By now, you’ve probably heard of ChatGPT, a generative artificial intelligence (AI) chatbot designed to simulate human conversation through text or voice interactions. If so, your co-worker, boss, parent, child, or favorite LinkedIn influencer has probably told you about some of the interesting ways they use the tool to make their lives easier. Generative AI like ChatGPT isn’t just a fun conversation starter, however. By all indications, its role will only grow because they have the potential to revolutionize various industries by automating complex tasks, generating creative outputs, and improving the overall user experience.
In this article, we’ll explore the present and future impact of generative AI on digital asset management (DAM) with the help of Aprimo, one of our partners and a leader in the DAM, Productivity Management, and Marketing Operations spaces.
To do this, we’ll begin by defining what Generative AI and DAM are. Generative AI refers to a machine learning technique that enables computers to generate data that mimics human input. This technology uses algorithms and data sets to create new content, such as images, videos, and text, that can be difficult to distinguish from human-created content.
Digital Asset Management (DAM) refers to the process of organizing, storing, and retrieving digital assets such as images, videos, and audio files. DAM systems help organizations manage and distribute their digital assets effectively. Generative AI, on the other hand, is a new and disruptive way to create them.
Impact of Generative AI on Digital Asset Management Today
While Generative AI may seem like a new and relatively fun tool, it’s already disrupting the way organizations think about creating content at scale in a way that few technologies ever have. Throughout this article, we'll include sections from Aprimo’s robust Generative AI Guidebooks (linked below). If you want to learn more about how Aprimo is integrating Generative AI into its systems and tools to make using this cutting-edge technology easier for its customers, read more!
Automated Tagging and Categorization
One of the primary challenges in DAM is categorizing and tagging an organization’s assets with metadata accurately and according to its taxonomy. Generative AI helps overcome this challenge by providing automated metadata tagging.
With generative AI, organizations can train algorithms to analyze the content of their assets and assign relevant tags and categories automatically. At present, training AI models takes time and requires human oversight. When done right, however, this feature saves time and eliminates human errors in the tagging process, making asset management more efficient and search results more successful.
Content Creation and Augmentation
Generative AI also offers organizations the ability to create and augment their digital assets automatically. Using generative AI, they can generate new assets based on existing ones. For example, an AI algorithm can analyze an image and generate several versions of it, such as different color schemes, cropping, or orientation.
This feature is particularly useful for organizations that require different variations of the same asset for different purposes, such as social media or advertising campaigns. As Aprimo says, “...you may be able to automate content variations targeted at different personas with specific campaign messages, all while pivoting for various channels (your Twitter post is going to need to be a lot shorter than your blog copy!)”
One of the key benefits of an effective DAM program is that your brand identity is easier to consistently maintain across all different channels and content types. The ability to quickly generate content based on existing assets is another step in the right direction.
Improved Search and Retrieval
Another significant benefit of generative AI in DAM is improved search and retrieval. With automated tagging and categorization, organizations can search for specific assets more efficiently. Additionally, generative AI can analyze the context of the asset and suggest similar assets, making it easier to find and retrieve related content.
Metadata can also provide information about how both human-generated and AI-generated content is performing. For this reason, it’s important to attach tags that identify AI-generated content.
Aprimo provides some advice about this, saying, “Tagging content that was produced by generative AI and knowing which tools were used can be greatly beneficial. Consider setting up a custom “AI Influenced” field on content projects as a simple “Yes/No” dropdown, and an additional field to let a user specify which generative AI was used. By marking which content is being improved by AI, you gain a bird’s eye view of which sets of content in your DAM is being improved by AI and which are not, identifying opportunities where you can generate content faster and at scale.”
Future of Generative AI in Digital Asset Management
The use of generative AI in DAM has enormous potential, and its use cases are continually expanding. Here are some applications of generative AI in DAM we can expect in the future.
Generative AI can help organizations personalize their assets for individual users. Using AI, they can generate custom images, videos, or audio files based on the user's preferences or behavior. This feature could be particularly useful for organizations in the e-commerce industry, where personalized content can drive sales.
Maintaining brand identity, voice, and image consistency is key to an organization’s success. Once generative AI can consistently learn an organization’s brand identity and replicate it across content it's creating and altering, as Aprimo says, “It would understand what your brand values are, what your brand voice sounds like, the tone of a campaign you’d like to run, and what good content looks like [for your potential customers], so it can produce high-quality content with less and less human refinement needed.”
Real-Time Asset Generation
In the future, organizations may be able to generate digital assets in real-time based on user demand. For example, an e-commerce website could generate custom product images for each user, based on their search history and preferences, all without needing an in-depth, specific prompt from a user inside the organization.
At the moment, prompts from users are the key to effectively deploying generative AI within your content creation workflows, just as a good search prompt is key to finding what you want in Google. Below, Aprimo shares some useful tips for using your DAM platform to provide these prompts to campaign managers and content creators.
“A prompt is the text you use to ask a generative AI tool to produce an output. ChatGPT is extremely powerful thanks to contextual understanding, which means that you can give ChatGPT a prompt, and if you aren’t happy with the output, you can ask it to refine that prompt. But how do you ensure that the first interaction with ChatGPT can get you as close as possible to the kind of output you’re looking for? That’s where the art of prompting comes in.
The more detailed your prompt, the better generative output you can get…To get generative AI to give you solid, on-brand messages across every channel, give the suggested prompts to your users via Aprimo. By supplying your marketers with the campaign brief in Aprimo, they are empowered to get amazing output from the get-go.”
Generative AI is transforming digital asset management by offering organizations new ways to create, categorize, and retrieve digital assets. With its ability to automate asset creation and improve search and retrieval, generative AI is making DAM more efficient and effective. As technology continues to evolve, we can expect to see more use cases of generative AI in DAM, providing organizations with new opportunities to manage and distribute their digital assets. If you need help building a metadata taxonomy or integrating these new tools into your workflows, contact Stacks! We help organizations of all sizes, across many industries.