The Incomplete Guide to AI in DAMs

We all know there’s an AI revolution going on right now, but does it overlap digital asset management? On the surface, it doesn’t necessarily relate directly - a lot of the recent buzz is about generative AI, and DAMs are more about managing than creating new assets. But when you dive deeper, AI can dramatically help you manage your assets.

Whatever platform you’re using to manage your digital assets, this is the guide for you - everything you need to know about AI in DAMs:

Why do we need AI in DAMs?

Before we go into the details, it’s important to understand why we even need AI in DAMs. There are a few main reasons:

Save time

Managing digital assets has always been time-consuming. Organizing, tagging, documenting each asset takes time. AI can help by automating many of those time-consuming tasks, making digital asset management easier and more accessible to teams with less resources.

Stay consistent

Manual work is not only time-consuming, it’s also error-prone. We all know that having many people organizing and tagging assets can lead to inconsistencies, with different people using different naming conventions. One benefit of AI is that it’s usually incredibly consistent in how it chooses to organize assets.

Achieve the impossible

Some things don’t just take time, but are nearly impossible without the help of AI. For example, finding all of the photos of an untagged person or a product out of a library of tens of thousands of files is nearly impossible without some AI assistance, as someone would literally need to go one by one to find them all.

Now that we’ve clarified the why, let’s take a look at the how:

Deep Search

Digital asset management is all about being able to easily and quickly find the asset you’re looking for. Search is a basic function, but we all know that not all searches were born equal. For example, searching on Google isn’t quite the same as searching in your laptop’s file explorer. Since search is a key tool for your searches, you want to make sure it’s able to deliver exactly what you need, whether it’s a basic or more complex search.

There are two main types of search:

1. Keyword-based search:

In this common for of search, AI performs a “keyword extraction”, basically tagging a photo as “Dog”, “Grass”, and “Sky”. It then searches through these keywords, as well as other user-generated tags and descriptions, to be able to find the right photo.

While great for basic queries, this type of search is very limited, as it struggles to find alternative queries, such as “pet running outside”, as non of these words actually appears in the extracted tags.

2. Deep semantic search

In this type of search, the AI creates a searchable representation of your images based on what’s included in the photo. This representation mimics the human way of storing information, so it feels like it “understands” the photo. The most popular example is Google Images search - you can search for various different terms and find great results, and in fact it feel like it can find almost anything.

With deep semantic search, you can look for “dog”, perform complex searches like “dog chasing a cat in a field”, or more generic terms like “pets running outside during sunset”. Deep semantic search also lets you find specific styles such as “vintage”, or photos taken by a specific camera angle, like close-ups or overhead shots.

Another benefit of deep-semantic search is its ability to work across formats. While keyword-based search usually doesn’t work well with non-photo images such as illustrations, vector art, and AI-generated images - deep semantic search, being more “human-like”, has no problem identifying them.

In summary, having a deep search really feels like having Google Images for your own database. If search is a priority for your team, make sure you test it thoroughly instead of just checking “yes” on the search column.

Custom AI auto-tagging

Auto-tagging has been widely adopted by most DAMs for year, and while useful at times, it can cause a lot of chaos too, and if you have a good search (☝), general auto-tagging actually isn’t all that useful.

But wait - does that mean auto-tagging is dead? Not at all! It can actually be tremendously useful for specific products, places, or symbols.

Say you're Ikea. Yes, a good search will help you find “chairs''. If it’s great, it might even be able to detect “wooden bar stool”. But even the smartest of AIs wouldn’t know what a “Rönninge” is, unless it’s specifically taught to do so.

While still far from widely adopted, custom AI training is a real possibility you should explore in 2024. In fact, it might be the holy grail of AI in DAMs - saving countless hours while helping you stay consistent across many thousands of assets.

A few other use cases for custom AI auto-tagging is identifying the logos of sports teams; identifying dishes of a restaurant chain, or parts of the manufacturing process at a factory.

Face Recognition for photos and videos

Facial recognition in digital asset management (DAM) automates the tagging and categorization of images and videos, making it easy to locate specific assets. In retail, it helps tag photos and videos of brand ambassadors or influencers, streamlining marketing efforts. For event planners, it quickly identifies attendees, speakers, and VIPs, simplifying post-event content sharing.

Without facial recognition, finding all photos of a specific person is nearly impossible. Given that this technology is standard on personal phones, it makes perfect sense it should be adopted for professional use as well.

Video Transcription

Video transcription in digital asset management (DAM) is crucial for enhancing accessibility and searchability of video content. Take social media, for example, where you may have dozens of similar 1-minute reels. Finding the right one can be a time-consuming task requiring you to watch each video. With auto transcription, you can simply search the text of the videos, instantly locating the specific content you need without the hassle of manual review.

On the flip side, you might have a 1-hour long interview, and you want to find a specific segment. Auto transcription will help you get there in seconds, rather than fast forwarding through the entire thing.

In conclusion, the integration of AI in digital asset management platforms is not just a trend, but a necessity for modern businesses. By automating repetitive tasks, ensuring consistency, and enabling advanced search functionalities, AI dramatically enhances the efficiency and effectiveness of managing digital assets. In 2024, you deserve nothing less.

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If you're interested in AI in DAMs, Tagbox.io will be presenting at the DAM New York 2024 conference by Henry Stewart Events on Oct. 23-24, and showcasing our next-gen DAM with unbeatable AI.

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