For Media and Entertainment enterprises monetization of content is paramount. The journey to monetization starts with a robust Media Asset Management (MAM) platform to store, organize and collaborate.
The evolution of media asset management (MAM) solutions has been driven by the rapid advancements in technology. Initially, MAM solutions were on premise, requiring significant hardware investments and maintenance. These systems offered robust control and security but lacked flexibility and scalability. As media companies faced increasing volumes of digital content and the need for more scalable workflows, hybrid solutions emerged. This allowed organizations to balance control with the flexibility of cloud. The most recent shift is led by phenomenal progress made in AI and Gen AI.
As per the DPP Predictions 2024 – cost, adaptability and Artificial Intelligence driven media operations tops the mood music for 2024. As per the report, content owners have started to believe that adapting AI in different areas of content lifecycle will have an increasingly significant role in driving operational efficiencies and new revenues in the next two years.
However, the fundamental question to ask is “Is the MAM smart”? Does just generating AI deep metadata and storing it, makes a MAM smarter?
This blog aims to put a perspective and assist M&E enterprises in formulating future MAM strategies.
Challenges with the traditional MAM
The challenges with traditional MAMs are well known and manifest in all too familiar scenarios. Some of the challenges in managing media archives traditionally include:
- Inefficiencies in search and retrieval - Unorganized content results in time- consuming search and retrieval processes. Users find it difficult to locate media assets among thousands or even millions of assets. This hampers overall productivity and constrains their objectives for content monetization.
- Limited or non-existent metadata – Metadata is the fuel for media workflows. Beyond technical metadata, content workflows require deep metadata. Deep metadata enriches content, enabling users to navigate extensive libraries seamlessly. Human cataloging is time-intensive and constrained by the metadata it can generate. M&E companies can unlock additional value in their content, and enhance ROI, with deep metadata.
- Limited automation and collaboration - Traditional MAMs are typically built to store and exchange content which often results in restricted automation capabilities and collaboration functionalities. Business agility presupposes agility inside the MAM to swiftly adapt to evolving demands, streamline workflows, and foster seamless collaboration across teams.
Traditional MAMs commonly attempt to tackle some of these challenges, particularly those related to metadata, by leveraging third-party AI models for metadata discovery and integration into the MAM. Nevertheless, these approaches have their limitations as they aim to solve only for metadata issues without aligning them with the specific objectives and outcomes demanded by the users. Hence the metadata for most part remains dormant within the MAM, untapped by users.
If your MAM is encountering many of these challenges, it may be outdated. This indicates that it's time to reassess your media archive system and look for the right media supply chain solution. Look for key indicators to help you decide if it's necessary to upgrade to a more efficient solution that meets your organization's needs and ensures smooth operations in the current digital content landscape.
So, what is a smart MAM?
Primarily, smart MAM stands apart from traditional MAM systems or those with added 3 rd party AI capabilities. To draw an analogy, it's akin to distinguishing between a cloud-native solution and one that is simply hosted on the cloud. smart MAMs come with an innate intelligence layer which empower users to harness the capabilities of AI and Generation AI to achieve successful results effortlessly and flexibly.
smart MAMs can offer new possibilities and ways of working which make enterprises hyper- efficient. Some ways in which smart MAM can transform M&E Digital Enterprises are
Content Management
AI-generated metadata can often become overwhelming in volume, leading to a flood of non-actionable information. smart MAMs, however, effectively utilize AI metadata to transform user experiences inside the MAM. Starting with intelligent categorization of AI metadata to bringing in conversational interfaces they can allow users to accomplish various goals like search, discovery to deduplicating archives, with greater ease.
Content Creation
smart MAMs extend beyond simple discovery functions. They aid in ideation, storyboarding, generating readily usable clips, facilitating faster and more accurate reviews, and more. By streamlining creative processes, enhancing storytelling capabilities, and fostering collaboration, they significantly reduce creative cycles.
Content Marketing and Distribution
smart MAMs can expedite content marketing and distribution workflows by seamlessly integrating AI, specifically GEN AI capabilities, alongside human collaboration. For instance, Gen AI-generated social media post recommendations can spark creativity during the ideation phase for content creators, while advancements like AI reframing can eliminate tedious tasks, thus streamlining the process.
Work with the right smart MAM provider for success
Talk to Prime Focus Technologies (PFT) about AI-led technology and media services that automate critical aspects of the content supply chain for reduced TCO. Finding the right vendor with a proven track record in media asset management is, for many M&E firms, the missing piece in the AI puzzle.
Want to use AI in your organization but unsure where to start? Watch this video to better understand how AI merges into your existing workflows. Watch the entire episode here!