Streamlining Digital Asset Cataloguing Using AI and Digital Asset Supply Chain Data

Overview

Until recently, the extent to which AI has been used within Digital Asset Management (DAM) was limited to auto-tagging, a few specialist use-cases like Facial Recognition and some integration with Generative AI to modify image assets. These techniques are all based on the pixels which make up the image, therefore, they lack context. This is because most models have been trained on generic or consumer-oriented datasets like stock image libraries. As a consequence, many real-world DAM users find the results to be unreliable and not useful for their vertical or niche, and will often ask vendors to disable them.

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The time-consuming element of the DAM process is the cataloguing of digital assets with descriptive metadata. This is not just tagging or adding keywords, but all kinds of other associated processes, such as enabling downstream integration with other solutions, rights management and advanced asset analytics/reporting. If the correct metadata is not in place, these initiatives become much harder, if not impossible to implement successfully.

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Fully automated digital asset metadata cataloguing remains the holy grail of DAM and the hope (if not expectation) is that AI tools such as LLMs (Large Language Models) will provide a solution which will one day make it a reality. Unfortunately, this analogy itself hints at how likely it is that this goal will ever be fully achieved. With that said, there is clearly some potential to streamline the asset cataloguing task and AI offers a range of tools and techniques which potentially could be useful. Furthermore, providing AI acts in an advisory capacity (and human end-users retain agency over the process) the risks of generating large amounts of useless metadata by industrialising the cataloguing process could theoretically be managed.

In this article, we want to explore some potential techniques and tactics that DAM users and vendors can utilise to streamline digital asset cataloguing.

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Leveraging AI's Strengths

Firstly, let's examine where current AI technology generates the most successful results.  There are two specific criteria, which both are good predictors of successful AI solutions:

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• The data being processed is more explicitly defined.  At a simplistic level, this means text rather than subjective material like images.  But it could also be numerical data like indices which represent states or values, for example, a controlled vocabulary.
• The problem domain (or context) is narrower and/or has been defined before the AI attempts to draw inferences from it.  For example, Facial Recognition is generally quite effective, similarly games with clear rules like Chess, Go etc.
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For all the sophistication and complexity which is claimed about these tools, the less AI systems have to think (or at least give the illusion that this is what is happening) is where they are most successful.   These two facts offer some clues on how to utilise AI to streamline the process of cataloguing digital assets. 

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Enter Digital Asset Supply Chains

The stages a digital asset goes through from origination through to cataloguing and then distribution are its supply chain.  Each of these points generates data.  The majority of it is textual and there are defined stages where assets begin to acquire normalised metadata (i.e. categories or classifications).

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Modern DAMs can be compared with airports.  What happens at airports is remarkably similar to the activity in DAMs.  Passengers arrive, are checked, directed and grouped towards specific destinations which are serviced by airlines where they then take off in aeroplanes to get them to their destination.  Each of these stages involves classifying the passengers into incrementally more specific groupings in order to move them to their required destinations. 

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In DAM terms, the passengers are analogous to assets; the checking and grouping stage can be compared with approval workflow and metadata; the airlines and aeroplanes are integration connectors and APIs to platforms like WCMs, mobile apps, CRM, social media etc. (i.e. downstream destinations).

Well before passengers or goods arrive at the airport, there is a significant amount of preparation, decision making and planning in order to streamline the process and make it as efficient as possible.  This is the context or reason why a decision to travel was made in the first place – and it is what is missing from most previous attempts to successfully use AI in DAM.

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Using Data from the Start of the Digital Asset Supply Chain

The lifecycle of all digital assets actually starts well before they exist.  Prior to assets being originated, there are typically emails, meetings and discussions using platforms like Slack, Teams, Zoom etc. about a specific project, initiative, new product or service launch.

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 Each of these generates data which AI tools can analyse.  In the case of meetings, it is now a simple matter to have them recorded and speech to text transcription software is now generally reliable enough for production use.  Emails are obviously already text and providing an AI agent with an email address so it can assimilate conversations is fairly trivial to implement. 

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As can be seen, it is a simple matter to give an AI tool like an LLM access to the context, discussions and background behind which decisions were originally made to commission the production of digital assets.  As such, it becomes more feasible for LLMs to start to make some inferences and predictions about what keywords will be used for cataloguing purposes.  If this is combined with an Enterprise taxonomy, for example, the kind used in Master Data Management solutions (or other form of controlled vocabulary) about what terms are acceptable for cataloguing purposes, then it is possible to see how LLMs might start to be able to automate some of the cataloguing work.  The LLM derives some suggested tags using the text source data, these get compared against a list of approved values and only where there is a match is the suggestion accepted.

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This is a remarkably quick and relatively easy way an LLM combined with some basic AI tech like transcription tools can be employed to help offload some of the cataloguing work.

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Autonomous Agents

One emerging area of AI with direct and growing relevance to DAM is the use of Autonomous Agents. These are powered by advanced machine learning and reasoning capabilities and can operate semi-independently within a DAM system to carry out routine tasks and drive proactive governance. 

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If the DAM maintains a comprehensive audit trail that records all user activity, such as uploads, downloads, edits, approvals, and sharing events, it becomes relatively straightforward for an agent to analyse behavioral patterns, detect anomalies, make predictions, and recommend governance-oriented interventions. 

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For example, an agent could flag missing or inconsistent metadata, notify administrators when assets are overdue for review, or automatically trigger predefined workflows to maintain compliance or brand standards. 

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Furthermore, when these agents are given access to a broader set of data from the Digital Asset Supply Chain, including project briefs, approval histories, content usage metrics, and campaign timelines, they can draw richer inferences, learn from historical patterns, and continuously optimize their decision-making and task execution over time. This transforms the DAM from a passive repository into an intelligent, adaptive system that supports content lifecycle management in real time.

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LLMs Reasoning 

Large Language Models like OpenAI’s GPT-o3 or Anthropic’s Claude 4 are increasingly capable of sophisticated reasoning over unstructured and semi-structured enterprise data.  This opens up an opportunity in the Digital Asset Supply Chain, even before assets exist (as described earlier).

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By integrating these models with an enterprise’s structured taxonomy, including controlled vocabularies, organisations can enable incrementally more contextual tagging that connects assets to their intended purpose rather than simply describing their surface-level content.

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For example, rather than just tagging an image as "woman on beach at sunset," an LLM informed by upstream data could tag it as “Q3 2025 lifestyle imagery for Product X, targeting Gen Z wellness audience, part of summer brand refresh campaign.” This deep metadata could persist with the asset throughout its lifecycle, aiding in searchability, rights management, performance analysis and future reuse.

This form of semantic enrichment (grounded in human intent and business context) has the potential to be far more meaningful than metadata generated solely through pixel-based AI models. It makes DAM not just a storage solution, but an intelligent storytelling layer that understands the ‘why’ behind every asset.

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The Value of Metadata Schemas and Advanced Metadata Management in DAMs

A common recent trend among some less enlightened DAM vendors has been to try and get users to over-simplify metadata schemas and rely more on unstructured data like text fields. The rationale behind this is that complex metadata isn’t required any longer because the AI tools will do all the cataloguing.

For AI to be effective, an even greater investment of time and effort is needed into the design of metadata schemas.  It is far preferable for metadata inputs to be controlled rather than free text.  As stated previously, AI works best where it has to do less thinking, in which case, this presupposes that the human beings need to do more of it. 

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The objective is to leverage the human being’s ‘thinking time’ as much as possible rather than to dumb it down to accommodate poorly implemented technology choices.  A carefully considered metadata strategy can enable greater and more effective use of AI so that the human beings have to do a lot less manual cataloguing work and devote more of their time to the organisation’s wider digital asset strategy.

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Conclusion

High quality descriptive metadata which is both literal and subject-specific will remain essential for Enterprise DAM.  Digital Asset Management initiatives with low quality metadata are almost always also the ones with poor ROI.  It doesn’t matter how much AI you add into the mix, if you can’t find an important asset - even with Generative AI features which might attempt to generate an alternative for you, then adoption (and therefore ROI) will undoubtedly suffer.

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With all that said, there clearly is some potential for DAM technology to leverage both AI and the data that is already being generated across Digital Asset Supply Chains to incrementally streamline cataloguing.  What all this will look like in 10 (or even twenty) years is harder to predict, but even if holy grails are exceptionally difficult to find, it does not mean that a cost-efficient and science-based search for them is not worth pursuing.

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