Not just users but organizations across industries are gearing up to leverage Generative-AI. For most organizations, it’s not just availing its advantages, but understanding the more profound implications it may have for them matters far more. The capability to generate content, imagery, text, and even music in an instant holds power that’s second to none and can be truly transformative. One of the industries that is directly affected by it is Digital Asset Management (DAM). The question to be asked are: Whether automated content creation and its efficiency, extent, variety, and velocity revolutionize personalized content delivery? Will Gen-AI forever change the face of DAM by dynamically creating assets (along with cataloging and managing)? Will it shoot up the speed of scalability? What will be the ramifications for DAM providers? This insight discusses DAM and its future in the times of Generative AI.
Generative AI uses AI algorithms to produce outputs in the form of images, codes, videos, data, and 3D representations based on the data on which they are trained. Content creation in various forms is at the heart of Generative AI; it is different from other forms or uses of AI, such as self-driving cars or data analysis. Generative AI has taken content creation to the next level, as performing a range of activities that needed human intervention to generate content, such as writing emails, social media posts, images, poems, formulas, etc., can now be carried out using trained data.
As almost all digital assets today are born digital, Generative AI will create assets in the digital world but at far greater speed and scale. The real impact will be customizing an infinite number of visuals, repurposing assets, rebranding, reaching new markets, conducting A/B testing, and making data-driven decisions. In fact, many variations can be created instantly, and many more options than before will be available for A/B tests. Rich media including videos, can be created at the click of a button. Generative AI has the ability to create images from scratch and those that don’t yet exist; hence has the potential to create intellectual property.
Generative AI algorithms can be used to augment or optimize existing digital assets. For example, they can improve the quality of images, remove noise or artifacts, enhance colors, or upscale low-resolution images. These enhancements can help maintain consistency and improve the visual quality of assets within a DAM system.
Apart from that, generative AI can train algorithms to analyse their assets’ contents and automate assigning relevant tags and categories. For companies owning multiple brands and having a presence across various geographies, the way generative AI classifies and streamlines assets can bring revolutionary changes.
Generative AI can improve search and retrieval of assets through advanced tagging and categorization, making searches more effective and targeted by decoding the context of the assets and enhancing asset findability.
Generative AI can automate the process of generating metadata for assets by studying their contents. For instance, it can analyse an asset’s visual or textual elements and automatically generate relevant keywords, tags, descriptions, and even classify assets. It can significantly improve the efficiency of metadata tagging and make asset discovery easier. In principle, generative AI does the job of turning an asset’s metadata into its DNA, hence going much beyond its searchability.
The real expertise of generative AI with respect to DAM lies in new image creation or assets that do not yet exist. It will prove to be of immense help, something that was never imagined before by marketing and communication teams. Instead of giving creative agencies written briefs to generate new assets, they would be able to produce images from within DAM solutions and offer the closest option to agencies for inspiration. Since Gen-AI is trained on zillions of internet images, it uses its existing knowledge to manifest pictures and utilizes its existing knowledge to generate new art forms—right from logos to human images, and alterations of all kinds and aspects are possible. The process’s sheer accuracy, sophistication, and speed will be a step toward transforming asset creation and management.
DAM vendors will need to integrate with Generative AI not only to get an edge over their competitors but also if they are to match customers’ expectations. It will increase the scope of their solution. From being a master repository of digital assets, it would add another dynamic capability and may also find itself in competition with creative tools and technologies, as it will expand DAMs ability to modify assets to several notches. The current capability of DAM is limited; for instance, in the case of images, only basic alterations such as flipping, re-sizing, cropping, and a few other things are possible.
However, Gen-AI textual interface can be used to instruct DAM about how users would want to alter an image kept in the archives. Changes that need a lot of expertise by a professional can be carried out through Gen-AI, such as altering expressions or ethnicity of humans. The process would be straightforward, and the manifested image would not even be needed to be stored but will be dynamically rendered. It will bring significant changes to the DAM market, something that did not exist before, and DAM providers must be prepared to face and embrace them.
Generative AI artifacts comprise high-quality images, words, and designs, including their creation and basic functions (such as editing, color correction, and re-sizing). Through this, Gen-AI has the potential to create new intellectual property.
By learning from existing artifacts, Gen-AI will produce new, realistic, never-made-before artifacts, taking cues from their training data, but does not create the same design twice. The artifacts would not be restricted to images but would include videos, speeches, narratives, and product designs. Another exciting aspect of Gen-AI creations is that they can create within the same type or between two different kinds of artifacts, i.e., creation can be from image to image or from image to narrative. Besides, the artifacts can be entirely new or can be an improvement of an existing one. Moreover, Gen-AI augments creative tasks through human collaboration by instructing the AI generator to strengthen its behavior by giving commands such as “more like this” or “less like this.” Similarly, the process can be automated as well by carving out the parameters for production by human users; for instance, by setting the brand guidelines for creating automated copies. Here’s a classification of the artifacts that can be created for superior content experiences.
While some of the benefits for DAM with respect to Generative AI are clear and visible, such as operational efficiency, workflow improvements, enhanced user experience, and better ROI, certain benefits are still new (and evolving). And the DAM industry is still coming to grips with them, as that’s the nature of AI. However, among the most prominent benefits are:
Gen-AI unlocks an immense potential for personalization for DAM. It can allow enterprises to personalize digital assets for individual users as well as user groups. Customizing images, audio, and videos to suit users’ behavior and taste can be made use of by various industries, especially where custom content is frequently needed to match user preference, e.g., digital commerce. The scale of personalization can be unrestricted, leading to the generation of massive volumes and varieties of assets, which can cater to a diverse range of customer-facing channels. Everything can be created to suit users’ tastes, from videos suitable to multiple channels, 3D designs, or short and long-form text copies.
Moreover, the assets could be molded and assembled into various combinations to offer users personalized experiences. Not only can Gen-AI create more and more versions, but it can also dynamically support the assembling of content artifacts to develop original customized responses to customer demands, thereby quickening up the time of creative optimization. It makes the process of targeting, optimizing, and re-targeting smooth and straightforward, primarily because Gen-AI arms DAM with real-time asset creation.
As Generative AI continues to evolve, the future of DAM will continue to progress with it. The real-time asset creation will offer organizations an unprecedented edge, something they never had before. Industries such as digital commerce and retail are all set to see significant changes in the way they manage their digital assets.
The powerful asset customization capability without an expert’s help will be a game-changer for DAM. Deployment of Gen-AI within content creation workflows has already begun and user prompts or user queries are being used to ask Gen-AI to come up with desired outputs. With every prompt, the query gets more defined, and the Gen-AI tools get even more sophisticated. In summary, DAM has a lot to gain from Gen-AI, as it stands to become from an asset management tool to a one-stop shop for content generation, collection, management, and storage. And it spells good news for organizations of all industries and sizes.
