Contents

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Overview

Skill Level: Any Skill Level

Ingredients

For image content libraries, AI and computer vision open new doors for uploading, tagging and finding content. For example, organizations that are early adopters of AI often see a reduction in time spent tagging assets with metadata. This is a win for anyone who knows the tedium of accurately tagging large batches of digital assets with metadata.

The rapid growth of digital content and digital image libraries, and the insane amount of time it takes to fill out core business-specific metadata demands a better strategy. AI tagging is proving to be a useful solution for any business that relies on metadata for search or information retrieval.

AI applications in B2B business tools such as digital asset management solutions may be in their infancy but are proving useful for companies looking to reduce time spent completing manual tasks. These advancements enable organizations to make more innovative and creative applications of their time.

Step-by-step

  1. How AI-Enabled DAM is Advancing Business

    Modern digital asset management requires working with huge volumes of media and metadata. It is simply not possible to manually identify, tag, and sort this data in a cost efficient or effective way. To keep content useful, DAM solutions need to enable automation of tasks, namely through AI.

    From Archive to Marketing Assistant

    Incorporating AI functionality, like image recognition, speech-to-text, and other machine learning technologies into DAM enables organizations to tag media automatically. Tagging is crucial and optimizes workflows by simplifying queries and use.

    Tags are created through metadata, contextual information about your assets. Tags provide context for content and increase its value by enabling you to track use. You can only understand  the context of your assets through metadata.

    To reduce noise and return only the most relevant content, you need a dictionary of tags (a taxonomy) specific to your business requirements. With AI-enabled solutions, you can upload this dictionary to the AI engine for guidance on tagging. 

    The benefits of applying AI-enabled DAM include:

    • Faster queries lead to greater productivity
    • Content is centralized and easier to manage
    • Easier to use and reuse digital content 
    • Content creators can focus on creating more engaging materials
    • Easier to collect real-time data on customer preferences based on content use

     

    Better Collaboration Between Teams

    Many organizations default to data silos. These silos happen when departments cannot easily share information between business units, causing long-term goals to be replaced by individual priorities.

    A centralized, sorted, and shared archive enables tighter integration between marketing, creative, and IT teams. This results in better customer communication, more efficient use of assets, and greater brand consistency.

    Facilitating Agile Methodologies

    Many organizations are beginning to adopt single DAM environments from which employees can share and manage information. This enables employees to share and access data quickly and facilitates agile workflows. 

    DAM solutions are no longer focused on being content archives and are instead becoming centralized systems. With the help of AI management, these systems can aggregate digital assets from many different sources and make them available to others through various integrations. 

    The growth of cloud technologies has played a large role in the evolution of DAMs, making it easier to host significant amounts of data and to perform the computations needed for AI. DAMs are scalable, flexible, and modular and can easily be to customer and business requirements.

  2. Deep Learning and AI in DAM Platforms

    There are many ways deep learning and AI can be incorporated into DAM platforms but most platforms focus on a few main functionalities. These include AI tagging, speech to text conversion, visual similarity, and computer linguistics.

    AI Tagging

    AI tagging implements a variety of machine and deep learning technologies to identify the media’s content, the media’s characteristics, and to classify the media. Once a media asset has been evaluated, the DAM can assign it tags based on the evaluation and in line with provided dictionaries. 

    Typically, when AI tagging is used, tags are specially marked as being produced by AI, especially at first. This enables managers to identify any issues in the classification system and modify them to improve future tagging. It also helps ensure that users evaluate the accuracy of the tags before use.

    Speech-to-Text Conversion

    Speech-to-text conversion is a technology that can convert spoken word into text by scanning an audio stream and interpreting the sounds into words. In DAMs, this feature is particularly useful for analyzing audio and video files. It can shorten the process of transcribing audio or video files and provide insight into content without having to watch or listen to the file. important for DAM solutions because the extracted text becomes searchable, allowing for faster queries.

    Visual Similarity

    Sorting through photos to identify duplicates or near duplicates is a time-consuming task. When DAM solutions include image similarity features, you can easily find photos in your asset catalog that are similar or duplicate. This reduces the number of hours you and your team need to spend sorting through them and helps ensure everyone is working with the same version.

    Computer linguistics

    When processing audio or text AI systems use speech recognition or optical character recognition (OCR). The AI listens to or reads content and uses natural language processing (NLP) and deep learning neural networks to identify words and sentences. 

    Once identified, the AI solution can identify speech patterns, decipher them, and contextualize what was said. In DAM solutions, this can enable you to automatically interpret the meaning or intent of audio, video, or text files, leading to more accurate searches and tagging. 

     

  3. Conclusion

    AI is increasingly becoming integrated into a wide range of industries, including digital asset management. AI capabilities drive efficiencies and improve productivity, supporting human resources with capabilities that reduce the time spent on repetitive tasks. 

    With AI tagging and visual similarity, asset organization becomes much more efficient. With speech-to-text conversion and computer linguistics, transcribing can be accomplished much faster. AI turns DAM to more than just an archive, but into a dynamic system that integrates into agile workflows.

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