There are many roles in marketing that, right now, AI can’t replace – things like original creative and strategic thinking and relationship building. But using AI to handle creative assets within a Digital Asset Management (DAM) system is a whole other story. Here’s how AI can transform a DAM, making it smarter, faster and more effective.
AI features in a DAM
Automated tagging with AI
Prior to AI, one of the most labor-intensive tasks involved in managing digital assets was having to manually tag them. AI has significantly reduced this burden by automatically adding searchable tags to photos, videos, and audio files based on a range of criteria:
- Facial recognition: AI algorithms can recognize faces in images and automatically tag individuals. This makes staff or talent image searches infinitely easier.
- Object recognition: AI automatically identifies objects within an image or video making it easier to categorize and perform a granular search for specific content. It even allows you to search by color.
- Brand logo recognition: AI automatically has the ability to recognize and tag assets containing logos. This makes for far easier asset retrieval – especially when you are managing a number of sub-brands.
- Location/Landmark recognition: AI can identify landmarks or locations within images and video, helping you categorize assets based on their geographical classification.
Speech-to-Text in video and audio
Benefits of using AI features in DAM
- Faster tagging: Manually tagging each asset is time-consuming. With AI, this task is done for you, allowing you to quickly categorize and retrieve assets. Of course, you can always manually add extra tags if you want to add more information.
- Reduced repetitive tasks: Imagine having to download or share 100 images that need cropping to a particular size specification for web or socials? Using AI to crop images automatically eliminates the need to perform this task individually, saving you hours of work.
- Instant closed captions: In the past, creating closed captions for videos and audio could take hours. With AI, this process is reduced to a matter of seconds, making your content more accessible to a broader audience. This is invaluable, particularly if your company has a large bank of archives.
- Improved asset search speed: AI-driven tagging enhances the speed and accuracy of asset searches. Find what you need within seconds, rather than sifting through a sea of untagged files.
- Enhanced efficiency: By automating time-consuming tasks, a DAM with AI capabilities frees up your team to focus on more strategic and creative endeavors. Instead of getting bogged down in manual tagging or cropping, your team can invest their energy on higher things.
- Typos or misspelled names: Humans are only human. We all make mistakes, but even small typos can result in assets being misclassified or becoming difficult to locate during searches. AI, with its automated and predefined tagging rules, ensures that tags are applied correctly without any spelling mistakes.
- Inconsistent tagging: Human taggers may apply tags inconsistently across assets, leading to confusion and reduced search accuracy. For example, one person may tag a series of similar images differently, while another may use synonyms interchangeably. AI maintains consistent tagging criteria across all assets, ensuring uniformity.
- Misinterpretation: As humans, we might misinterpret the context or content of an asset, leading to incorrect tags. AI relies on data-driven algorithms and pattern recognition, reducing the risk of inaccuracies.
- Incomplete tagging: Manual tagging can sometimes result in incomplete or missing tags. A human tagger may overlook certain aspects of an asset or omit relevant keywords.