Media
| Laurence Mifsud |
14 July 2023
"Supply chains" aren't exclusive to manufacturing and logistics. Digital media has a supply chain of its own that encompasses the entire content lifecycle, from ideation to development, and all the way down to production, post-production, marketing and distribution. That means the management of digital media files along with their associated metadata throughout the content value chain. The Digital Media Supply Chain (DMSC) forms an intricate ecosystem where diverse digital assets are created, assembled, managed and finally delivered to the end consumer.
Globally, digital content volumes are expanding exponentially, and any asset is eligible for worldwide distribution and monetization as soon as it is created digitally. As such, the need for DMSC streamlining is more critical than ever. This involves refining workflows, enhancing processes and leveraging cutting-edge technologies like artificial intelligence (AI), machine learning (ML) and cloud infrastructure.
A subset of AI called Generative AI presents an extraordinary opportunity to accelerate and, in many ways, fundamentally upend the traditional approaches to content creation and distribution. Below, we'll look at how Generative AI offers practical solutions for DMSC optimization and explore some of the impediments to using it to successfully to streamline and optimize content creation, distribution, management and monetization processes.
Digital Media Supply Chains: Leveraging Generative AI
Integrating generative AI into DMSC processes and ecosystems enables the automation of various workstreams and processes, creates additional avenues for personalizing content, optimizes metadata management and fosters predictive analysis and trend forecasting.
Generative AI models such as Generative Pretrained Transformer (GPT) models or Generative Adversarial Networks (GANs) can create everything from written text to music to images. Visual AI is focused on understanding, interpreting and generating images or videos. This includes tasks like image recognition, object detection and even creating or altering new images.
But it's the fusion of generative AI with visual AI that gives us the most powerful tool for to generate new visual content such as character designs for video games or animations based on simple descriptions. These tasks are labor-intensive and time-consuming to do manually.
The combination of generative AI and visual AI makes it possible to automate and accelerate many aspects of the creative process in digital media. This opens up new possibilities for content creation and personalization that were previously unattainable due to limitations in technology and resources.
Here are four examples of how generative AI can be utilized in different aspects of the digital media supply chain.
Automating Content Creation
Generative AI can automate processes such as script generation, storyboard development, character creation and graphic design, resulting in streamlined and compressed production cycles, improved efficiencies and cost reductions. For example, AI models like GPT-4 can generate simple dialogues and complete storylines based on a defined parameter, and it's much faster than the fully manual creation of scripts and screenplays.
Meanwhile, tools like DALL-E can create unique characters or graphics based on textual descriptions. It's not a replacement for human creativity, but DALL-E can generate new concepts that don't exist in the real world, combine disparate elements in a single image, and generate images of hypothetical objects. This can be particularly useful in fields like animation or video game design, where creating unique characters and environments is crucial.
Similarly, advertising and marketing professionals can use DALL-E to generate visual content for campaigns based on specific thematic or conceptual requirements. This could significantly speed up the creative process, reduce production costs and even remove the need for manual sketching. DALL-E's designs provide a wealth of options that artists may not have considered. As a result, generative AI can accelerate the media supply chain by speeding up the production process and opening up new possibilities for creativity.
However, this approach isn't without its challenges. AI models lack a human artist or writer's nuanced understanding and instinctive creativity. Therefore, while AI can assist in the creative process, human oversight is crucial to ensure that the generated content meets the desired quality and artistic standards. Further, there's the challenge of bias in AI, as models can unintentionally perpetuate the biases present in their training data.
The topic of intellectual property (IP) also becomes particularly intricate when it comes to AI-generated content. As AI's capabilities expand, especially in the realm of generative AI, questions arise over who owns the rights to the content that these systems produce.
Personalized Content Delivery
Generative AI can craft a unique viewing experience tailored to each viewer's preferences. This personalized approach doesn't just improve viewer engagement and satisfaction; it also bolsters strategies for monetizing content more effectively.
For example, the same news article could be tweaked to adopt various tones or styles to cater to different age groups. Or a promotional video could emphasize different product features depending on what's most likely to capture a specific viewer's interest, resulting in higher engagement.
The power of personalization is particularly potent when it comes to recommending content. Take Netflix's recommendation algorithm, for instance. It gleans insights from viewers' past interactions with content, including their viewing history, ratings and even the time they tend to watch. Armed with this information, it can suggest content that is likely to appeal to a viewer. This tailored approach can boost viewership, ramp up ad engagement, and pave the way for targeted advertising and product placements.
However, the use of AI in personalizing content delivery also comes with challenges. AI operators must take care to ensure data privacy and to avoid creating "filter bubbles" where viewers are only exposed to content that aligns with their existing preferences, potentially limiting their exposure to a wider range of content.
Intelligent Metadata Management
Automatically generated metadata, such as keywords, categories, or tags, can improve content discovery and enhance recommendation algorithms. For example, YouTube uses AI-generated metadata to guide its recommendation system, leading to a more personalized and engaging user experience.
The automatic generation of metadata isn't limited to just traditional categories. AI can create detailed and nuanced metadata, such as the mood of a song or the visual aesthetics of a video. This significantly improves content discovery and personalization, allowing users to find exactly the type of content they're looking for.
But while AI can greatly enhance metadata management, it's not a standalone solution. AI-generated metadata should be reviewed and validated to ensure accuracy. There should be processes in place to handle any errors or inconsistencies that may arise and resolve concerns around data privacy.
Predictive Analysis and Trend Forecasting
By analyzing past and present viewer behavior and discerning patterns, AI can anticipate future viewer preferences and trends. This predictive capability can then guide content creation and marketing strategies, enabling digital media companies to create content that resonates with their audience and stays ahead of evolving trends.
Look at Spotify as an example, which uses AI to analyze listener behavior, including song choices, skipped tracks and listening frequency. By analyzing this data, Spotify's AI can predict the types of songs or artists a listener might prefer in the future. It can then recommend new music accordingly. This leads to a more personalized user experience and helps Spotify keep listeners engaged on the platform.
Similarly, companies like Netflix leverage AI to forecast viewer trends. By analyzing data such as viewing history, ratings and search queries, Netflix's AI can discern trends and preferences to optimize viewer engagement. In turn, this boosts subscription revenue and viewer retention rates.
However, while AI-driven predictive analysis and trend forecasting offer significant benefits, data privacy and security are crucial considerations when handling sensitive user data. That said, the predictions made by AI should be seen as a tool to inform decision-making rather than a definitive guide.
The Future of Digital Media: Powered by Generative AI
As generative AI continues to evolve, we can expect it to become an increasingly integral part of the digital media supply chain, transforming how content is created, managed, and delivered. In the future, we could see even more advanced AI models capable of generating increasingly complex and creative content, further personalizing the viewer experience and offering even more accurate predictions.
While challenges such as data privacy, bias, and IP rights remain, the potential benefits of generative AI are vast. As we navigate these challenges, it's important to remember that AI is not a replacement for human creativity and intuition but a powerful tool that can augment our capabilities.
At Endava, we're excited about the potential of generative AI to revolutionize the digital media supply chain. If you're interested in learning more about how you can leverage generative AI in your workflows and stay ahead of the curve, we're here to help you navigate the exciting opportunities this technology presents. Contact us today for any inquiries.
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Laurence Mifsud
Head of Content (Broadcast and Publishing Sales)
Laurence is a 20-plus-year veteran of the media and broadcast industry with a focus in sales and relationship growth. As traditional broadcast evolves to encompass streaming, publishing and overall content, Laurence works with our partners on a global basis to align their current and future business objectives. He resides in London happily with his family, and he wishes he had more time to consume broadcasts outside of work hours.
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