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How Does AI in Digital Asset Management Work?

A keyboard with a glowing "AI" button.

Automated aspects of DAM workflows have been around for some time, but they relied on predefined triggers and actions that had limited potential outside of their specific scope. While these automations excelled at streamlining specific parts of DAM workflows, many businesses hoped for a technology to assist with an increasing volume and complexity of digital assets.

As a result, AI has taken the DAM industry by storm. AI and machine learning algorithms are far more sophisticated than simple automation tasks, capable of bringing together unstructured bits of information scattered across a database and identifying patterns that lead to actionable insights.

With the capacity to address a wide variety of issues, AI is helping companies approach DAM in a whole new way. It accelerates content creation, improves asset organization, enhances collaboration, and even helps businesses meet industry-specific regulatory demands.

A Brief Overview of How AI Works

The field of artificial intelligence is diverse and stretches back farther than you might think. Here’s an outline of some of the most relevant subsets of AI and how they are applied within digital asset management.

Machine Learning

Machine learning algorithms are a key component of any intelligent DAM platform. As the name suggests, these algorithms learn and improve from data, even if they weren’t explicitly programmed for a specific task. However, they still require human oversight in terms of model selection, training, and evaluation.

For example, ML models are used to analyze large datasets and identify patterns that enable automatic tagging and categorizing. This is especially useful for automating digital asset organization based on visual elements without requiring human users to open every file and evaluate its contents.

Through a process of continuous learning, ML algorithms use historical data from your unique datasets to improve accuracy and decision-making. In other words, the more you train the ML model on your data, the better it will perform in the future.

Here are some applications of machine learning in DAM:

  • Automated metadata tagging
  • Improved search results
  • Workflow automation
  • Content recommendations and personalization

Predictive Analytics

Predictive analytics uses a combination of machine learning algorithms, statistical models, and data mining techniques to predict future outcomes based on historical trends. This area of AI is incredibly useful for uncovering patterns in past user behavior and using the knowledge to guide better strategies moving forward.

When specifically applied to DAM, predictive analytics helps identify patterns in asset management workflows. For instance, assets that have high usage rates in relation to other assets could be recommended in the future when the related assets are accessed. Understanding how assets are used and how they might be useful in the future is essential for enhancing resource allocation and overall content strategy.

Here are some ways predictive analytics is used in DAM:

  • Trend forecasting
  • Risk management
  • Performance monitoring
  • Resource optimization

Natural Language Processing (NLP)

Natural language processing allows machines to understand human language in a more nuanced way. Through NLP, computers can understand, interpret, and generate language that is more conversational, nuanced, and unstructured.

This is especially valuable for DAM because it allows users to search for assets using conversational terms. For example, you could describe a campaign’s theme even if you forgot the specific keywords. An NLP-enabled and trained system would still produce the correct results because it already analyzed the text content within the files to create its own keywords and tags.

Here are some areas where natural language processing is used in DAM:

  • Metadata creation
  • Natural language search
  • Sentiment analysis
  • Content summarization and translation

Computer Vision

Computer vision is like giving computers the gift of sight — it enables machines to gather visual information from images or videos. By interpreting this data, computer vision allows DAM platforms to automatically tag and categorize assets based on visual cues like objects, faces, or scenes.

But it can go deeper than mere recognition. Computer vision is capable of analyzing visual assets for quality, performing compliance checks, or helping to edit visual content based on predefined criteria. This opens up powerful automation possibilities for DAM systems looking to efficiently manage their high volumes of assets with a high degree of accuracy and detail.

Here are some applications of computer vision in DAM:

  • Visual content tagging
  • Content quality and compliance assessments
  • Facial and object recognition
  • Automated image editing

Generative AI

Thanks to the rise of public AI text and image-generating platforms, generative AI is a subset of AI technology that more people will have first-hand experience using. Generative AI tools are trained on massive data sets and use machine learning algorithms to identify the underlying patterns within this data.

By ingesting text prompts, generative AI tools create new content in the form of text, images, audio, video, or code in a way that resembles the training data. Generative AI usage is already widespread in creative fields and has demonstrated incredible potential to complement creative workflows and streamline resource utilization processes.

Here are some ways generative AI is used in DAM:

  • Brainstorming and prototyping
  • Content creation and editing
  • Content recycling
  • Personalized content generation

7 Ways AI Is Used in DAM

All of the following points can be boiled down to one central function: AI in DAM streamlines and optimizes asset management at every stage of an asset’s lifecycle. By allowing AI to do what it does best, people can focus on the core aspects of their roles and deliver higher-quality digital experiences for their clients and target consumers.

1. Metadata Creation

Manual metadata creation used to be tedious work. But thanks to AI machine learning algorithms and predictive analytics, this step in the workflow is now largely automated. Human input is no longer required at the most time-consuming stage in the metadata creation process. Instead, editors can review the automatically generated metadata for accuracy and relevance before giving their stamp of approval.

Features

  • Detects brand-specific taxonomy
  • Recommends brand-specific tags
  • Recognizes and tags things like objects, faces, and scenes

Benefits

  • Improves consistency and accuracy by eliminating human error
  • Makes it easier to scale the volume of digital assets entered into the system

2. Content Summarization and Translation

Natural language processing has made it possible for AI models to take complex pieces of content and turn them into easy-to-read summaries for quicker review processes and decision-making.

Features

  • Auto video transcriptions leveraging speech-to-text
  • Time-based video summaries
  • Fast, accurate translation of text and multimedia transcriptions

Benefits

  • Makes assets, especially videos, easier to find and utilize
  • Enhances the reusability of an asset and extends its lifecycle
  • Assists with content localization

3. Search and Discovery

By improving the detail and accuracy of metadata creation and content summarization, AI has supercharged the asset discovery workflow. Users who regularly interact with DAM systems will know that there’s nothing more frustrating than trying to find an asset that’s been mislabeled or lacks sufficient metadata.

AI-powered search doesn’t just read file names — it has a deep understanding of the content itself, using natural language processing to digest conversational search terms and match the right asset with the user’s intent based on context, not exact keywords.

Features

  • Shows you similar and related assets
  • Conversational search allows you to find campaign assets by describing their theme
  • Visual search allows you to search by image instead of text

Benefits

  • Speeds up asset discovery
  • Enables teams to cross-collaborate more effectively

4. Content Creation

Generative AI empowers users to accelerate asset creation in a variety of media, including text, image, video, and audio. When specifically applied to DAM platforms, generative AI tools can do things like write blog posts based on asset content or localize marketing materials to fit regional contexts.

For example, let’s say you want to create a social media post based on content from a newsletter. You can prompt AI to create sample posts, including both text and video, that you can later review and edit or publish as-is.

Features

  • Creates text, image, audio, and video content from text-based prompts
  • AI content coaching adjusts content to suit different localization needs or publishing formats

Benefits

  • Reduces the time spent on brainstorming and drafting content
  • Empowers non-creative personnel to play a larger role in content creation

5. Image Transformation

Asset creation is just one step in the content delivery process. It’s likely that other image variants will be needed to fit different aspect ratios or highlight different focal points in the image. Previously, this process would require an editor to spend hours editing and exporting new files. But with generative AI tools, you can create image variants faster than ever and move assets on to the next stage of deployment.

Features

  • Automatic cropping and placement by focusing on different focal objects
  • Change backgrounds, swap out models, alter facial expressions, remove objects, or extend images with content-aware fill
  • Generate image variations to suit different crop formats

Benefits

  • Accelerates the content editing and publishing workflow
  • Reduces human time and effort in tedious work like file conversion and formatting
  • Enables better omnichannel content delivery

6. Content Recommendation and Personalization

A sophisticated combination of machine learning algorithms, predictive analytics, computer vision, and natural language processing enables AI to learn from user behavior and recommend relevant content at various stages in the DAM workflow. For example, AI models use computer vision to recognize similar models, themes, or objects and can recommend these assets to you whenever you search for terms that may be related to these assets.

The more you interact with the system, the better it gets at understanding how you interact with assets. It will curate a more personalized asset discovery experience that allows users to get more value from the system as time goes on.

Features

  • Similar content recommendations
  • User behavior analysis

Benefits

  • Enhances workflow efficiency by delivering the right content at the right time
  • Encourages asset re-use by surfacing related content

7. Brand Safety and Compliance

Staying compliant is a top priority for any enterprise and is especially important when working with AI-generated content that requires special handling in certain regulatory contexts. AI helps maintain brand integrity by performing compliance reviews and triggering review workflows for any assets that need manual review.

Features

  • Detects AI-generated content and stamps metadata
  • Performs compliance checks for legality or contradictions and suggests edits

Benefits

  • Review workflows to ensure you don’t publish anything without human approval
  • Reduces the heavy manual workload of performing compliance reviews

Learn How to Leverage AI’s Potential for DAM

AI technology should never be used to replace human ingenuity. Instead, leverage its strengths to complement your teams’ creative output and improve operational efficiency at every step of the DAM workflow.

On the whole, AI-powered, intelligent DAM systems are paving the way to more intuitive, efficient digital asset management. The best way to understand how AI is transforming DAM is to see it for yourself. Schedule a live, personalized demo with our team today to learn more about the way that AI DAM can help your brand reach its full potential.

FAQ

What AI technologies are used in DAM?

Machine learning, generative AI, predictive analytics, computer vision, and natural language processing are all branches of AI that are used to aid DAM workflows and increase operational efficiency.

Why do companies need AI for digital asset management?

Businesses are always expanding their digital footprint to stay relevant in a competitive landscape. As a result, the high volume output of digital assets is making it more difficult for companies to manage their vast content databases. Security risks, lack of organization, and inefficient collaboration workflows all pose significant challenges that can be addressed by AI-powered, intelligent asset management.

What are the benefits of using AI for digital asset management?

AI in digital asset management has the incredible ability to quickly process large amounts of data in a way that is more in line with human thinking than other data processing technologies of the past. AI enables intelligent DAM platforms to optimize digital asset organization and management through a suite of tools and integrations. This, in turn, streamlines content operations, enhances collaboration, safeguards against compliance issues, and increases marketing ROI.

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