The National Association of Broadcasters conference, held April 15–19 in Las Vegas, traditionally highlights the infrastructure and technology used to deliver content to entertainment-hungry viewers. This year was no different, although the heavy emphasis on artificial intelligence was new.

Fueled by the tech industry's interest in AI platforms like ChatGPT, multiple tech and infrastructure vendors at the National Association of Broadcasters (NAB) conference attempted to explain how AI is providing increased efficiency, along with potentially new business models, to the broadcast industry.

In discussions with exhibitors at the show, a key narrative about AI focused on how broadcasters, pay TV service providers and streaming video platforms have been using it for years, highlighting that the broadcast industry was not exactly jumping on the AI bandwagon at the last minute.

Specific discussions about AI platforms and capabilities being used in the industry generally fell into three AI segments: data analytics, machine learning and generative AI.

Data analytics

Both content creators and infrastructure vendors at NAB noted that the industry has long used data analytics to produce near real-time insights into viewer behavior. Using data tracking to find out who is watching what provides immediate feedback to broadcasters and content creators. Companies like Netflix and Hulu have mastered the use of data analytics to provide viewers with recommendation engine-based programming suggestions, while traditional over-the-air broadcasters and cable channels also use analytics to gauge the success of advertising campaigns.

Also important is the capability that data analytics can provide when it comes to tracking content consumption outside of the traditional broadcast environment, to include content viewed on mobile devices and social media platforms.

Machine learning

Machine learning in the broadcast industry seems to be defined in several ways, although the most common definition revolves around using a detailed mathematical model to digest and learn from large datasets. This learning capability can then be used to create content ranging from animated cartoons to virtual environments used in gaming to creating voice prints used by content creators to replicate voices. Some interesting discussions at NAB about voice prints included using AI to replicate the voices of deceased actors or celebrities, along with using voiceprints to translate a speech or conversation in a program into multiple languages.

During one keynote session, an executive from Veritone Inc., a company that produces enterprise AI solutions, discussed how the company is providing its AI technology to soccer star Cristiano Ronaldo. Using a synthetic voice platform, Ronaldo can post updates on his personal website in multiple languages, including Arabic, Hindi and Mandarin.

Generative AI

With all the buzz surrounding large language models developed by companies such as OpenAI LLC and Alphabet Inc., generative AI and its potential in the broadcast industry was a hot topic at NAB. Generative AI can be defined as an AI platform that creates or generates new content on its own, largely based on mining existing patterns and datasets. This generated content can range from text to images to software code but also includes video and audio.

While generative AI is not as broadly used in the broadcast industry compared to machine learning and data analytics, its potential is viewed as being significant.

One possible use of generative AI is focused on making existing broadcast content easier to consume by condensing existing video clips into bite-sized chunks of content. In this example, a generative AI platform could take an hourlong piece of video, such as a TV documentary or a news program, and generate a 60-second summary that can be distributed online or via social media.

Another potential use case for content creators is generative AI's ability to search for and produce specific pieces of video that can be integrated into the production of new content. An example discussed at the NAB was a sports program producing a 30-second clip covering the previous night's baseball scores, along with some recorded game action as a background to the clip. Using a generative AI platform, the producer said, "Show me baseball highlights from yesterday," and the platform searched existing online baseball footage and produced an appropriate series of video clips.

An actual example of generative AI usage in the US broadcast industry was announced in early May by Gray Television Inc., the nation's second-largest TV broadcaster. Gray signed a deal with Waymark, an AI solutions developer based in Detroit, to allow Gray's local TV stations to use Waymark's generative AI video production platform.

Waymark's AI solution gives local businesses the ability to produce TV commercials with voiceovers in five minutes or less. Both companies said Gray ultimately intends to use the platform at all of its 113 local TV stations.

Not a magic wand

While AI in the broadcast industry was one of the key themes at the NAB, some industry voices at the show were less effusive about the potential impact of AI in the near future. One executive from Synamedia Ltd., a leading video technology provider to the broadcast industry, cautioned that while AI can offer significant potential for several broadcast applications, it is "not a magic wand."

The executive said AI usually is deployed on a case-by-case basis, whether it is a data analytics tool, a machine learning capability, or a generative AI platform. The executive also noted that it takes time for most broadcast and video AI applications to improve and become useful, so for those looking for AI to rapidly transform the industry, the executive's advice was to "be patient."

Technology is a regular feature from Kagan, a media research group within S&P Global Market Intelligence's TMT offering, providing exclusive research and commentary.