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Digital Asset Management (DAM) platforms have become essential to how organizations create, store and deliver content across the nearly endless number of channels where they operate. If you’re reading this, there’s a good chance you’re either familiar with or already using a DAM. According to CMSWire research, 52% of organizations currently use a DAM, while another 34% plan to adopt one in the future. Further, 26% of leaders indicate that investing in DAM is a priority for their organizations this year.
But despite these high adoption numbers and investment rates, DAM’s expected benefits and transformative results always seem to remain just out of reach. Managers know just where DAM excels — and where they run into roadblocks. Classic challenges for DAM platforms include content discovery, metadata tagging, version control, global consistency and more.
The good news is that generative AI (GenAI), a class of AI models designed to understand, create or transform content, can now tackle many of these lingering challenges. Organizations can amplify the impact of their DAM investments by leveraging AI-driven metadata enrichment, automatic content transformations, proactive compliance checks and more.
What Are the Use Cases for GenAI in DAM?
GenAI can enhance your DAM workflows in a number of different ways.
1. New Asset Ideation & Creation
GenAI can quickly create fresh ideas when trained on existing brand guidelines. This helps creative teams jump-start the brainstorming process by exploring and refining AI-generated concepts, and then creative teams can bring those concepts to life.
2. Asset Intake & Preparation
By preparing assets for GenAI-based retrieval from the get-go, you set the foundation for robust findability and smoother workflows down the line. Instead of retrofitting assets that are already buried in the DAM, you can capture high-quality metadata and transform your files immediately with a proactive approach. When new assets enter your DAM, you can prepare them for Retrieval-Augmented Generation (RAG), helping to make search results faster and more accurate.
3. Metadata Enrichment & Transcription
Assets need to be fully discoverable via text-based or semantic search. Teams should be able to search for “Spring campaign hero shot with red dress” and instantly retrieve the exact video or still appropriate for their needs. But until this point, tagging and creating metadata has been a mostly manual and time-consuming task. AI-driven transcription services convert audio and video into text, while image-recognition models identify objects or people in visual assets. This data is then embedded as metadata in the DAM, aiding findability.
4. Translations, Localization & Multilingual Support
According to CMSWire research, translation is one of the fastest-growing use cases for Large Language Models (LLMs) in marketing. LLMs can translate metadata, on-screen text, documents and more into multiple languages. They can also adapt visuals, adjusting color palettes and imagery to respect regional or cultural norms. This both accelerates regional launches and ensures each market feels uniquely served — strengthening customer loyalty.
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5. Content Personalization
To strengthen customer loyalty even more, GenAI can generate unique image/text variants tailored to specific segments or individuals using user preference data. Integrating AI-driven personalization into DAM workflows allows you to deliver fresh, targeted assets at scale. This level of personalization drives engagement and conversions.
The Measurable Outcomes Possible with GenAI and DAM
DAM professionals often need to show real ROI. With GenAI, they can achieve reduced time-to-market, lower storage costs, improved engagement and greater team efficiency.
These outcomes don’t just happen magically. DAM professionals should be aware of and take steps to prevent any adoption barriers. These can include gaps in AI expertise on the team or data that’s siloed off among different teams. Having a change management plan and solid governance in place will help mitigate these challenges.
Conclusion
It’s time to move beyond mere file storage and get more value from your DAM. GenAI can ensure your content operations remain both efficient and visionary. By leveraging AI’s insights and creative ideation, you can transform your DAM from a siloed archive into a genuine engine for growth and efficiency — essential in an era where continuous innovation is critical.
Get the full list of use cases, along with key takeaways and recommendations, by downloading the full report, DAM without AI is just storage, at vertesiahq.com.
