DAM + generative AI = A new paradigm for creatives and martech leaders, as this powerful combination enables a seamless workflow where digital assets can be efficiently managed and dynamically generated. By integrating advanced AI algorithms with Digital Asset Management systems, teams can not only streamline the process of storing and organizing their assets but also leverage AI to create tailored content that resonates with their target audience. This innovative approach enhances creativity and productivity, allowing marketers to focus on strategy rather than manual tasks, ultimately leading to more engaging campaigns and improved brand experiences.
Generative AI (GenAI) has changed marketing significantly, enhancing customer interactions and content creation. While attention has been on chatbots and blog posts, a shift is occurring in Digital Asset Management (DAM). Initially focused on improving asset findability and reuse, we are now discovering new valuable applications that extend beyond simple asset tagging and unlock the full creative potential of your DAM solution.
Unleashing the Power of Asset Tagging and Retrieval!
The core principle of Digital Asset Management (DAM) is asset reuse, which highlights the inefficiency of recreating existing assets. However, achieving effective reuse is challenging because digital media assets like images and videos lack self-descriptive qualities, relying heavily on metadata for retrieval. Traditionally, humans have created this metadata by manually entering information, which is often inconsistent and incomplete. Organizations either ask creative teams to do this during asset ingestion, which is generally disliked and poorly executed, or hire librarians to manage metadata post-ingestion. Due to these challenges and reluctance, many organizations struggle to generate adequate metadata, hindering precise asset retrieval and reuse.
GenAI solves this problem in two very meaningful ways. First, with GenAI organizations are no longer dependent on humans to properly “tag” or apply metadata to assets. Computer Vision is a particular aspect of artificial intelligence (AI) that enables computers to interpret images, video and other rich media assets.
Utilizing Computer Vision, and particularly Vision-Language Models (VLMs), we can now automatically generate text to describe images and videos. We can also easily convert audio – either audio files or audio tracks for video – into text. As a result, we have a virtually limitless, inexhaustible and inexpensive resource to tag digital assets. These models can be augmented or fine-tuned to provide specific metadata that is unique to your organization or intellectual property – think, for example, about color codes, product IDs or character versions. And, they can be constrained by your organization’s unique taxonomy and ontology.
GenAI enhances asset retrieval by allowing users to use natural language for precise searches, effectively addressing the asset reuse challenge and helping DAM users find existing assets quickly and comprehensively.
Ditch the Tags: Revolutionize Your Asset Creation Process
That’s a pretty extensive overview of how GenAI can address asset findability and reuse. And, as you’ll find, many DAM platforms have begun to incorporate GenAI-powered functionality to intelligently tag assets and enable natural-language searches, drastically improving user experience. But what we’re beginning to see is a whole new set of use cases — beyond tagging and retrieval — that will streamline and accelerate new asset creation and the asset review process. For instance, these advancements may include using GenAI to analyze user behavior and preferences, thus enhancing the relevance of the suggested assets during searches. Additionally, the automation of repetitive tasks in the asset lifecycle can free up creative teams to focus on more innovative endeavors, ultimately fostering a more dynamic and productive environment. As the technology evolves, the potential for integrating GenAI into various aspects of digital asset management appears promising, paving the way for a future where efficient collaboration and faster time-to-market become the norm rather than the exception.
Unleashing Bold Asset Ideas
One powerful use case we’re seeing is asset ideation. Creatives can upload sample assets and define parameters for new ideas using a simple natural language. This data is processed by a Computer Vision model that quickly generates various asset concepts. Users can then refine their results through a chat-like interface, making it easy to identify working concepts.
We want to highlight that “concepts” is essential, and GenAI is best for generating ideas rather than creating assets. We’ve noticed that although Computer Vision models can produce many visual assets quickly, most people can easily tell which ones are AI-generated, and they don’t feel as authentic as real photos and images.
So the goal is to use GenAI for its strengths: quickly creating ideas to help creative users plan campaign assets, photo shoots, etc., and then letting your creative team finalize the assets. GenAI doesn’t replace creative resources; it gives them tools to work more effectively and efficiently.
Is Your Asset Localization Strategy Failing?
We often think of asset localization as just translation, but it’s more than that. For global companies, visual assets need to be adjusted for local tastes, cultural differences, and specific needs of different areas. For text, this might mean translating into the local language, as well as changing things like currencies and measurements. For images and videos, you might need to change colors or show local clothing and settings.
GenAI can help with asset localization in two main ways. First, it can apply localization rules to existing assets and point out any problems. It can also identify where certain assets should or shouldn’t be used, which can be added to their information for better clarity. Second, GenAI can help create new localized ideas and versions of assets that match your localization rules.
Is Your Brand Playing by the Rules or Just Breaking Them?
Another helpful way to use GenAI is to make reviewing and approving creative work easier by checking if designs meet brand standards. When new designs are made and added to the Digital Asset Management (DAM) system, a GenAI model can see if they follow brand rules. If a design doesn’t match the standards, the model can explain why and suggest how to fix it.
The key point is that when designs are sent for review, those approving can be sure that they meet brand standards, which saves time in the review process.
Unleashing the Power of Intellectual Property
For organizations that use third-party intellectual property (IP) in their products and designs, it’s essential to know what IP is used and in which assets. It’s also important to understand when the organization has permission to use that IP. GenAI can help by identifying if an asset has third-party IP and checking if the organization has the right to use it.
This information is valuable and can be added to an asset in a Digital Asset Management (DAM) system. The process can be automated to check existing assets or applied when new assets are added, ensuring that IP rights are always protected.
This Is Not a Plug-and-Play Experience
As a final thought, and something I will explore more in future articles, GenAI models depend on their training data. In the early days of AI, we believed we needed to create custom models to tag assets accurately or check brand compliance. Nowadays, with techniques like Retrieval-Augmented Generation (RAG), we can use publicly available models for these tasks, although some may need adjustments to improve accuracy and output.
The key point to understand is that to get accurate and useful results from GenAI – even for tagging assets – you need to consider your model inputs and fine-tuning. This is not just a simple feature you can turn on. However, organizations that manage this properly can gain great benefits, as GenAI can truly enhance the value of your Digital Asset Management (DAM) solution.
