Artificial Intelligence in Digital Asset Management: Pros and cons


Artificial Intelligence is heralded as the next advance in Digital Asset Management - promising faster ingest and more meaningful metadata among the many benefits.

AI is big news in all sectors. IBM’s Global AI Adoption Index 2022 finds the global AI adoption rate has risen to 35% - a four-point increase from 2021 - and generative AI is one of Gartner's top strategic technology trends for 2022.

Artificial Intelligence is being built into DAM systems and widely offered through third-party plug-ins like Google Vision, Amazon Image Rekognition, and Clarifai. But with the technology still relatively new, is now the time to invest in adding AI to your DAM?

We’ve pulled together some of the main benefits - and barriers - to the use of Artificial Intelligence in Digital Asset Management in 2022.

(This is the second part of our series "Artificial Intelligence in Digital Asset Management". To read the first part, click here.)

AI in DAM: the benefits

If you’re not familiar with how AI works in Digital Asset Management, you’ll find it helpful to read the first article in this series Artificial Intelligence in Digital Asset Management: How does it work and do I need it?

That will explain what AI in DAM can do - such as auto-tagging, subject recognition, intelligent cropping, compliance monitoring, and more.


What are the main benefits of AI in DAM?

The main benefits of Artificial intelligence in DAM typically fall into the following categories. Improvements to:

  • Operational, processes and workflow efficiency - by accelerating and automating workflows - from asset ingest to creative processes
  • DAM user experience - by making your DAM system easier and more productive to use
  • ROI - by making assets more discoverable and supporting re-use

All of which are advantageous to organizations where Digital Asset Management is a business-critical process. Let’s look at a few in more detail.



The primary benefit of using AI in DAM is that it can make your Digital Asset Management processes more scalable. When you’re processing - potentially - millions of images and video, it simply isn’t possible to get human eyes on every digital asset.

Manual processes can choke your digital asset management systems, causing major bottlenecks to upload, approval and use. AI can eliminate the need for congestion-causing manual processes like

  • Selecting and tagging digital assets with keywords
  • Checking uploads to check quality and compliance
  • Editing and cropping digital assets

For example, manual application of metadata can be slow, and prone to human error and inconsistencies. AI doesn’t have this problem. AI can ingest and tag your uploads accurately 24/7. This doesn’t just accelerate processes, it makes them scalable. By removing the human element, you can go from processing hundreds to millions of assets in the same amount of time.

Being able to trust AI - to do things like apply appropriate tags, crop images intelligently, and filter out non-compliant uploads - lets you scale up with confidence. It also frees your team from time-sinks, so they can reclaim time for more creative and strategic work.



There are lots of use cases where using DAM isn’t just efficient, it’s essential; where the ability to access and leverage digital assets can be the difference between failure and success.

Ecommerce, marketers, publishers, and agencies all rely on fast and accurate digital asset management to deliver their services. And that’s the crux of the AI opportunity in DAM - improving and accelerating the processes that DAM is designed for.

Take designers. They can lose hours every week, playing hide-and-seek with digital assets. Are they on someone’s desktop? Attached to an email? Hidden in a subfolder with an unguessable file name?

Simply having a DAM system helps alleviate that by centralizing digital assets and attaching metadata that makes them easier to find. But adding AI to your DAM improves things further by

  • Adding MORE metadata than a human could - so assets are more discoverable than ever
  • Automatically creating searchable transcriptions of video uploads - so you can find the exact excerpt you need - in an instant
  • Adding visual search ability - so you can upload an image and see if your DAM has something similar

Ultimately, AI makes your digital assets more discoverable. This makes it easier for users to surface assets that meet their needs - speeding up content creation processes and increasing the ROI on your commissioned assets.



Another - often overlooked - benefit of augmenting your DAM system with AI is that it makes your DAM easier and more productive to use. Even without AI, DAM is a workflow game-changer. But with AI, things get even quicker and more convenient - which means your time-poor team will love it even more. For example

  • Making assets instantly accessible from a much wider range of search terms, so you’re more likely to find what you need first time
  • Weeding out duplicates and old assets automatically, so people aren’t wading through lookalike assets to find what they want
  • Surfacing existing content as a fast track to creating new assets more efficiently
  • Accelerating and automating workflows, so your team is more productive and less stressed

These improvements to user experience help maintain high adoption and usage rates - which means you’re more likely to reap the system ROI you’re banking on.

Discover Next-gen DAM integrations: Integrating DAM using iPaaS and more

Download our at-a-glance guide to find out more about DAM integrations - and how to select the right integration tools for your use case.

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AI in DAM example: e-commerce image automation

A growing e-commerce brand sells fashion products from a range of suppliers. In a highly visual market, their sales depend on high-quality product imagery and video. Cross-selling drives higher revenue by letting shoppers ‘buy the look’ instead of single items. Here’s how deploying AI in their DAM could help them scale.

The brand has commissioned a photoshoot for its Fall season highlights. In each photo, a model wears a range of clothing items, jewelry, and footwear. They’ve trained their AI to recognize each of those items and add the SKU to the image metadata in their DAM.

The DAM is synced to their PIM system. Adding the SKU in the DAM triggers a process to access all of the related information about that SKU from the PIM - and add it as metadata to the image.

Information such as the product name, range, collection, availability, sizes, fabric, price, etc.

Every image from that photoshoot is now tagged with meaningful metadata, making those images infinitely more discoverable and usable.

So if a designer looking for images to illustrate ‘dresses below $40’, they could search for photos showing the items from the current collection under $40. They don’t need to cross-reference to other databases or ask colleagues - they know they can rely on the information that’s been synced across from the PIM.

Or, imagine you’ve published a photo on a product page. Let’s say it’s for a leather jacket. Your CMS can use the metadata available in the images to pull in ‘related items’ that link through to other clothing within the photo - like the shirt, shoes, and belt - creating cross-selling opportunities.

[Read more about the benefits of linking your DAM and PIM systems]

AI in DAM: the challenges

Of course, it isn’t all plain sailing. AI is in its infancy and the technology isn’t perfect yet. Here are some of the challenges you might encounter with AI-powered Digital Asset Management.


AI in DAM is still learning

Your DAM AI plug-in can only tag subjects it’s already learned about. This means it’s great on common subjects - like landmarks, famous faces, flora and fauna, weather, and colors. But it might struggle with more subtle or abstract subjects. This is especially true if an image is particularly complex and includes a lot of detail/different objects.

AI in DAM is generic

AI is trained to recognize generic subjects that are common to all experiences, cultures, and businesses. But it doesn’t know anything about your specific business - yet. You’ll need to train it. And that takes time and expertise.

For example, imagine you sell pottery. Your AI might recognize several different products as ‘plates’ but lack the nuance to tag them as side plates, dinner plates, porcelain, or stoneware… In the context of your DAM system, having lots of different products tagged as ‘plates’ - when you want to find images of a porcelain side plate - isn’t very helpful.

AI and DAM master lists don’t always mix

If your DAM uses a master keyword list, you may struggle to get it to accept the auto-tags generated by your AI. If you’ve locked down a controlled vocabulary for keywords - and your AI is suggesting tags that aren’t on the list - this can prevent them from being added. One way around this is to have two separate keyword fields, one for manual entry from your master list and one for keywords generated by your AI.

AI in DAM gets it wrong

AI and human image recognition are very different. Whereas humans understand the content of an image, AI just recognizes patterns in the pixels. That means it can make a strong guesstimate as to the content - but it won’t always get it right.

To prevent your search results from getting cluttered with irrelevant assets, you may need to edit and remove auto-tag errors. In a larger enterprise, this could require an additional team member to quality-check the AI tagging.


Sometimes VERY wrong

Sometimes, AI tagging doesn’t just result in mislabelled digital assets and the inconvenience they cause. Sometimes AI tagging can reflect biases and errors found in real life which, if admitted into your DAM unquestioned, may cause deeper issues.

For example, when this writer used a stock photo library to find images of breastfeeding, it was evident that AI tagging was in play. As well as appropriate keywords - such as motherhood, feeding, and infant - the images had been erroneously tagged with sexual keywords as well. In this case, we can assume the AI had correctly identified the anatomy but failed to understand the context.

More worryingly, some AI has been found to perpetuate racist and sexist prejudice. Not something any Digital Asset Manager wants to introduce into their business.

Is AI-powered DAM worth it?

Weighing up the benefits and challenges of AI in DAM, you may not be convinced that you need it yet.

As you’ll have gathered from the challenges above, AI isn’t always a plug-and-go product. Depending on your use case, you may need to invest time and resources to train it to work effectively for your organization.

With the technology still in its infancy, there are certainly still glitches to iron out. But we believe - overwhelmingly - that AI and DAM combined will be a powerful force in the future.

As AI technology and machine learning improve, they have the potential to transform how businesses manage their digital assets - allowing for infinitely scalable processes that let you grow your business without growing staff overheads.

AI-powered DAM will become a key lever of operational efficiency in any digital asset-heavy business model - such as publishing, e-commerce, entertainment, and more. The question is: where will your business be on the adoption curve - an innovator, early adopter, or laggard?

You’ll discover a wealth of AI tools built into WoodWing’s two Digital Asset Management systems - WoodWing Assets and WoodWing Swivle. To talk about the benefits of AI-assisted DAM for your use case, book a call back now.

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Tom Pijsel, VP Product Management

Tom loves to solve complex software challenges. Working together with the WoodWing Studio and Assets product teams, he creates solutions that support publishers, agencies and marketers in their daily work. Tom loves to spend time outdoors with his family. And when he needs to clear his head to make room for new ideas, he’ll put on his running shoes.