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From answer to insight: When LLM search feeds the metadata loop​​​​​​​
One of the most powerful—but still underexplored—opportunities in AI-enabled DAMs is not just that an LLM can return the right asset, but that it can explain why that asset was returned.

If an LLM-driven search surfaces the correct image and shows how it maps to the DAM’s controlled vocabulary—concepts, taxonomies, relationships, and business terms—the search result becomes more than an answer. It becomes insight.

Suddenly, search is no longer a terminal action. It becomes part of a feedback loop.

Visual AI Search - the latest game-changing solution
Lets focus on how the search function can be enhanced in DAMs using Visual AI Search, allowing users to search using natural language or visual cues, thereby bypassing the limitations of keywords and traditional metadata-based search methods.

This user-centric approach not only improves satisfaction and productivity but also ensures that the full value of the digital asset repository is realized.

Using Artificial Intelligence (AI) to automatically identify and classify assets and objects in mediafiles can enhance the Digital Asset Management (DAM) workflow in several ways, while also providing more accurate and useful information about the assets.

Everywhere you turn, for the past months, you hear people discussing how incredible OpenAI's newly unveiled ChatGPT model is. Will the rapid AI development and the awareness of it's powers affect DAMs and how and what kind of AI can enhance digital asset management?

Yes, for sure metadata can be ingested using AI, and this input can be useful to automate classification and move media files to specific user groups. It can save time and effort in organizing and managing digital assets.

It comes without saying, content creation costs money. A well researched post can take hours of time when you include the research, production, choice of images, and the writing and editing of the post. These costs will vary depending on the content.

A notable cost is involved in producing high quality, unique content on a regular basis. And yet organisations staff costs of producing content are often lost. 

So why not utilise generative AI tools like Open AI:s ChatGPT to get basic information for articles and blogs and then simply curate and repurpose the content. After all it all comes down to ROI and speed of content production. With the help of AI tools like ChatGPT writers, designers, researchers and any person  can generate high-quality, original content faster and more efficiently than ever before.

Wouldn't it be nice if just by uploading images and videos to your media library the AI-assisted DAM system would automatically categorise and tag them and make them searchable?

So why is computer vision, despite major development steps, still not relevant in media bank workflows in 2022?