How Can Enterprises Turn Fragmented Data into Strategic AI Advantage?

Podcast: Tech Transformed podcast Guest: John Newton, Chief Innovation Strategist at Hyland Host: Dana Gardner, President and Principal Analyst at Interabor Solutions Enterprise leaders rushing to integrate artificial intelligence (AI) into their operations often think the biggest challenge is the technology itself. In reality, the issue is much closer to home. It’s in the piles of unstructured enterprise data spread across documents, systems, and repositories. In the recent episode of the Tech Transformed podcast, John Newton, Chief Innovation Strategist at Hyland, sits down with host Dana Gardner, President and Principal Analyst at Interabor Solutions. They discussed how enterprises can unlock the full value of enterprise AI by addressing fragmented information and building stronger governance frameworks. Their conversation highlights that unstructured data is not an obstacle; it is the foundation for next-generation AI-driven productivity. As Newton stated, “The opportunity to truly use AI and use it effectively in your organisation really depends on that unstructured information.” For companies looking to adopt AI on a large scale, the real work is in organising and contextualising their internal knowledge. Is Unstructured Data the Hidden Fuel for Enterprise AI? Most enterprise data does not sit neatly in structured databases. Instead, it exists in contracts, reports, emails, videos, policies, and operational documents, creating a vast amount of unstructured content. The enormous amount of such unstructured data ends up creating a challenge for AI projects that rely solely on foundation models. Large language models (LLMs) may be trained on public data, but they cannot inherently access proprietary business intelligence. Newton argued that enterprise AI must therefore be built around internal knowledge systems. “Foundation models can’t train on your internal information,” he explained. “What you really want is that information to be part of the AI when you’re answering questions, doing research, or executing business processes.” This change requires organisations to rethink how information flows across the enterprise. Instead of isolated systems—CRM platforms, ERP databases, content repositories—companies need an interconnected information structure that connects multiple sources in real time. Such a structure enables AI systems and AI agents to find the right data at the right time. This also improves decision-making, automation, and operational intelligence. Key Takeaways Unstructured data is the foundation for effective enterprise AI. Data curation improves AI accuracy and reduces information noise. Connecting enterprise systems enables AI to deliver real-time insights. AI guardrails help manage security, compliance, and data governance. AI automation boosts employee productivity by reducing repetitive work. Chapters 00:00 Unlocking AI's Potential with Unstructured Data 05:20 Signal to Noise: The Clarity Challenge 11:21 Guardrails for AI: Balancing Control and Flexibility 14:41 Harnessing the Enterprise Context Engine 17:48 Real-World Applications: Case Studies in AI 20:37 Curation: The Key to Effective Automation 22:21 Future Business Value: Productivity and Beyond For more information, please visit hyland.com To stay updated on B2B Tech front and centre, follow EM360Tech: YouTube: @enterprisemanagement360 LinkedIn: @EM360Tech X: @EM360Tech Follow Hyland on all its major platforms: YouTube: @HylandAI LinkedIn: Hyland X: @Hyland #UnstructuredData #EnterpriseAI #DataCuration #AIGuardrails #LLMs #AIAutomation #FragmentedData #InformationManagement #SignalToNoise #EnterpriseContext #TechTransformedPodcast #Hyland #B2BTech