actively curates, connects, and transforms it into actionable insights through AI-powered automation, interoperability, and real-time intelligence. It’s incredibly exciting to help organizations break down silos, drive smarter decisions, and deliver better outcomes through the next generation of content solutions.
Why do organizations fail to effectively leverage unstructured data, despite its potential to drive insights, and how much of a challenge are data silos in hindering the application of AI in business decision-making?
Unstructured data remains one of the greatest untapped resources in most organizations not because leaders don’t recognize its value, but because of the inherent complexity in making it usable. Unlike structured data that lives neatly in rows and columns, unstructured content such as documents, emails, images, clinical notes, and contracts, is messy and often scattered across disconnected systems. The real challenge lies in capturing, classifying, and contextualizing this data at scale and in real time, in a way that can fuel intelligent business processes.
Data silos make this exponentially harder. When critical information is locked within departmental systems or legacy platforms, it prevents organizations from creating a complete picture of their operations, customers, or patients. For AI to drive meaningful insights, it requires access to holistic, high-quality, and contextually relevant data. This is why interoperability and intelligent automation are so essential today. Breaking down these silos and turning unstructured data into connected, enriched information is what unlocks the full potential of AI. It’s not just a technical problem; it’s a strategic imperative for organizations that want to compete and innovate in an increasingly data-driven world.
What role does scalable cloud infrastructure play in unlocking the full potential of unstructured data in AI systems?
Scalable cloud infrastructure is a critical enabler for organizations looking to unlock the full potential of unstructured data in AI systems. Cloud infrastructure provides the scalability, flexibility, and security required to manage the large amounts of computing power required for unstructured data to be leveraged in AI systems. Cloud-based environments also provide a unified platform for real-time data ingestion, processing, and enrichment, ensuring that AI models can work with comprehensive and high-quality datasets.
How does cloud-based data storage and processing facilitate the seamless integration of disparate data sources for AI-driven insights?
Organizations using the corpus of their information has always been a challenge, but AI in the cloud is bringing the opportunity to bridge information silos so end-users can access the information they need quickly. Cloud-based platforms offer a unified environment to centralize and standardize this diverse data, effectively breaking down these silos. By leveraging cloud infrastructure, organizations can seamlessly aggregate content from multiple sources, ensuring that AI models have consistent and real-time access to a holistic dataset. This centralized approach not only enhances data accessibility but also streamlines workflows, as cloud solutions can integrate with existing applications without extensive custom coding.
How does a strong data governance strategy improve AI’s ability to make accurate and actionable predictions, and how do you see AI systems evolving as organizations prioritize well-governed data?
A robust data governance strategy enhances AI’s ability to generate accurate and actionable predictions. AI systems have the potential for improved decision-making, enhanced compliance and risk management, and increased trust and adoption among organizations with well-governed data. A strong data governance strategy enhances the performance of AI systems while laying the foundation for responsible and strategic AI evolution.
What is the role of data curation in improving AI performance, and how can organizations start realizing immediate benefits from AI once they address data challenges like silos and inconsistent quality?
Data curation is essential in optimizing AI performance because it ensures that data is not just accessible, but also clean, relevant, and properly structured for AI applications. High-quality, curated data allows AI models to generate more accurate and actionable insights, reducing bias and improving decision-making. To start realizing immediate benefits from AI, organizations need to address data challenges by focusing on use cases that deliver quick wins. These could be automating document processing, enhancing searchability, or improving customer interactions with intelligent recommendations. By integrating AI-ready data into business workflows, organizations can see faster time-to-value and build momentum for broader AI adoption.
What is the measure of the success of AI initiatives in terms of ROI after implementing scalable infrastructure and strong data governance?
The success of AI initiatives should be measured through a combination of tangible business outcomes and efficiency gains. ROI isn’t just about cost savings, it’s about AI’s ability to drive operational improvements, accelerate decision-making, and uncover new revenue opportunities. With scalable infrastructure and strong data governance in place, key success metrics should include increased process automation, faster insights generation, reduced compliance risks, and improved customer experiences. Organizations should also evaluate AI performance in terms of accuracy and relevance of insights, as well as adoption rates across teams. The true measure of success is when AI moves from being an experimental initiative to an embedded, value-generating component of business operations.
What key trends in data management and AI technology will have the greatest impact on businesses in the upcoming year?
I’m closely watching a few key technology trends that are poised to have a significant impact on businesses: agentic AI and AI-augmented data ecosystems. agentic AI, which takes artificial intelligence to the next level by enabling systems to act independently. Unlike traditional AI, these intelligent agents can gather information, evaluate situations, and make decisions on their own. This capability has enormous potential for streamlining complex processes, such as supply chain management, predictive maintenance, and personalized customer interactions.
Another game-changer is the rise of AI-augmented data ecosystems, which are transforming how organizations manage and extract value from their data. By leveraging adaptive data fabrics, businesses can seamlessly connect, analyze, and utilize structured and unstructured data in real time, unlocking insights that drive smarter decisions and new opportunities.
With your extensive experience in driving product innovation across industries, how do you see data governance and effective data curation impacting product development and innovation at Hyland?
At Hyland, data governance and effective data curation are seen as foundational to product innovation. AI-driven solutions are only as powerful as the data they rely on, and ensuring that data is well-governed, high-quality, and accessible allows us to build smarter, more intuitive products. Strong data governance enables us to develop AI solutions that are not only accurate but also secure and compliant, which is critical as organizations navigate evolving regulations and industry standards. Additionally, by leveraging curated data, we can enhance automation, improve decision intelligence, and create seamless user experiences within our content services and AI-powered solutions. As AI continues to evolve, our focus remains on enabling organizations to harness their unstructured data more effectively, turning fragmented information into actionable insights that drive real business impact.
What advice would you offer to organizational leaders facing data challenges in their AI adoption?
I’d advise organizational leaders to prioritize breaking down data silos, improving data quality and focusing on data governance and security when trying to overcome common data challenges in AI adoption. AI requires well-structured and high-quality data to generate the most useful outputs, meaning organizations should invest in tools that can help standardize data and prepare it for AI applications. Data silos remain a huge barrier for many organizations in making the most of their AI efforts. Leaders should prioritize unifying structured and unstructured data to ensure that fragmented data across multiple sources isn’t an issue blocking AI adoption. Finally, data governance and security should remain top of mind. Organizations must establish data governance policies that focus on ownership, compliance, and security to ensure responsible AI use from start to finish.
Leonard Kim
Chief Product Officer at Hyland
Leonard Kim is the Executive Vice President and Chief Product Officer at Hyland, the leading provider of unified content, process and application intelligence solutions and the pioneer of the Content Innovation Cloud™. Kim has 20+ years of product development leadership experience across several industries and previously held leadership roles at ADP, Sage Group, DIRECTV, and NBC Universal. At Hyland, Kim leads the global product management organization, driving innovation to enhance the company’s position as a market leader.
About Hyland: Hyland empowers organizations with unified content, process and application intelligence solutions, unlocking profound insights that fuel innovations. Trusted by thousands of organizations worldwide, including more than half of the Fortune 100, Hyland’s solutions fundamentally redefine how teams operate and engage with those they serve.