Generative AI Applications Media Companies Can Use Now

It’s safe to say that everyone working in the media industry is aware of generative artificial intelligence (AI). As accessible platforms like ChatGPT have grown in popularity, they have spurred a number of reactions from those operating in media. Journalists and other content creators are anxious that AI could replace them, to the point where AI is one contributing factor to the dual writers’ and actors’ strikes halting Hollywood production.

Meanwhile, media leaders are viewing generative AI with equal doses of curiosity and skepticism. Some likely see AI as a flash in the pan, while others are eager to find ways to incorporate AI into their offerings and products in order to drive revenue. There are lots of broader applications for AI and machine learning within the media industry, which can solve some of the biggest issues.

When we focus specifically on generative AI and its ability to create entirely new data – including text, images, music, and code – there are several ways in which the tools can provide immediate value for media companies. Here are a few applications that media companies can take advantage of, right now.

Chatbots

One of the major things that separates generative AI from predictive AI is that generative AI requires far less data to generate an output. This is especially helpful when it comes to chatbot technology, which can craft responses using the question posed.

When we think of chatbots, we often think about applications that interact with consumers and subscribers. This is definitely an opportunity that media companies should pursue, updating their chatbot technology so that it can better answer user queries in public.

But the other use case is for an internal chatbot that can help content producers – reporters, editors, copyeditors, video producers, and others – navigate the massive amounts of historic content within media companies’ archives.

Reporters can use a chatbot to identify when certain people, subjects, and topics have been covered in the past, and what that coverage said. This ability to draw on institutional knowledge ensures consistency and accuracy, helping cement the strong relationship between media company and subscriber.

Improved search and website functionality

Another clear use case for generative AI is to help with content tagging, which immediately improves the user on-site experience through better search, easier navigation, and improved discoverability.

Big media companies have lots and lots of content hosted on their servers, going back decades. Historically, humans have had to tag this content based on topics and article subjects. This is critical work, because this meta tagging helps with on-site search, ensuring that users can find the content they are looking for.

Generative AI can automate the process of meta tagging, interpreting each piece of content and applying the necessary tags. This will free up content producers and editors from the manual task, giving them more time to focus on what they’re good at – creating content. At the same time, AI learns from the natural language searches that website users make. This further improves the tagging, adding both automation and improved discoverability.

The same generative AI language models can create short summaries of the articles, making it even easier for users to search and find the content they need, leading to deeper engagement.

Recommendations

One way many media companies are already utilizing AI and machine learning is through recommendation engines. For streaming platforms, these recommendation algorithms are often built with predictive AI to pinpoint trends, common behaviors, connections, and other patterns. By drawing on the entire subscriber base’s viewing habits, streamers can surface a recommendation on what to watch next to an individual user.

Media companies on the news and publishing side can use the same idea to build personalized home pages, but they can go a step further and use generative AI to build something new. This may mean weekly recommendation newsletters, built based on predictive algorithms that are then fed into a generative AI tool to create a completely unique newsletter for an individual subscriber. This completely automated process would deliver something unique, based on personal preferences, while being completely automatic. The improved meta tagging outlined above would further streamline this process, resulting in an engaging product that keeps subscribers interested and interacting with the media brand.

Building for the future

If there’s one important thing to keep in mind when mulling generative AI applications, it’s this: don’t be scared. AI is not coming to wipe out the media business, but propel the business forward. AI outputs are only as good as the originating content, so the companies with the best content stand to gain the most from this exciting new technology. And of course, no one has better content than the media business.

Here at 3Pillar we’re continuing to monitor the latest developments in AI and identify how media companies can apply AI to build breakthrough products that transform their business. To get started on a conversation about how AI can apply to your business, contact us.