Using Digital Asset Supply Chain Management And AI To Improve Efficiency And Enhance Metadata Quality

As many DAM users will be aware, recently there has been a lot of interest in using Artificial Intelligence and visual recognition tools to automate the tagging and cataloguing of content digital assets like images and video with relevant metadata.

While the results of these technologies have improved over recent years, they are still unsatisfactory for the majority of real-world Digital Asset Management scenarios. The reason is that DAM tool vendors rely exclusively on the visual content of the asset alone, so the suggested keywords have no context or awareness of the purpose of why the asset was uploaded to the system in the first place.

The two key ingredients missing are: context and the ability to learn. Context means knowing that a picture of a tall building is your organisation’s head office, that a smiling woman wearing business suit is your CEO. Learning, means that the Artificial Intelligence can refer to this contextual awareness historically so it can gradually improve its results.

Simply put, AI image recognition is only half the story of your digital asset metadata. What you need is the information from your digital asset supply chain: the supporting emails, documents, meetings, workflow and discussions that lead to your assets being created or commissioned from suppliers in the first place. Using this data, in combination with AI tools and other information sources, it is possible to derive far more credible digital asset metadata which means something to users and therefore dramatically improves the chances of finding assets later.

We have teamed up with some AI implementation experts to provide a service where we can help you optimise your digital asset cataloguing operation by making it far more efficient and accurate by using a combination of AI technologies and digital asset supply chain optimisation. This is what we do:

We analyse your current digital asset supply chain: where assets enter the business and leave it for other destinations.
We review the different sources of metadata and use some very simple methods to group them together.
We apply AI-based text analysis tools to extract keywords and concepts that relate to your digital assets, from a business perspective.
We leverage AI image recognition to help derive literal descriptions of your assets (i.e. what you can see if you knew nothing about your visual media assets).
We propose simple coding schemes that can be used to direct the AI tools so they are more effective at identifying relevant metadata
We advise and implement machine-learning techniques so you can develop the automation tools into a business asset that gets incrementally better at solving your cataloguing challenges.
We analyse your catalogues and work with you to segment them into those that will still need human input so you can make more efficient use of these more versatile (but expensive) resources.
We place risk and quality management at the heart of all our projects and constantly review our recommendations and the results so you maintain quality levels at the targets you require and without taking on undue business or brand risks.

Our AI cataloguing assignments are firmly grounded in supply chain continuous improvement best practice and attaining maximum ROI for you. We can provide something as simple as a short document describing some recommendations through to a full implementation and delivery project. We work with all kinds of DAM software vendors (or other suppliers, whether in-house or external).

If you want to finally get control of your digital asset management cataloguing operations and make low-risk, high ROI use of advanced AI and automation technology, why not have a conversation with us? We don’t charge for an initial meeting or phone call so you have little to lose.

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