Everything you need to know about AI in Digital Asset Management

As digital asset libraries grow, managing all that content can be a headache. That’s where AI (artificial intelligence) comes in, making it easier to handle and retrieve digital assets through automation. This guide will walk you through how AI can make your digital asset management (DAM) system more efficient, what it costs, and what to expect for the future.

AI and Digital Asset Management (DAM)

AI is becoming a crucial part of digital asset management, solving problems like how to tag, categorise, and store tons of content without spending hours on manual work. Whether you're dealing with images, videos, or documents, AI can handle a lot of the heavy lifting for you.

Why AI Matters for DAM

With AI, you can cut down on time-consuming tasks like entering metadata, which helps streamline your processes. This frees up your team to focus on more valuable tasks, like improving the accuracy of the metadata and ensuring content is easily searchable.

What’s AI and How Does it Work in DAM?

What is AI?

AI is essentially getting machines to do tasks that usually require human intelligence, like learning, problem-solving, and decision-making. In DAM, AI helps identify images, transcribe audio or video, and even tag files automatically, which can save a lot of time.

The Different Types of AI for DAM

AI in DAM systems typically involves machine learning, natural language processing (NLP), and computer vision. These technologies allow the system to “see” and “understand” what’s in your assets, making it easier to organise and retrieve them.

Generic AI vs. Custom AI

Generic AI is what you get from big providers like Amazon or Microsoft, where the tools are designed to handle a wide range of tasks. Custom AI, on the other hand, is tailored to your specific needs, like recognising your unique brand assets or industry-specific images.

Big Players in AI for DAM

The heavyweights in the AI space—Amazon, Microsoft, Google, and IBM—are the go-to for AI tools in DAM. These companies offer powerful tools for tasks like facial recognition, transcription, and object detection that can be easily integrated into your DAM system.

AI and Business Process Automation (BPA)

AI often overlaps with Business Process Automation (BPA), helping to streamline repetitive tasks. This means you can automate processes within your DAM system, saving you time and resources.

The Costs of AI in DAM

Built-in AI vs. Third-party Solutions

Some DAM systems come with AI built right into them, while others require you to add it on through a third-party provider. Built-in AI is usually easier and cheaper to implement, but third-party solutions might offer more specialised features, though at a higher cost.

AI costs for Digital Asset Management (DAM) can vary quite a bit depending on the features and level of automation you're looking for.

AI in DAM Costs breakdown

1. Basic AI Features: These usually include things like automatic tagging, metadata generation, and simple search capabilities. The cost for these types of AI integrations can be included in a standard DAM platform subscription. Many platforms bundle basic AI at no extra charge, especially if they are using existing AI services from companies like Google or AWS.

2. Advanced AI Features: When you get into more complex AI tasks, like visual recognition (e.g., identifying objects in images or videos), facial recognition, and deep search capabilities, costs start to rise. Some DAM systems charge extra for these features, either as an add-on or based on usage (pay-per-use or per asset processed). This can range from a few hundred to several thousand dollars a year, depending on how often you’re using AI tools.

3. Custom AI Models: If you need a custom-built AI model—let’s say you want it trained specifically to recognise your industry-specific content or workflows—the cost can really spike. Developing and maintaining a custom AI solution for a DAM system can run into the tens of thousands of dollars, with additional ongoing costs for updates and maintenance.

4. Cloud vs. On-Premise: The costs also vary depending on whether your DAM is hosted in the cloud or on-premise. Cloud-based DAM solutions generally charge on a subscription basis, and AI costs are usually tied to your monthly or annual fee. On-premise solutions might have a higher upfront cost to install the AI capabilities, but they might save money over the long term depending on your usage.

In short, basic AI in DAM systems can come at no additional cost or a modest subscription fee, but advanced features and custom solutions can get expensive. It’s important to weigh how much automation and intelligence you need versus your budget for AI.

Curious how much an AI-powered DAM can cost?

Security with AI in DAM

Keeping Your Data Safe

When using AI, you need to make sure your data stays secure, especially if you’re handling sensitive content. It’s important to know whether your assets stay within your DAM environment for processing or are sent to external servers, which could pose privacy risks.

In-house vs. External AI

Processing assets within your DAM’s existing infrastructure is usually safer than sending them to third-party servers. Always check the data retention policies and privacy measures of your AI providers to make sure your content stays secure.

AI Tools You’ll Find in DAM

Natural Language Search

The latest advance in AI for DAM skips the tagging and metadata process by delivering search results from natural language inputs, recognising the language search elements in images and videos. 

Smart Tags and Object Recognition

AI can automatically recognise objects in your images and videos, creating “smart tags” that help categorise your assets. This can significantly speed up the tagging process.

Facial Recognition

Facial recognition can automatically tag people in images or videos, but it’s not always perfect. You’ll still need some manual checks to avoid mistakes, like false tags.

Colour, Text, and OCR Scanning

AI can also detect colours, text, and use Optical Character Recognition (OCR) to read text from images or PDFs, adding more useful metadata to your assets.

 

See Canto AI features in action.

Automating Metadata

One of AI’s biggest strengths in DAM is automating basic metadata entry. Instead of manually tagging every asset, AI does most of the work for you, freeing up your team for higher-level tasks.

Quality Control and Moderation

While AI is great at tagging, it’s not perfect. You’ll still need your team to check and refine the metadata AI generates to ensure everything is accurate and relevant.

Download the free eBook: Adopting Artificial Intelligence as The Cornerstone of Your DAM Project here

Custom AI Learning for Your DAM

What’s Custom AI Learning?

Custom AI learning is when you train an AI system to recognise things specific to your business, like particular products or brand assets. This is more advanced than generic AI and can offer huge benefits tailored to your needs.

Costs, Risks, and Benefits

Custom AI learning can be expensive, but it can deliver significant value if you have a unique need. However, there’s always the risk that it could become outdated if generic AI capabilities catch up, or that the project could fail if it’s too complex.

Examples of Custom AI Projects

A great example of custom AI is The Australian Ballet’s project, where they use AI to recognise costumes, poses, and performers. Another example is retail businesses using AI to match product names with images.

AI-Generated Content and Copyright

In Australia, using AI in content creation and management brings up tricky issues around copyright, ownership, and fair use. Since language models can produce human-like text and analyse huge amounts of data, they’re pushing the boundaries of what we think of as authorship and intellectual property rights.

Attribution and Licensing

When AI is used to enhance searchability or retrieval of content in a DAM system, ensuring that the underlying content is properly attributed and licensed remains important. Even though the DAM system isn't creating new content, the copyright rules governing the assets it manages must be respected, and proper access and usage rights applied.

Moral rights and Authorship

Since DAM systems handle assets created by humans, respecting the moral rights of the original creators—such as crediting the authors—remains a key concern. AI tools used for categorisation or management must be designed to preserve such attribution, especially in cases where the AI's metadata might obscure or alter the original authorship details. AI ethics also plays a crucial role in ensuring these systems are developed responsibly, safeguarding the rights and recognition of creators throughout the process.

How to use Large Language Models (LLM) in DAM

What is LLM?

Large Language Models (LLMs) are a type of AI built to understand and generate text that sounds like it was written by a human. These models, like OpenAI's ChatGPT-4, are trained on huge amounts of data that cover different languages, topics, and writing styles. Using deep learning, specifically neural networks, LLMs can predict what word should come next in a sentence. This allows them to do a variety of language tasks, such as writing text, translating between languages, analysing sentiment, and summarising information.

How does LLM help digital asset management?

The introduction of Large Language Models (LLMs) has brought new possibilities into the world of DAM. By tapping into the power of advanced AI, LLMs can revolutionise how digital assets are organised, accessed, and used, offering unmatched levels of automation, accuracy, and personalisation. This integration not only simplifies workflows but also improves the user experience, making it easier to manage and make the most of digital assets. 

Possible applications of LLM in DAM:

1. Automated Metadata Tagging
   For example, when uploading a product photo, the LLM could automatically tag it with relevant information like product name, colour, and context, saving time on manual entry.

2. Content Search and Retrieval
   For example, instead of searching for an exact file name, users can type a descriptive phrase like, "Show me the latest social media graphics from our campaign," and the system will retrieve the relevant assets.

3. Content Summarisation
   For instance, if an organisation stores webinar recordings, the LLM can provide a brief summary, making it easier to review content without needing to go through the entire recording.

4. Translation and Localisation
   For example, when a marketing team uploads a campaign video, the LLM could automatically generate subtitles in different languages, ensuring the content is accessible to a broader audience.

5. Enhanced User Experience through Chatbots
   LLM-powered chatbots can help users navigate a DAM system more efficiently. A user could ask the chatbot to find specific content or suggest assets for a particular project. For instance, "Can you find last year’s annual report in PDF format?" would trigger the chatbot to search for and display the requested file.

6. Content Creation Assistance
   LLMs can assist in content creation by providing text generation capabilities directly within the DAM system. For example, if an organisation stores product images, an LLM could help generate product descriptions or marketing copy, reducing the time spent on content creation.

Learn more in our comprehensive article: How To Use LLM in Digital Asset Management.

AI and Ethics

The Australian Government has introduced eight AI Ethics Principles to help ensure AI technologies are used safely, securely, and responsibly. These principles are all about making sure AI systems are created and used in ways that reflect our human, societal, and environmental values. Similar to the Essential Eight framework for cybersecurity, the AI Ethics Principles offer a clear structure that organisations can follow to get the most out of AI while keeping potential risks in check.

Here are the 8 principles:

1. Human, Societal, and Environmental Wellbeing 
2. Human-Centred Values
3. Fairness
4. Privacy Protection and Security
5. Reliability and Safety
6. Transparency and Explainability
7. Contestability
8. Accountability

Australian AI principles are described in more detail here: AI Ethics in Digital Asset Management: A Guide To Safe AI Use

AI: Enhancing or Replacing Processes?

AI as a Support Tool

AI is great for enhancing your current processes, but it doesn’t fully replace human work. It can take over the repetitive tasks, like tagging, so your team can focus on more valuable work.

Automating Repetitive Tasks

Tasks like tagging and transcription are perfect for automation. AI can handle these, freeing your team to focus on quality control and improving metadata accuracy.

Getting Contributors Involved

AI can also make it easier for contributors to upload and tag their assets, reducing the manual effort required on their part.

The Future of AI in DAM

What’s Coming in AI

As AI continues to evolve, we’ll see more accurate and useful tools for DAM systems. Expect improvements in natural language processing and machine learning that will make it even easier to organise and find content.

AI and Long-term DAM Success

AI’s impact on DAM systems isn’t just about efficiency today—it also helps ensure the long-term success of your project by making content easier to manage and search, which boosts user engagement over time.

Conclusion

AI vs. Manual Metadata Entry

AI will never fully replace manual metadata entry, but it can handle a lot of the more basic tasks, freeing up your team to focus on higher-level work. You’ll need both AI and human expertise to get the best results.

Wrapping Up

AI is a game-changer for DAM systems, automating repetitive tasks, improving metadata accuracy, and boosting efficiency. But like any tool, its success depends on how you implement it and whether it fits your business needs.

Disclaimer:

This content may have been created with the assistance of AI technology for accuracy and efficiency.