Integrating AI into traditional media asset managers unlocks advanced features and finally fulfills the original promise of these systems as dynamic, adaptable repositories in a content-heavy world. 

In the rapidly evolving media landscape, organizations are increasingly integrating artificial intelligence (AI) into their media asset management (MAM) systems to boost efficiency and reduce costs. The swift advancement of AI technologies necessitates a thorough reassessment of existing workflows to identify and address underperforming areas, ensuring that the full potential of AI is harnessed. By adopting multimodal and generative AI, media companies can significantly enhance content searchability, gain contextual insights, and explore new avenues for return on investment (ROI).

AI Helps MAM Systems Reach Their Potential

Within media organizations, traditional MAM systems hold a crucial function. They organize and store digital assets in searchable repositories, however, many legacy MAMs are built around static metadata tagging, demanding extensive manual input – and often falling short when scaling for today’s vast content demands. This outdated manual approach is both labor-intensive and limits MAM’s ability to generate contextual inferences within archives. 

Multimodal AIs, such as MXT-1.5, offer a new way of working by automating the creation of quality indexing through vector embedding or commonly used data formats that can be integrated into a MAM database. Indexing empowers MAMs to recognize relationships between assets, which radically improves searchability and content discoverability through the lens of concepts, rather than just keywords. Integrating AI into traditional MAM infrastructure unlocks advanced features such as semantic search, automated summaries, and sound bites, finally fulfilling the original promise of MAM systems as dynamic, adaptable repositories in a  content-heavy world.    

However, legacy MAMs are not always equipped to handle the massive metadata volumes AI generates. Simply layering AI onto older systems only taps a fraction of the data’s potential. AI’s power can be truly transformative for media workflows, if organizations adopt platforms designed for the scale and complexity of AI.

Enabling New Content Discovery Workflows

Multimodal and generative AI are not only reshaping media management, but opening doors to workflows previously beyond human reach. By processing and integrating data types such as text, images, and audio, AI can generate thorough summaries and detailed scene descriptions, tasks that otherwise demand significant human effort and financial investment.

One of our clients, the 24/7 Arabic news service Asharq News uses multimodal AI on newly broadcast content, empowering them to automatically generate transcripts, facial recognition data, and summaries that enable Asharq producers to stay aligned with — and even ahead of — current trends. 

Another significant use case lies in archive digitization. Vast magnetic tape collections, once the backbone of media archives, are rapidly reaching end of life, creating a daunting challenge for digitization. Manually cataloging each tape represents a substantial time investment, which is prohibitive for many organizations. The U.S. media network Hearst, our longstanding customer, calculated that it would take 100 years to index 200,000 hours of archive. Multimodal and generative AI changes the game, not just in relation to the speed at which it can index digitized content. Advanced AI models are showing enormous potential in identifying what’s inside physical tapes before digitization, providing insights into the value of that content. 

The Speed of AI’s Development Calls for Constant Reevaluation

The next generation of AI has brought an unprecedented speed of technological change. Organizations and vendors operating with advanced AI models must continuously reassess their applications and workflows to ensure they remain competitive. This evolution, which is showing to be faster than even the shift to HD, requires service providers to take a dynamic approach to managing the role of AI in media operations. AI-driven content discovery platforms equipped with built-in evaluation at the core and supported by dedicated research teams, will push technology providers towards proactive innovation in AI video understanding. This will also intensify the need to keep pace with rapid advancements and maintain rigorous output quality assessments — a challenge that will grow as AI continues to evolve.

Identifying Inefficiencies in Media Workflows to Maximise AI’s Benefits     

Effective AI implementation calls for thinking in terms of outcomes rather than outputs. The first step is recognizing specific inefficiencies within a media organization’s workflows. Traditional MAM systems are often inconvenienced by low-quality metadata and fragmented indexing, which limits their usefulness for rapid asset search and retrieval. Identifying these pain points allows organizations to strategically apply AI to create tangible improvements in media operations. This use-case centric approach helps focus AI’s potential on areas where it can deliver the most value. As a result, the integration of AI is only a part of a larger transformation of how content is managed, accessed, and used.

Integrating multimodal and generative AI into MAM and content discovery workflows marks a significant advancement in media operations' efficiency and effectiveness. Traditional MAM systems often struggle with low-quality metadata and fragmented indexing, hindering rapid search and retrieval capabilities. By adopting AI technologies, the media industry can address these challenges, enabling professionals to navigate the evolving landscape with enhanced agility, depth, and innovation.

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A version of this article was originally published by IBC 365.

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