How AI/ML Can Enhance Digital Asset Management

Digital Media content, both creation and consumption, has grown exponentially over the last three decades. Due to the sheer amount of data generated each year, Cloud-based SaaS DAM solutions such as FileSpin have become the lynchpin for managing digital assets. In fact, by 2026, the global DAM market size is expected to reach more than $10 billion compared to $3 billion in 2019.

Like any other data-intensive market, modern technologies like Artificial Intelligence (AI) and Machine Learning (ML) can enhance the overall DAM experience for customers by automating mundane asset management tasks. DAM vendors can integrate AI/ML features in their solutions by adopting an AI-first approach, enabling customers to improve their business ROI significantly.

AI & ML Applications for B2B SaaS Digital Asset Management

AI innovation is at the forefront of digital expansion. DAM solutions that offer the following AI-enabled features can improve an organization’s operational effectiveness and provide a competitive edge in the market.

1. Automatic Asset Tagging & Categorization

AI-automated asset tagging aids more precise asset discovery. Unlike most automated solutions, this goes beyond suggesting generic tags and is able to suggest highly specific descriptive tags.

A robust DAM solution uses product image recognition to add precise tags, analyze individual video frames, and automatically transcribe and tag speech from audio. AI-based asset tagging enables on-the-fly image and video annotation. Marketing teams can manually adjust annotations using multi-shaped bounding boxes as well.

Organizations can set asset tagging rules to fine-tune the automated process to suit their brand language. The DAM can generate multilingual tags, accommodating customers from different regions. Marketers and DAM admins can review suggested asset tags to improve the AI over time. AI-powered asset tagging features can enhance the search experience for all stakeholders. Using simple keyword searches, team members can quickly find digital assets within the DAM asset library.

2. Object Detection and Facial Recognition in DAM

Using object detection and recognition techniques, a DAM can identify age, gender, location, and the number of objects in images and videos, making asset tagging more robust.

Modern DAM solutions also accompany or integrate large-scale databases to accurately identify humans, animals, and worldwide landmarks. They can also detect sensitive, inappropriate or non-compliant digital assets and notify DAM admins automatically.

In addition, AI-enhanced DAM solutions can also apply optical character recognition (OCR) on images to extract text and categorize it to enable text-based search.

3. AI-Powered Audio & Video Transcription

Automatic speech recognition (ASR) and natural language processing (NLP) systems have become more intelligent and powerful with the development of transformers-based approaches and advancements in unsupervised learning.

Unsupervised speech recognition systems can be integrated within DAM systems to power multi-language speech recognition in audios and videos without curating and training large language datasets.

The DAM solution can automatically maintain an internal dictionary containing business-specific words deciphered from your digital assets like webinars, podcasts, and instructional videos. They can also enable real-time audio/video captioning for live events. DAM managers can utilize the built-in text editor to review and correct transcriptions manually. Audio and video assets become more searchable, editable, and shareable with better transcriptions.

4. ML-Based Image Quality Corrections

ML-based image processing has revolutionized the online and offline media capabilities in recent times. The DAM can analyze images in detail to automatically adjust image contrast and brightness. It can apply many effects and filters to enhance the user’s visual experience.

The DAM can enhance image features, remove blur, and increase image resolution with ML-based image corrections. It can also suggest appropriate crop and zoom levels based on the images' depth estimation, background, and objects.

5. Video Upscaling with ML

High-quality video content is vital for successful marketing. In fact, 87 percent of marketers report that video content improves marketing ROI with a better lead generation rate. Dynamic video enhancement and scaling is a game-changer for running effective marketing campaigns.

DAM integrated video upscaling tool enables marketers to enhance their product videos. Marketers can upscale videos to 4K and 8K resolutions. They can also restore and enhance old videos while maintaining their features. With different video resolutions available, DAM systems can automatically serve the users per their bandwidth, improving the user’s overall web experience.

Best Practices for Applying AI & ML in Digital Asset Management

AI-enhanced DAM solution vendors must encourage responsible AI/ML practices that ensure transparency, interpretability, and auditability to empower their products and customers.

1. Improve AI/ML Features Over Time

AI demands constant improvements because real-world data trends continue to change. An AI or ML model deployed a year ago is likely to show performance degradation or become obsolete considering the rapid pace of AI development.

For DAM systems, a feature like image processing or video upscaling will benefit if new real-world data is collected regularly and existing models are retrained. AI/ML teams can also experiment with state-of-the-art AI algorithms to improve the features.

A great way to ensure consistent performance is by building robust ML pipelines. ML pipelines enable continuous model monitoring and automated maintenance and empower the team to take appropriate measures whenever AI features give unfavorable results like inaccurate predictions or increased inference time.

2. Keeping AI-Curated Digital Assets Separate From Manually-Curated Assets

AI automation reduces the cost of performing repetitive manual tasks. But it has a tendency to generate inaccurate results, requiring constant supervision. For DAMs with AI asset tagging, it is crucial to keep AI-generated tags and metadata separate from manually curated assets.

DAM systems must empower users to distinguish between AI or manually generated metadata. AI-enabled DAM must always be on-demand, allowing DAM admins to opt-in or opt-out of any AI features, thereby avoiding or mitigating any irregular AI output.

3. Track & Monitor AI-Generated Assets

DAM solutions must have built-in monitoring capabilities to keep track of any AI-generated asset metadata, images, or videos. DAM users should find or classify AI assets based on name, size, format, and asset’s internal contents like people and locations.

DAM should allow teams to track the performance of AI-edited images and videos based on end-user engagement and make any corrections quickly. With rapid advancements, AI will become more reliable in the future. But until then, having human supervision over AI-generated content can significantly improve the overall business efficiency.