The technologies behind artificial intelligence (AI) have been around in various capacities for years, whether through algorithmic recommendations on platforms like Spotify and Netflix or auto-tagging a company’s photo library via object detection models. But in the past year, generative AI in chatbots like ChatGPT and image generators like Midjourney have led to an explosion of interest and optimism in the space. Statista predicts the AI industry will grow twentyfold by 2030, up to two trillion dollars. The field is rapidly developing and attracting regulators’ attention on both sides of the pond, most recently demonstrated by an executive order from President Biden.
AI has the potential to transform pretty much every industry, from law to agriculture to defense, in a way not seen since the advent of the personal computer. In the media and entertainment sector, it has already been the cause of disruption. Its usage was a key sticking point in 2023 contract negotiations by the Screen Actors Guild and the Writers Guild of America, as rank-and-file actors fought against signing over their likenesses to be generated in perpetuity and writers fought against the outsourcing of their labor to AI.
However, usage of AI in Media and Entertainment (M&E) doesn’t need to be controversial. It’s already being used in many applications, and holds exciting possibilities to streamline workflows, boost creativity, and inspire new types of art. In this article, we’ll explore some of those use cases and outline ways creatives can wield these technologies to their own benefit.
The Media and Entertainment sector has long been a pioneer in the use of technology, employing algorithms to generate eye-popping special effects in movies as old as Westworld (1973) and Tron (1982). More recently, algorithms have been used to generate entire worlds in games like No Man’s Sky. Algorithms also order our social media feeds, defining not only the public’s experience of the internet but also the ways media organizations frame and distribute their content.
The rise of AI is no different. As the technology continues to mature, creatives in the M&E sector should look to leverage AI in five key ways:
We’ll discuss all of these in more depth below. But first, let’s take a quick, high-level look at why we should be using these technologies in the first place.
One thing everyone seems to agree on about AI is that it’s going to change things pretty dramatically. Some of the benefits it can bring to the media and entertainment sector in particular include:
Now that we’ve covered the why, let’s dig more into the how.
When we talk about AI these days, generative AI gets much of the focus, and for good reason: It’s impressive! Generative AI typically works by feeding many examples of a given type of content into a model that learns to make associations between them and spot patterns. Users can then have the AI make original creations based off of those associations. A model trained on numerous examples of fine art can create a painting of a Lamborghini in Van Gogh’s distinct style, assuming a user first requested such a thing.
Some of the types of media that AI can currently create include:
People working in creative disciplines sometimes look at technologies like the above and see them as a threat. But they can also just as easily be used to enhance creativity. Think of AI the way Steve Jobs thought of the computer: as a “bicycle for our minds,” a tool humans created to perform tasks better than we would’ve been able to otherwise.
Here are a few of ways AI can help the creative process:
As fun as the creative process is, some aspects of it can just be boring. AI is particularly good at speeding up some of the more tedious and time-consuming aspects of content creation across media and entertainment workflows. Let’s take a look at some of the ways in which AI can help creative teams speed through the boring stuff so they can focus on the more interesting parts of the workflow.
All of these tools help speed up pre-existing creative workflows in the media industry, cutting out time-consuming processes and allowing creatives to devote more attention to deeper creative efforts.
One futuristic dream people occasionally float about AI is the idea of completely, 100% personalized content: an AI learns your exact favorite medium and artists within it and usage patterns and generates an entertainment specifically for you. This seems as interesting as it does a little deflating, particularly for people who like to be surprised or challenged by the culture they consume.
Perhaps more interesting is the ability of AI to not generate completely custom-built content for each user but to personalize it toward them. For example, recommendation algorithms — like those on TikTok, Spotify, or Netflix — are getting increasingly better at solving the problem of “decision fatigue” for users, proactively suggesting things they may like. Additionally, B2B tools like Tavus, Maverick, and bHUman can be used to make customized videos for sales, marketing, and customer service purposes.
Surely, personalization and repurposing of data raise some privacy concerns, so it’s worth diving into how we can remain ethical while exploring these exciting technologies.
With a new technology as powerful as AI, ethical concerns are top of mind. Some of the earliest use-cases of technology like deepfakes were non consensual, for example, and many writers and news platforms are working to protect their work from being scraped for use in large language models. For these reasons, it’s important to keep up with ethical best practices in AI. The technology will only be used more and more widely, so these are likely to evolve, but a few principles to keep in mind are:
For now, it’s best to label AI-generated work, particularly if it’s for a client.
Use diverse datasets
Many current AI models reflect the biases of the media that informed them, reinforcing pre-existing social stereotypes. For this reason, models should be trained on diverse datasets and even have their outputs corrected to feature more diverse output.
Don’t use Large Language Models (LLMs) drawn from work that hasn’t been explicitly licensed for these uses. For example, Getty created a custom AI tool trained on its library, meaning any output from that tool is built on images licensed for AI training.
Promote human-AI collaboration
Look for places to streamline and enhance workflows through AI, as well as creating new types of human art, rather than to automate the process of creativity. (Audiences respond better to a human touch, too.)
Use any AI-powered analytics about consumption patterns responsibly, ensuring that all user data is anonymized and properly encrypted.
Keep up with AI ethics
Stay abreast of developments relative to this exact topic. Perhaps transparency becomes less important as the technology becomes widespread but the choice of model carries larger ethical weight. Like the technology itself, these conversations will move quickly.
While the ethical guidelines above are the most important pointer in this article, staying forward-focused about AI use cases is probably the most fun. A lot of this seems like science fiction. That’s part of the reason to keep a toe in this space: for all its disruptive capability, AI’s powers are legitimately transformative, and the creative teams who wield it will not only have a competitive advantage but a creative one.
Likely future AI use cases include:
Ultimately, no one can really predict what far-flung dreams AI can help make real. But one thing is certain: vastly more content will be generated. LucidLink can seamlessly embed AI-generated content in media workflows.
It features best-in-class security and encrypted storage, helping creative teams comply with AI best practices. It facilitates instantaneous collaboration and sharing regardless of where creative teams are located, working to supercharge creative workflows just like AI does. Exploring how LucidLink can help your creative teams and try for free.
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