No media company should invest in AI tools for content management if they have not previously considered how to optimize their application
Artificial Intelligence tools have begun to evolve from an early childhood stage to their youth in the Broadcast and Media industry. It could be said that we have already overcome the phase in which everybody talks about it, but nobody dares to adopt it. Today, the implementation of these tools for content management and creation is beginning to be much more extensive, although there is still a long way to go before we reach full adoption. So, what is the main stumbling block that companies are facing today in order to maximize the application and performance they can extract from this technology?
We could list several reasons why companies claim not to progress towards full AI adoption. For example, they are usually concerned about not being able to extract accurate results tailored to their needs, as well as about the security of their data when using third party platforms in the cloud, in addition to the possibility of not being able to correctly estimate the costs of these services, among other drawbacks.
Indeed, as AI is still under development, many doubts may arise when we value investing in it or not. But here we must not deceive ourselves: all the above-mentioned obstacles are not the consequence of an immature technology. The main trigger for all of them lies exclusively in not asking ourselves the two most important questions of all before investing: first, what real application do I want to give to Artificial Intelligence in my company? and second, is my content management system ready to integrate this technology in a useful and profitable way?
A Media Asset Management (MAM) system with AI tools is as valuable as the sets of images and metadata that the chosen AI engine analyzes, on the one hand, and the use and management we make of all this information through the MAM, on the other. That is to say, it is useless to have a service that provides us with an infinite amount of information about our media files if we are not able to configure and adapt our MAM to manage all this associated data, analyze it and present it correctly, thus allowing us to draw conclusions and achieve our business objectives.
Making AI investment useful and profitable requires having a MAM capable of linking and understanding all the “out-of-control” information offered by AI engines. Only a system capable of filtering and displaying all the data in a logical and coherent way, and even capable of integrating several AI engines at the same time and adapting to different data models that must be sorted and harmonized, will allow us to take better advantage of the full potential of these tools to produce and manage more content in less time.
We usually see many companies invest in this technology without knowing too clearly what benefits it can bring and not even aware if their infrastructure and systems are ready for it. This is why they end up implementing an expensive AI service exclusively for content cataloging. The lack of planning and previous analysis usually limits the potential of this technology, ignoring many of the real applications it can offer today.
If we pay attention, for example, to content consumption (which audience consumes them and when and how they do), AI can help us predict future behaviors and and assist us in planning our available resources for productions. Similarly, when it comes to process automation, AI tools can predict potential disruptions in the content value chain or in the supply chain by analyzing patterns. And in terms of sheer production, the possibilities are endless – from automatically translating content into multiple languages for global distribution, to suggesting specific assets and sequences when editing our videos based on what has worked best for us. And for that, it doesn’t matter if all this information comes from a single AI engine or from several (using, for example, Google’s transcription for audio recognition and a local AI system trained by us for face and object detection). In the end, what really matters is to have a clear idea of the objectives we are looking for when purchasing this service and, of course, to know if our MAM is adequate to integrate these tools.
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