What if the most valuable AI asset your organization already owns is sitting untouched inside years of video, audio, and unstructured content?
In this episode, I sit down with Sean King from Veritone to explore how organizations are transforming massive archives of content into searchable, licensable, and revenue-generating assets for the AI economy. As the executive leading Veritone’s commercial business, Sean works directly with major organizations, including the NCAA and ESPN, to help unlock value hidden inside decades of multimedia data.
We discuss why the next phase of AI will be defined by multimodal data rather than text alone, and why businesses are dramatically underestimating the value locked inside their video, audio, and image archives. Sean explains how AI is turning what was once treated as a passive storage problem into an active business asset, making unstructured content searchable, contextual, and commercially valuable at scale.
The conversation also looks at how organizations can modernize decades-old archives without becoming overwhelmed by the sheer volume of data involved. Sean shares how companies can approach AI transformation by first building scalable workflows for incoming content before tackling historical archives. From sports media and broadcasting to enterprise knowledge management, we explore how searchable multimedia data is creating entirely new opportunities for storytelling, fan engagement, licensing, and monetization.
We also get into the growing debate around intellectual property and AI training data. Sean offers a thoughtful perspective on why trust, transparency, and rights-cleared content are becoming increasingly important as AI models evolve. He explains why sustainable AI ecosystems will depend on respecting creators, fairly compensating rights holders, and ensuring enterprises know exactly where their training data originates.
The discussion then shifts toward the emerging “agentic web,” where AI systems move beyond research and begin handling tasks, workflows, and decisions autonomously. Sean argues that future competitive advantage will belong to organizations with access to the highest-quality licensed data, because the difference between an average AI agent and a truly effective one will come down to the quality, structure, and accessibility of the information they can use.
We also talk about the human side of AI adoption. Sean shares why he believes AI should be viewed as a tool that amplifies human potential rather than replacing it, comparing today’s AI shift to the arrival of the internet itself. From personalized AI experiences to machine-driven workflows and new business models around licensed content, this conversation offers a fascinating look at how the AI economy is beginning to reshape media, enterprise technology, and digital experiences in real time.
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