AI-Driven Media: The future of content creation and distribution

By Yeray Alfageme,  Business Development Manager at Optiva Media an EPAM company


The future of content creation and distribution is being shaped by AI. With the ability to analyze large amounts of data, AI can predict viewers’ trends and preferences, thus enabling the creation of audiovisual products that are more aligned with the tastes and concerns of viewers and, therefore, more likely to be successful. This not only increases viewers’ content consumption, but also opens up new revenue channels.

AI can also automate the distribution process, thus ensuring that content is delivered to the right audience at the right time. This can significantly increase the reach and impact of productions, which in turn results in a larger audience and higher revenues. In addition, this distribution process can be optimized, thus reducing the time and resources needed and therefore this resulting in lower operating costs.


AI-driven media: the main goals

The primary goal of AI-driven media is to retain viewers and increase engagement, especially in this highly competitive content creation environment. This is achieved through a concept that has become very prominent in almost every aspect of the services we enjoy on a daily basis: hypercustomization.

We all want to enjoy “exclusive” and “designed for me” services but, obviously, it is difficult to pay for them. That’s where AI or, more specifically, Machine Learning -ML-, comes in to analyze our behavior patterns and offer us those customized services. And this applies to distribution media such as linear channels, content aggregators or OTT platforms in general, where AI is used to customize the media offering according to individual viewer preferences, thus increasing content consumption and consolidating revenue in a more robust way.

A new path is opened in which companies, to grow beyond the traditional vertical expansions of distributors towards creators and vice versa, can make use of their existing catalogue, which in turn allows them to increase revenues without great risks. This includes the creation of new experiences, knowledge and content for consumer satisfaction and growth, making use of their current catalogue of productions. The very well-known example of FAST channels, which we have covered on TM Broadcast, is a clear case of this and of the most direct application of machine learning in our industry.

In addition, AI plays a crucial role in decreasing operating costs in the audiovisual sector in particular, and in the high-tech sector in general. By optimizing content creation, distribution, and consumption through automation, companies can ensure a sustainable cost reduction and improved efficiency. And beware, that cost reduction does not imply a decreased number of staff, but that people will be dedicated to creating and adding value where it is required and stop doing repetitive or low-value tasks that are easily automatable. It’s a double-edged sword, to be sure, but just like the steam engine in the industrial revolution, you have to learn how to use AI the way it deserves.


Increased content consumption of viewers with AI

AI-driven decisions in media produce a much stronger content delivery system, thus driving consumption of audiovisual products and revenue. Custom metadata improve viewer experiences, search accuracy, and real-time content updates.

If instead of offering all viewers the same metadata, the same description of the film, series or program, we offer different descriptions depending on the demographic profile of the specific viewer, including their mood, as we will see later, as well as time of day and time of year, the experience is greatly enriched. And this does not require an inordinate investment or extraordinary complexity, it is about applying AI as a tool that complements our current workflow.

AI also enables dynamic presentation of productions, real-time feedback, and accessibility improvements, which enhance usability and inclusivity. Real-time suggestions keep viewer interest with content they really like, while predictive analytics aligns specific content with the preferences the viewer expects to be offered. And we are not just meaning the typical recommendation engines based on collaborative filtering that compare similar users, which required considering thousands of users to work “well” -please note the quotation marks- but algorithms that are able to detect and predict patterns with the mere consumption of content.

In addition, AI can generate subtitles from audio to improve accessibility and user experience in real time and in any language, without the need to have them generated beforehand. Who wouldn’t want a platform that offered content in any language in the world? Unbelievable.

It is also capable of implemented an AI-powered navigation system that learns from user preferences and behavior to provide a customized graphical interface with the menu options shown in a particular order “the way I like it”; and, all this, different for each viewer, time of day or mood, impressive.

All these concepts go in the same direction, that the viewer perceives a high customization of their services without extraordinary complexity or exorbitant costs.


New revenue streams through AI

AI-driven media segmentation enhances revenue through ultra-targeted and segmented advertising beyond premium subscriptions. And they are not just niche channels where we program content for a particular segment of the population. By segmenting them simply with demographic data and putting stereotyped advertisers, but dynamic and different advertising inserts for each viewer, or group of these, with their favorite content at the time of day and week, for example. In short, FAST channels boosted to the zillionth power.

Emotional analytics for content creation and distribution can provide insights into viewer preferences, thus helping creators produce emotionally engaging content that appeals to a larger audience and generates an increased revenue. This goes beyond offering content if we are sad, happy, tired or cheerful; it is about using this information to create such content and deliver it in a timely manner. And for the time being it’s not just deciding when to release it, but at what time to offer it on my OTT platform or receive a notification on my mobile to watch it now that I’m sitting on the couch after a meal following a hard day’s work or after training, which is not the same.

Emotional trends and feelings within viewer segments can also be identified, thus facilitating associations with brands that align with specific emotional themes, this also leading to new revenue opportunities. If whenever I am happy I see a certain product, Paulov’s dog that we all carry inside will associate that product with my happiness.

Some of us may already get a whiff of: “Can we unconsciously influence the audience and persuade them without them knowing?” This is a valid conclusion but we will now address this ethical dilemma that we may have already raised…


Decreased operating costs with AI

AI can help reduce operating costs by automating various tasks. For example, an AI system can automatically generate content metadata, identify key scenes, generate industry-standard thumbnails, trailers, and descriptions, thus ensuring accuracy and consistency.

AI can also be used to develop a unified search system that integrates large language models with all internal information sources, thus providing a unified search interface to consult this content, and an external one, such as IMDB or the Internet in general.

And here we insist on the fact that we do not mean replacing people with machines, but about people stopping doing repetitive and automatable tasks that do not add any value and starting to do interesting things that really contribute to the creative process and to viewers, which at the end of the day is what our industry is all about.


The power of hypercustomization

As we have already mentioned, hypercustomization is one of the most significant benefits of AI in the audiovisual industry. By analyzing viewer behavior and preferences, AI can create customized content recommendations, improving viewer experience and increasing production consumption.

Hypercustomization goes beyond simply recommending content based on past viewing history. It can also take into account factors such as viewer mood, time of day, and even the weather, to recommend content that is more likely to be enjoyed. This level of customization can significantly increase content consumption and viewer loyalty, this leading to an increased revenue.

At this point we began to set foot on a slippery slope: Is it ethical to make use of the predicted mood of viewers to tailor our offering to them? To what extent are such predictions accurate or can unconsciously influence the audience? And this is precisely the crux of the matter, in which the viewer is made aware of why we recommend certain content or their platform behaves in such a way. As long as it is made explicit that the recommendation or customization offered is based on certain subjective criteria, ethics is not a problem but, when we use these data to surreptitiously modulate the behavior of our audience without them being aware of it, that is another matter.


The role of emotional analytics

Emotional analytics is another area where AI can have a significant impact within the audiovisual industry. By analyzing viewers’ reactions and emotions, AI can provide insights into what type of content is most liked by whom and at what time of the day, week, or year that happens. This can help content creators produce more emotionally engaging material.

Emotional analytics can also be used to customize content recommendations. For example, if a viewer tends to enjoy productions that bring out a certain emotion, AI can recommend similar content. This can further enhance viewer experience and increase audiovisual consumption.


The challenges that lie ahead

Although we have commented on it throughout the article, it is worth emphasizing that one must be very careful when applying this new tool to avoid making mistakes in the attempt. And this happens with every significant innovation or progress being made for mankind in general, we need time to learn how to use it. AI is not an exception, but just one more example of this, and very significant one.

Despite the numerous benefits of AI, there are also challenges that need to be addressed. One of the main challenges is the potential for bias in AI algorithms. If not managed properly, AI can reinforce existing biases, thus leading to unfair outcomes. When an AI model is implemented -this being really complex- it can have unexpected results over time or whenever more data is added in a massive way. Therefore, constant monitoring is very necessary and these biases must be corrected in all honesty.

Another challenge is the potential for discontinued jobs. As AI automates various tasks, there is a risk that some jobs may become obsolete. Therefore, companies need to invest in retraining and upskilling their staff to ensure they can adapt to the changing landscape.

And right here we’ve outlined certain solutions. These tasks that were previously performed manually, and that therefore required workmanship, have been automated, but new tasks have been created such as the monitoring of algorithms, the correction of biases or the optimization of systems. And, just as the steam engine did not end direct labor in the industrial revolution, AI is not going to put us all on the dole overnight.



In sum, AI is transforming the audiovisual industry, offering numerous opportunities for hypercustomization, increased viewer content consumption, new revenue streams, and reduced operating costs. However, to make the most of these opportunities, companies need to invest in the necessary infrastructure and skills, as well as to address the challenges posed by AI such as task automation or the biases that such algorithms can introduce.

As AI keeps evolving, it will undoubtedly continue to shape the future of the audiovisual industry, making it more dynamic, attractive and profitable. And, as always, we will have to adapt to it. Today our industry is more exciting than it was 5, 10 or 15 years ago, and less so than it will be a decade from now. Not all industries can say the same, let’s enjoy the moment!

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