Industry Insights: A look at modern MAM systems and workflow optimizations

How do you manage massive volumes of media without sacrificing speed or creativity? That question lies at the heart of this continuing Industry Insights roundtable, where we explore the evolution of storage strategies in broadcasting.

Building on our previous discussion, we dig deeper into the role of modern media asset management (MAM) systems in handling everything from legacy archives to AI-assisted metadata enrichment.

Our industry experts reveal how to tackle format incompatibilities, high costs of migration, and the ever-present demand for faster workflows.

Whether it’s real-time collaboration across global teams or leveraging advanced metadata for monetization, the strategies highlighted here can transform how broadcasters store, organize, and retrieve content. Ultimately, it’s about aligning technology with the creative vision — because efficiency shouldn’t stand in the way of innovation.

Industry Insights: Storage strategies in broadcasting, from on-prem to cloud

Key takeaways from this Industry Insights roundtable

• Scalable and flexible future: Broadcasters increasingly favor more flexible, interoperable platforms that are simpler to scale and boost user acceptance.
• AI-driven tagging: Automated metadata generation speeds up content retrieval, letting creative teams focus on storytelling.
• Legacy integration: Phased migration and transcoding strategies prevent valuable archive footage from going unused.
• Open APIs: Seamless data exchange across production tools reduces technical barriers and accelerates workflows.
• Worldwide collaboration: Cloud-ready, user-friendly solutions empower teams to edit and review assets from anywhere.

What are the key features of a robust MAM system for modern production needs?

Denise Soto, director, product and QA, TMT Insights: A robust MAM system designed for modern workflows requires collaborative tools aimed at streamlining operations and enhancing efficiency, which include features like search capabilities and transparency of data occurring within systemic automated workflows. Furthermore, it provides comprehensive metadata management, accommodates various media formats, implements robust security measures, and includes role-based access controls to safeguard assets. 

Peyton Thomas, product manager, Panasonic Connect: Automation and collaboration across multiple third-party systems will help build a robust MAM system for modern production needs. Automation helps speed up workflows and allows teams to spend less time managing content, which in turn gives them more time to create unique, personalized content that meets the demands of today’s consumers. Collaboration allows users to work together in real-time, reviewing content and making changes efficiently, regardless of location.

Ryan Stoutenborough, president, Studio Network Solutions: A robust MAM system should include advanced features like AI auto-tagging, subclipping, custom metadata fields, automations, and integrations with other production technology. Unfortunately, added capabilities often come with added complexity, which can hinder adoption across the company if these features are not also met with an intuitive user experience. Above all, a balance between powerful features and user-friendliness is essential for modern production needs.

Jan Weigner, CTO, Cinegy: A modern MAM system needs to be the Swiss Army knife of broadcast — handling everything from ingest to distribution while enabling real-time collaboration. Our systems allow editors and producers to work on material on the fly, with parallel access for authenticated users. Most importantly, it needs to automate the tedious stuff so creators can focus on, well, creating.

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Jochen Bergdolt, global head, MAM business unit, Vizrt: Primarily, a robust MAM system must be built with reliability at its core. In practice, this means ensuring that the system can recover from losing a single piece of infrastructure in a reasonably seamless fashion. The software must also recover from any individual failure, automatically retrying or adapting jobs and tasks that have not succeeded.

Claire Humphries, product manager, news and production, Grass Valley: A robust MAM system for today’s production needs must feature open APIs for seamless third-party integration, ensuring compatibility with a wide range of tools and workflows. It should prioritize user-friendly interfaces for operators, alongside reliable federated searching to locate assets efficiently across systems. Additionally, it must include an intuitive workflow orchestration tool that empowers customers to adjust technical workflows independently

Bob Caniglia, director of sales operations, Americas, Blackmagic Design: In order to operate at the speed that broadcast requires, all modern media asset management needs to prioritize real-time collaboration tools, such as the ability to work on the same editing timelines at the same time and live sync capabilities. High-speed data transfer technologies ensure seamless access to media files even in multi-user high-resolution workflows, and integration with cloud services enables scalable and efficient remote collaboration while maintaining local caching for minimal latency.

What are the challenges and solutions for integrating legacy media assets into modern MAM systems?

Denise Soto: Integrating legacy media assets into modern MAM systems is a challenge due to a lack of metadata, format incompatibility, and the high cost and time required for migration and ingestion. Solutions include using AI tools to enrich metadata, transcoding legacy files into modern formats, and adopting phased migration strategies to prioritize critical assets while managing costs and resources effectively.

Julian Wright, CEO, Blue Lucy: One of our customers has 33 petabytes of archive material with no metadata beyond the file name and no browse versions of the content. This media may as well not exist as you can’t monetize what you can’t see.  This isn’t an isolated case but a situation that’s becoming more common due to rationalization in the industry and multiple catalogues being combined. We’ve helped customers facing this challenge with a simple microservice that derives discrete metadata from the file names; you could also use AI-powered tools like image and facial recognition to automatically create some of the missing metadata.

David Phillips, senior solutions engineer, infrastructure, LucidLink: One major challenge is the variety of older codecs and file formats, as well as inconsistent or missing metadata, which complicates indexing and management in modern MAM environments. Another challenge is the cost and complexity of migrating assets from outdated tapes or optical systems to cloud or hybrid storage. Solutions include structured metadata onboarding, automated transcoding workflows, and bridge technologies that facilitate efficient, scalable migration into current MAM systems.

Ryan Stoutenborough: With ShareBrowser MAM, broadcasters can centralize their entire media library — including offline and archived legacy assets — in one searchable database. Once legacy media assets are integrated in a MAM, the issue becomes weeding through all those files to find the clip you need. AI auto-tagging simplifies the process of adding metadata to decades of untagged content, reducing the manual workload from months to minutes. Modern tools like these ensure legacy assets remain a usable and valuable part of your media library.

Laquie TN Campbell, media and entertainment product marketing manager, Backblaze: The migration of legacy assets into modern MAM systems can often feel like a technical and logistical nightmare, but there are partnered MAM and cloud storage systems offering structured, assisted migration plans to minimize disruption of operations and protect assets throughout the process, at minimal cost. Broadcasters should also vet cloud-friendly solutions that assist with automating transcoding pipelines (that can process many obsolete formats), quality control, and intelligent metadata mapping tools.

How can broadcasters ensure interoperability between MAM systems and other production tools?

Peyton Thomas: Broadcasters should prioritize software-based solutions that seamlessly integrate with one another. By leveraging API integration, broadcast tools can enable automation features to communicate effectively across various third-party equipment.

Julian Wright: The MAM or orchestration vendor should provide, or be able to rapidly build, connector microservices into any system, including legacy platforms. The vendor should also keep these connector interfaces up to date so that metadata passing between systems is correctly mapped. Adaptability in the processing layer is essential.

Laquie TN Campbell: Broadcasters can ensure interoperability by prioritizing partnerships with technology providers who support robust API ecosystems and actively collaborate with other industry leaders across the entire pipeline. MAM implementation requires careful evaluation of each workflow touchpoint to verify compatibility with both current and emerging production tools, while selecting vendors who demonstrate commitment to open standards and extensive third-party integrations. Also look for MAMs with the ability to seamlessly handle both on-premise and cloud-based tools and support implementation of standardized protocols like SMPTE ST 2110, IMF, and MXF.

Jochen Bergdolt: Standards initiatives can play their part, but often applications and tools need to be built with interoperability in mind — making it quick for software companies, customers, or third parties to integrate tools together and share content. Viz One ships with a REST based API for just this need, allowing Vizrt’s customers and partners to build their own integrations, or working with our engineering team to build out their required capabilities to work with other systems. Low and no-code solutions will also play their part, allowing customers to tweak business logic and adapt workflows without having additional development costs.

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Bob Caniglia: Many products on the market today can help broadcasters achieve maximum interoperability, whether through pre-built integrations or those built by third-party developers. To ensure interoperability between technologies, broadcasters can prioritize products that interface between MAM systems and other tools in live production environments and should also invest in solutions that support open standards and protocols, enabling seamless communication across different platforms.

How can AI-powered MAM systems streamline media workflows and metadata management?

Denise Soto: AI-powered MAM systems streamline media workflows by automating metadata generation, including speech-to-text transcription for subtitle creation and object recognition for keyword indexing, thereby significantly reducing manual effort and errors. Furthermore, these systems also manage tasks such as content ingestion and quality control, ensuring faster turnaround times and greater precision in the management and distribution of media assets.

Peyton Thomas: AI-powered MAM systems can streamline workflows for greater efficiency across projects. A strength of the AI-powered system is addressing “dark data” — unused and unorganized data consuming storage space. By leveraging AI, this hidden data can be automatically tagged and organized, transforming it into a searchable and accessible resource for future projects.

Ryan Stoutenborough: AI auto-tagging in ShareBrowser can quickly generate metadata for decades of untagged content, making it searchable without months of manual work. While human input often results in richer metadata, AI metadata tagging gives creative teams a head start, allowing them to focus on more specific, project-based metadata that an AI engine wouldn’t know to use.

Jan Weigner: Modern MAM systems focus on automatic metadata generation, turning hours of manual labor into streamlined, efficient processes. We’re seeing AI’s role increasing in everything from automatic transcription and summarization to cataloguing, translation, and captioning. This automation of metadata enhancement is crucial because only metadata that is generated automatically will be entered for certain.

Jochen Bergdolt: AI can augment, and in some cases replace, much manual data entry. And when combined with AI search technologies, helps to quickly find and utilize the right content, for instance for sports highlights and package creation. Composing new sequences, such as promos or highlight reels in an automated or semi-automated fashion, is likely to become the norm – with solutions making content selection choices and simplifying the addition of on-screen data-driven graphics.

Leanne Tomlin, marketing director, EMEA, Perifery: Advanced metadata management and AI capabilities allow us to search data from its content, giving us easier access and driving more value from assets that were once considered static. This means organizations can justify their archive investments through a measurable return on assets previously sitting in storage. It’s changing our perception of what content is and isn’t valuable.

Claire Humphries: AI-powered MAM systems revolutionize media workflows by automating the generation of secondary content from approved media, reducing manual effort and speeding up delivery. With capabilities like AI-driven language localization and international voiceovers, they enable global reach and enhanced audience engagement. These systems integrate seamlessly with workflow automation, ensuring consistent, efficient metadata management across projects.

Sam Peterson, media technology leader and COO, Bitcentral: AI-powered MAM systems streamline workflows by automating time-consuming processes like tagging, cataloging, and metadata enrichment, drastically reducing manual effort. These systems improve searchability and enable content teams to focus on creative tasks, accelerating production timelines and maximizing the use of media assets.

Sam Bogoch, CEO, Axle AI: AI-powered MAM systems analyze media for what is happening in the video, including objects, speech, and scenes, automating the creation of detailed metadata. Prior to availability of these systems, MAMs relied on manual tagging of the material, and there was never nearly enough staffing or time to make this tagging broadly useful. AI solves this problem and allows nearly all media to be searched rapidly, thus letting editorial teams focus on the creative side of their jobs rather than endlessly scrubbing through media to find what they need.

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Geoff Stedman, CMO, SDVI: fNew AI-powered tools are being used to analyze content archives and extract far more metadata than has been possible before. This metadata augmentation enables more efficient media workflows because much of this new metadata is time-based and can be used to guide operators to specific points of interest. Workflows such as QC verification, segment identification, ad insertion, and more are all made more efficient with the use of AI-generated time-based metadata.

How can automated tagging and metadata enrichment improve searchability in MAM systems?

Denise Soto: Automated tagging and metadata enrichment streamline content management, making it easier to locate specific assets- such as scenes, characters, or locations for editing, repurposing, or distribution. By reducing the need for manual searches, these tools accelerate workflows and ensure consistent organization of media libraries, allowing for a greater focus on creative processes.

Whit Jackson, VP, global media and entertainment, Wasabi Technologies: Video editors and creatives can spend up to 90% of their time searching their libraries for the right file, scene or clip. This waste of time can be eliminated by applying AI/ML processing to media in storage to generate time-code-based descriptive and technical metadata that can be ingested by MAM systems to enable the rapid search of media libraires for people, scenes, objects, spoken words, logos and more. Enabling highly refined content search in MAM systems speeds production workflows and shortens time to revenue.

Sam Peterson: Automated tagging uses advanced AI algorithms to assign precise and contextually relevant metadata to assets, ensuring they can be located quickly in vast libraries. This improves searchability, eliminates errors from manual entry, and allows teams to spend less time searching and more time creating impactful content.

Geoff Stedman: Most MAM systems have limited metadata on content in an archive, mostly due to the limitations associated with manual entry when the content was first ingested. Automated tagging and metadata enrichment adds far more information about stored content, which can then be used to enhance search capabilities when looking for more specific results.

What is the role of metadata in maximizing the value of stored media assets?

Philip Grossman, VP of solutions architecture and business development, DigitalGlue: Metadata plays a crucial role in maximizing the value of stored media assets by making content discoverable, accessible, and meaningful. It provides details like descriptions, keywords, and technical specifications, preventing assets from becoming “dark data” that cannot be easily found or reused. Metadata ensures compatibility with modern workflows by embedding technical details like file formats and resolutions. Additionally, it enhances usability through better sorting, versioning, and rights management, enabling efficient reuse and monetization of content across various platforms.

Ryan Stoutenborough: Metadata gives broadcasters and post-production teams a wider pool of datapoints with which to search, so they can find the right media asset in less time. This maximizes the value of your stored assets because you can only monetize assets you can access, and metadata puts everything in your storage infrastructure at your fingertips.

Leanne Tomlin: Metadata allows organizations to fully exploit their media libraries by making content easily discoverable. By enriching content with descriptive tags, technical details, and contextual information, metadata simplifies organization and retrieval. This enables organizations to quickly repurpose and monetize their assets. With effective metadata management, large volumes of content become actionable resources that drive operational efficiency and maximize ROI. 

Geoff Stedman: Expansive and complete metadata is critical to making content findable, both in an archive and in consumer services. If content cannot be found, it of course has minimal value and, in fact, is just costing money to store. The better the metadata associated with a stored asset, the easier it is to find that asset and then monetize it.

How can MAM systems support emerging workflows in media and entertainment?

Sam Bogoch: The best MAM systems powered by AI automate metadata creation, streamline collaborative editing, and integrate seamlessly with cloud platforms. Advanced tagging capabilities make it much easier to find and retrieve specific content, saving time and enabling faster decision-making. Since social media are driving a huge amount of marketing and engagement, AI-powered MAM is becoming essential — and a key aspect is the ability to deploy the AI portion on premise, at predictable rather than hourly-metered costs.

How can MAM systems facilitate collaboration across geographically dispersed teams?

Derek Barrilleaux, CEO, Projective: Think in terms of project containers. With a coherent framework in place for creative projects, it becomes easy to orchestrate the flow of assets and projects to the right team and location. The traditional MAM and storage focus on assets or files does not lend itself to this.

Julian Wright: Your MAM should provide easy access to all your assets, no matter where you or the media is physically located. The platform should also integrate with technologies distributed across physical sites and in the cloud. For dispersed teams, this means that everyone has the same, global view and access to your content inventory, workflows and technologies, making collaboration easier. If, like BLAM, your MAM is underpinned by a distributed services model, global operations are uniform but also support regionally specific capabilities.

David Phillips: With the rise of hybrid and remote teams, MAM vendors have shifted from primarily on-premises, limited-access solutions to cloud-based platforms accessible anywhere via the public internet. However, this transition has introduced a new challenge: distributed teams often face lengthy download times when working with large media assets. One effective solution is to pair a cloud-based MAM service with a cloud-hosted file storage service, such as LucidLink, which eliminates the need to download files by making them immediately available on a local mount point.

Whit Jackson: Modern MAM systems are now cloud-based in that the data management function is performed in the cloud while the original media files can be stored alongside in the cloud or stored on premise with proxy files in the cloud. These cloud-based proxy workflows allow users to organize assets, share assets, perform reviews and publish finished work from virtually anywhere.

Sam Peterson: MAM systems with cloud integration enable geographically dispersed teams to access and work on the same media assets in real-time, ensuring seamless collaboration. By centralizing content storage and providing tools for version control and task management, these systems help teams deliver projects faster and with fewer logistical challenges.

Sam Bogoch: Browser-based MAM systems enable teams to access and review media remotely, ensuring seamless collaboration regardless of location. Axle AI supports this by generating H.264 proxy files and previews for video, image, and audio, which can be accessed on devices like laptops, phones, and tablets. This ensures that teams can collaborate effortlessly, no matter their location.

Geoff Stedman: Cloud-based content archives make geographically dispersed collaboration more accessible and efficient. Rather than sending files between locations, a cloud-based archive is a single repository that everyone can access from anywhere. Teams that use a “follow-the-sun” model where a new team picks up work when the prior team finishes their day can simply access the same asset in the same cloud location, thereby avoiding sending large files from team to team.