AiThority Interview with Ryan Steelberg, CEO & President of Veritone

Ryan Steelberg, CEO & President of Veritone chats about the move to an AI-first ecosystem and what that means for the existing and past content assets of most businesses in this AiThority interview:

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Hi Ryan, tell us more about the shift toward a more democratized AI ecosystem, what it truly means for organizations adopting AI?

Enterprise AI is entering a new phase where access to capable models is no longer the primary competitive advantage. Organizations today have more high-performing AI models to choose from than ever before, giving them the flexibility to select the right model for the right use case rather than relying on a single vendor or platform. As these models become increasingly powerful, accessible and cost-effective, the differentiator is no longer the AI itself—it’s the proprietary knowledge, institutional expertise and unique data that organizations bring to it. Instead of asking, “Which model should we use?” enterprises should increasingly be asking, “What unique knowledge can we bring to AI that no one else has?” The democratization of AI allows organizations to spend less time chasing the latest model and more time solving meaningful business problems. Over the long term, success won’t be defined by who has access to AI—it will be defined by who can combine AI with trusted, proprietary knowledge to create better decisions, smarter workflows and lasting business value.

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Why are dormant media archives and unstructured content becoming strategic AI assets today?

Organizations have accumulated enormous amounts of unstructured content over the years, much of which has remained largely inaccessible. In industries like media and sports, for example, major events such as the FIFA World Cup generate vast libraries of video and audio that often sit unused after their initial purpose has been served. AI is changing that by making it possible to automatically index, transcribe, classify and understand content at a scale that simply wasn’t feasible before. As a result, archives are evolving from passive storage repositories into dynamic knowledge assets that support content creation, fan engagement, rights management, research, institutional memory and new revenue opportunities. Similar transformations are taking place across government, public safety and other sectors where historical content contains valuable operational knowledge. The greatest opportunity isn’t creating more content—it’s unlocking significantly more value from the content organizations already own.

How can organizations transform proprietary content into AI-ready actionable knowledge?

Preparing enterprise content for AI begins with understanding what information already exists across the organization and making it discoverable. Most enterprise knowledge lives in unstructured formats—documents, videos, audio recordings and images—which must first be enriched through transcription, metadata generation, tagging and classification before AI can effectively use it. Equally important is establishing strong governance, provenance and data quality practices from the outset so AI systems are built on information that is accurate, trusted and auditable. The objective isn’t simply to create searchable archives – it’s to transform content into trusted knowledge that employees, business applications and AI agents can securely access and use with confidence. Organizations that invest now in preparing and governing their data will be in a far stronger position to scale AI initiatives and realize meaningful business outcomes over the coming years.

Why will trusted, rights-aware enterprise content will become more important than ever in the next few years

As AI becomes embedded in increasingly critical business workflows, trust will become just as important as model performance. Organizations need confidence that the data informing AI systems is accurate, current, properly licensed and aligned with internal governance policies. Rights-aware enterprise content provides that foundation by reducing legal, compliance and reputational risk while improving the transparency, reliability and quality of AI-generated outputs. Across the industry, the conversation is already shifting away from simply building more capable AI models toward building AI that organizations can confidently deploy at enterprise scale. In the years ahead, the organizations that invest in trusted data foundations, responsible governance and transparent AI practices will be the ones best positioned to capture long-term value from artificial intelligence

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