Why Organizations Still Struggle To Adopt AI For Video Operations

Ryan Steelberg is CEO and President of Veritone, an enterprise AI company focused on intelligent data and rights-managed AI solutions.

​For studios, production companies, media agencies and broadcasters, the promise of AI-powered video operations is compelling. Who wouldn’t want to automate repetitive tasks in media production workflows like transcription, tagging, content discovery and metadata generation?

In addition to freeing up video production and editing teams from the more mundane side of this work, AI can also deliver faster turnaround times on content, reduced labor costs for routine post-production work and the ability to process larger volumes of content with smaller teams.

Unfortunately, many enterprises, including those I speak with regularly, say they are struggling to see meaningful business impact from their AI investments, even after significant spending. The challenge isn’t unique to video—it reflects a broader problem in enterprise AI adoption.

According to Statista research, the biggest barrier to AI adoption in global business is the lack of skilled professionals (cited by 50% of businesses), followed closely by lack of vision among managers and leaders (43%) and high costs of AI products and services (29%).

These challenges are magnified in video operations. Unlike chatbots or email summarization tools, AI-driven media workflows demand integration with legacy infrastructure, robust governance and organizational alignment across production teams. Studios and media companies operating on decades-old systems face an especially compounded adoption problem: the general barriers to AI are real, but the technical and operational complexity of implementing AI into video production makes those barriers even more formidable.​