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Artificial Intelligence in Digital Asset Management

Each year, the number of digital assets (videos, pictures, images) managed by any given organization grows. You need not only to archive and maintain the existing assets but constantly create new ones.

Consequently, managing these assets becomes more and more challenging. Tools such as digital asset management (DAM) certainly contribute to making the asset management process easier. After all, DAM has been proven to save each employee at least 10 hours/mo that they would have otherwise spent looking for assets.

Yet, the DAM system is just a tool, a tool that requires users to do things manually, even if streamlined.

That is why over the past few years many DAM vendors, Pics.io included, have introduced AI features to make the management of an asset library so much easier and satisfying.

In today's article, we'll talk about the benefits of artificial intelligence in digital asset management, and how Pics.io specifically utilizes it to make users' journeys more enjoyable. Let's dig in.

Benefits of AI in Digital Asset Management

So, what does AI digital asset management entails?

At its core, digital asset management is about a few main things: organizing and finding assets in your library.

By incorporating artificial intelligence, the DAM system seeks to reinforce these capabilities and make them easier to scale for media libraries that span multiple thousands of digital assets.

In more practical terms, AI technology lets you automatize metadata tagging and describing the assets.

When you have thousands of assets it is highly unlikely that they all have proper metadata in place - Keywords, descriptions, custom metadata fields - the things that make the search work.

Going through each asset and manually adding metadata to all of them is time-consuming and borderline impossible.

By delegating this job to AI algorithms it will be easier to find, organize, and distribute your assets in a sliver of time than it would have taken you otherwise.

This also eliminates the inevitable human input errors that would have occurred with manual input. For a human, tagging one asset with The United States and The U.S. doesn't have a noticeable difference.

However, if you were to try to find all similar assets later, inconsistent tagging would leave you with incomplete and potentially unsatisfactory search results.

To conclude, we can summarize the main benefits of AI in DAM as

  • Automated tagging, image recognition, and metadata management
  • Improved searchability and discoverability of assets
  • Reduction of manual labor and human error
  • Optimization of workflows and increased efficiency
  • Enhanced security and compliance
  • Saved time

AI Technologies Used in DAM

For those curious about the technical underbelly of AI technology, let's look at the 3 main technologies that are colloquially referred to as AI.

  1. Machine Learning (ML) - ML is a very broad category of AI, essentially an algorithm that has the capacity for self-improvement. By training an ML algorithm on a specific set of data, it will be able to discern its patterns and recognize and make predictions about similar data sets in the past. To put it simply, if you were to train it on images of different cat breeds, it will eventually be able to discern Siamese from British Shorthair and such.
  2. Natural Language Processing (NLP) - NLP is a more niche thing that focuses specifically on analyzing and understanding human language. ChatGPT is an example of an NLP model that can understand and process text while also being able of writing its own. In theory, combining image recognition + NLP can allow you to create legitimate descriptions for your images.
  3. Computer Vision - Computer vision can be used to analyze the visual content of digital assets, such as images and videos, to automatically tag and categorize them.
  4. Deep Learning (DL) - DL focuses on using neural networks with many layers (hence "deep") to model and understand complex patterns in large amounts of digital assets. In DAMs, this technology is used to “understand” complex data and improve keyword tagging, for instance.
Both ML and NLP have found its uses in AI digital asset management setting
While there's an overlap between NLP and ML, they are ultimately used for different ends. Source

Examples of Successful AI Implementation in DAM

Pics.io DAM system boasts two major fusions of AI and digital asset management in a singular workflow.

AI Keywording with Computer Vision

First, you have AI keyword generation, powered by computer vision. Keywords are one of the main metadata categories in DAM and the tool that users turn the most to when they need to quickly find assets through advanced search.

The problem that AI keywording solves is quite common – when you have numerous assets in your digital library it can be difficult to properly tag all of them manually.

Using AI algorithms allows you to bulk tag multiple assets, thus significantly reducing time spent on enterprise metadata management.

Pics.io has recently started using an AI model that generates 63% more relevant keywords for images compared to the previous technology. As you can see in the screenshot below, the Pics.io improved AI auto-tagging feature offers tags that describe the context of the picture and the processes instead of simply applying generic keywords. 

No wonder that the new version of AI-enhanced keyword tagging in Pics.io shows better results and provides keywords that are directly related to what’s happening in the image.

You must agree that nobody will search for the picture of the pasta-rolling process described using such keywords as “machine”, “engineering”, “metal”, etc. Conversely, it’s obvious that keywords like “rolling pin”, “chef”, and “flour” are more relevant indeed. 

In the green box, you can see keywords generated by the new AI model, while the red box demonstrates keywords generated by our previous AI model.

Face Recognition in Digital Asset Management

Face recognition is a bit of a touchy subject in the AI scene because of the obvious implication of data security.

After all, it would be a breach of privacy if there was a privately owned database of people's faces that AI can pick out and recognize from any picture. This is why tech companies tend to abstain from such implementations; it's a bigger legal headache rather than a boon.

So, in what way DAMs, then, integrate facial recognition?

In simple terms, the DAM solution creates a local database of faces inside your DAM media library. And, what's more important, it doesn't remember or know anything before you tell it who's who.

Even if you were to show it the face of a famous person, it wouldn't be able to tell you if it's Lionel Messi or John Doe.

Instead, what most DAM systems do is as follows:

  1. The user asks AI to scan a given image for faces
  2. It detects and highlights all faces in the image
  3. The user assigns a name to a face
  4. DAM then assigns that name to the same face it had identified across multiple pictures
  5. A user can search through their library by that metadata
Face recognition is one of the newer and more exciting utilizations of AI in DAM setting
Discover Pics.io's Face Recognition

That's the gist of it but you can read a more elaborate and in-depth explanation of face recognition in one of our articles.

The point here is that it obviously limits the AI's capabilities. So the question is, then, who will benefit the most from AI face recognition in the DAM solution?

The list is non-conclusive but we'd say that modeling and talent agencies, media and press, and any other team that needs access to images of the same people on regular basis.

Best Practices for Implementing AI in DAM

So, are the AI-powered tools the right call for you?

After all, AI functionality isn't necessarily free.

Because of the processing power required to process and analyze images, each time you want to analyze a batch of images, there will be a certain per-asset price attached to it.

It's not anything groundbreaking, mind. You can always check out the exact cost on our pricing page.

But, nonetheless, it's something you have to factor into your budget and determine if implementing artificial intelligence into your digital asset management is worth it.

So, let's look at the step-by-step thought process:

  1. Identify use cases. Do you have a use case for AI tools? For example, what if you have thousands of assets just recently migrated to DAM meaning that they don't have any metadata on them. Or, perhaps, are you running a talent agency and need quick access to photos of your clients?
  2. Audit your existing library. Look at your existing library, how many assets do you have? How many of those would you need to process with AI? And, will artificial intelligence actually help you in a meaningful way? Because if you need AI tagging your needs are so specific that computer vision might not handle it properly, then it might not be worth it in the long run.
  3. Do a test run on a small batch of assets. You can use it on your existing account or use a free trial. See how AI handles things, and assess if you are receiving the expected outcomes. Using a test run will help you identify and solve any potential problems with implementation and determine the validity of the AI use case.

If, after following all these steps, you determine that AI is the right thing for you, you can start implementing it into your greater digital asset management strategy.

At the moment, we are only at the precipice of AI's potential. As it continues to develop, DAM will be able to absorb more and more of its capabilities to create the ultimate single source of truth for your digital assets.

So, what does the future holds for us?

Improved Metadata Generation and Management Capabilities

Although computer vision is already a powerful tool, it's far too generalized to create metadata as high-quality as that created by humans.

Broader access to personalized models that can be trained on DAM users' datasets and integration of NLP + image recognition can allow us to create detailed descriptions and metadata for all your assets. And that metadata will understand the context in which you operate, so it won't give you information that you don't need.

AI-Powered Analytics

Digital asset management isn't just for managing and storing assets but it's also for distribution.

As you upload collections of assets to the web and share it with stakeholders, it would be worthwhile to learn what assets are in high demand and which ones are underperforming.

Using AI, we will be able to parse all of that information in mere minutes and receive actionable tips on how to improve our existing assets.

AI Image Generation

DAM already lets you modify existing assets straight from your media library. And it's a small step from just editing the existing images to creating new ones.

AI models like DALL-E and Midjourney had already proven that they are more than capable of creating quality assets from scratch, provided you know how to engineer prompts well.

Having a similar capability integrated directly into your DAM gives you unlimited potential as far as the generation of new assets for any occasion is concerned.


AI-powered personalization involves tailoring digital experiences to individual users by using data-driven insights and machine learning algorithms.

AI can be used to automate digital assets recommendations, for instance. By analyzing user behavior, preferences, and interaction history, AI can suggest the most relevant digital assets to each user. For instance, marketing teams can automatically receive suggestions for images, videos, or documents that are most likely to resonate with their target audience. This process is similar to Spotify music recommendations based on what you’ve been recently listening to. 

AI-driven personalization also extends to predictive analytics, where AI can forecast future content or digital assets needs based on current trends and usage patterns. This should be an immense help when crafting a marketing strategy, for example.


Wide implementation of AI technology will revolutionize all industries and can open doors to many new possibilities.

It's not a surprise, then, that the DAM niche is embracing this new development by already implementing AI into its functionality.

Obviously, in its current iteration, the capabilities of AI in digital asset management AI might not be for everyone and there are problems it cannot yet save. However, for those teams that will actually benefit from it - those that have a large number of assets without metadata or those working with faces - it will be a huge boon.

So, if you are curious to see what the future holds, you can check out DAM's AI capabilities today using our free 7-day trial.

And, if you're curious to learn more, don't hesitate to book a free demo with us!

Did you enjoy this article? Give Pics.io a try — or book a demo with us, and we'll be happy to answer any of your questions.

Pics.io Team
Welcome to Pics.io blog, where you'll get useful tips, resources & best practices on how digital asset management can help your business to manage & distribute digital content on top of cloud storage.