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Leveraging the Power of AI for Digital Asset Management

Leveraging the Power of AI for Digital Asset Management. Digital asset management is a process to store, organize, retrieve, and share digital assets.




Leveraging the Power of AI for Digital Asset Management

Artificial Intelligence (AI) technologies are in the process of integrating into every field of work, including marketing workflows. AI, however, is not a replacement for a human workforce.

Instead, AI capabilities support your work by automating repetitive tasks, thus enabling marketers and developers to do more in less time. In this article, you’ll learn how AI improves digital asset management (DAM) tasks, and how to implement AI in DAM ecosystems properly.

1. What Is Digital Asset Management?

What Is Digital Asset Management

Digital asset management (DAM) is a process you can use to store, organize, retrieve, and share digital assets. DAM uses a centralized content library that is accessible by employees, contractors, clients, and stakeholders. Assets stored in a DAM include images, audio, video, documents, and original files.

2. AI and Digital Asset Management

Artificial intelligence (AI) is the use of machine learning to perform tasks that were previously only capable of manual human effort. You can use AI technologies to automate and facilitate a variety of functions within DAM systems. AI can help organizations and teams retain control over significant amounts of data with less effort.

3. AI Technologies That Power Dynamic DAM Systems

AI Technologies That Power Dynamic DAM Systems

Several AI technologies can be incorporated into DAM systems. The most commonly integrated technologies are covered below.

a) Smart Tagging

A tag is a piece of metadata that you use to classify and identify assets. Cards are used for searching, tracking, and sorting assets within your DAM. Smart tagging is the use of AI to apply these tags. It is based on API services offered by organizations such as Microsoft, Google, and Amazon.

These services use image recognition to determine the content of media and suggest appropriate tags. Image recognition technologies scan images to identify specific characteristics associated with a category or classification. AI then weighs these characteristics to determine the most likely content of the picture.

Some DAM systems enable you to incorporate business intelligence and marketing data to categorize assets with tags better. This can allow you to distribute content across multiple media channels automatically reliably.

b) OCR

Optical Character Recognition (OCR) is an AI technology you can use to identify text inside images. You can use it to extract text from images and convert that text to an editable document. Extracting text from images is not a new capability, but previously it could only be performed on images with defined structure or layout. OCR uses the same image recognition strategies that are used for smart tagging.

Modern AI, however, enables you to extract text more dynamically. This is possible because AI algorithms can learn from feedback supplied from previous text extractions. This feedback is applied to future removals, refining the sensitivity and accuracy of the OCR algorithm.

c) Natural Linguistics Processing (NLP)

NLP is a field of AI that deals with how computers process and analyze linguistic information. It is commonly used in speech to text conversion and chatbots. In a DAM system, you can use NLP to extract speech content from video or audio files. This content can then be used to create transcripts, subtitles, and captions. It can also be used as a guide for speech removal from files.

NLP capabilities incorporated in DAM systems can also enable you to serve content according to requests in a variety of languages. This can be accomplished through translations of search requests.

4. Best Practices for Implementing AI in a DAM

When incorporating AI into your DAM systems, there are several best practices you should follow. Below are the essential methods to start with.

a) Keep AI-Generated Metadata Separate

You should find a way to identify AI metadata separately from user-generated data. This enables you to verify the accuracy of your AI more efficiently and to ensure that tags are appropriate. By keeping metadata separate, you can ensure that your tagging is as reliable as possible and that user-generated tags are not automatically overwritten.

b) Track AI Like Your Users

You shouldn’t provide users unfettered access to your DAM; this is as true for AI. You need to be able to track the actions of your AI services and vendors to ensure the integrity of your data. Tracking can also help you identify when AI is not working correctly or if it is causing conflicts with other integrations or users.

c) Ensure That You Can Filter AI-Generated Metadata

You should be able to filter whether or not AI-generated metadata is used for searches, content serving, or analytics. Some users may not trust AI-generated data or may prefer to use only specific AI sources. Likewise, users may wish to use only AI-generated metadata created after an algorithm has been refined.

d) AI-Generated Metadata Should be Convertible

Many DAMs enable you to convert embedded metadata into a regular asset attribute, such as a keyword. This ability should extend to AI-generated metadata. Convertibility can help you ensure that AI-generated information remains available even if AI services are removed or changed. You may also wish to set protocols that automatically convert metadata once enough users have approved it.

e) Use Feedback to Improve Your AI Accuracy

Any AI services you use should improve over time. This could mean that algorithms are refined with each analysis or suggestion or that algorithms are periodically retrained. Sources of feedback can include manual audits, accumulated user verification of accuracy, or asset use statistics.

Check if you have specific tags that are consistently rejected, or assets that you know are relevant that aren’t’ being returned. Both of these situations can indicate a fault in your AI and can help you target areas for improvement.

5. Conclusion

AI enhances DAM systems with capabilities that save time and enable personalization. By delegating repetitive tasks to the machine, you can free up your time and focus on sophisticated and creative jobs. In many cases, once you set up an organization system, the AI mechanism will take care of the grunt work of classifying the assets. You can then set up a dynamic process that delivers personalized, and sometimes hyper-personalized, content to users.

Gilad David Maayan is a technology writer who has worked with over 150 technology companies, including SAP, Samsung NEXT, NetApp, and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.

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A Guide To Using AI for Knowledge Management

Using AI for knowledge management and to transform massive data pools into actionable insights is not just beneficial; it’s becoming a necessity to stay competitive.




In the digital era, the fusion of AI technology with knowledge management is revolutionizing the way organizations manage and exploit their informational assets. Using AI for knowledge management and to transform massive data pools into actionable insights is not just beneficial; it’s becoming a necessity to stay competitive. Keep reading to unlock the full potential of AI-driven knowledge management.

1. The Intersection of AI and Knowledge Management: A Synergy Explained

Knowledge management traditionally involves capturing, organizing, and distributing knowledge across an organization. When AI steps into this territory, the potential for enhanced efficiency and decision-making emerges. AI algorithms can sort through and analyze data at a rate no human can match, revealing patterns and insights that can be critical for strategic planning. This melding of AI with knowledge management practices is a modern alchemy, creating an invaluable resource.

One of the most significant benefits of integrating AI into knowledge management is the automation of data processing. AI systems can continuously learn from new data, refining their algorithms and providing even richer insights over time. Moving from static data repositories to dynamic knowledge hubs, businesses are now armed with constantly evolving intelligence. This represents a profound shift from data being a static historical record to a dynamic, predictive tool for decision-making.

Using AI for Knowledge Management is a top solution

Customization is another strong suit of AI in this space. Rather than one-size-fits-all information resources, AI can personalize knowledge dissemination to the needs of each employee. The focus moves beyond mere information access to ensuring the right knowledge reaches the right person at the right time.

2. Implementing AI in Your Knowledge Management Strategy

Transitioning to an AI-driven knowledge management system begins with identifying the scope and objectives of knowledge needed. Organizations must be clear about the kind of knowledge that is most valuable and how AI can aid in its cultivation and dissemination.

Following initial evaluations, the selection of appropriate AI tools and technologies becomes the next crucial step. There are various AI solutions designed for specific knowledge management tasks, from natural language processing for content analysis to machine learning models that predict trends and behaviors.

Integrating AI requires a cultural shift within the organization. Employee buy-in is crucial, and it is important to address any concerns about job displacement head-on. Training and educating the workforce on the benefits and use of AI systems can facilitate smoother adoption, ensuring everyone understands the role of AI as a partner, not a replacement, in the knowledge ecosystem.

3. The Impact of AI on Knowledge Retention and Dissemination

An AI chip sits in a computer

The true value of AI in knowledge management is often most visible when assessing knowledge retention and dissemination within the organization. AI-driven systems can significantly enhance the ability to capture institutional knowledge, store it and make it available in engaging, interactive formats that increase retention.

Analytics are also central to measuring the impact of AI on knowledge management. By evaluating these metrics, businesses can see whether the knowledge is being leveraged effectively, which areas may need more focus, and where improvements can be made.

Furthermore, AI can be a boon for training and development programs. By adapting to the learning pace and style of individual employees, AI can deliver personalized training recommendations and content, leading to more effective learning outcomes.

Altogether, AI is transforming the realm of knowledge management with its capacity to automate, personalize, and revolutionize how information is processed and utilized. Challenges notwithstanding, the rewards of integrating AI into knowledge management strategies are profound, paving the way for smarter.

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