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Two years after the introduction of ChatGPT, artificial intelligence has become a commodity at most radio stations. Many AI tools are in place, including voice creation solutions like ElevenLabs, image generators like Midjourney and, of course, ChatGPT itself. In many cases, trial and error is the strategy for implementing such tools, often combined with a promotion-driven approach.
However, the more possible it is, the more critical it is to focus on the application that really makes sense for radio.
Fiction writer Joanna Maciejewska’s quote hit the mark when she summarized AI’s potential: “I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.”
So, what are radio’s “laundry and dishes,” and what is its “art,” and how can AI impact these?
Efficiency and effectiveness
AI can raise a radio station’s operational efficiency and, therefore, help make it more effective. At the same time, AI can also add functional benefits, which will make a radio station more effective.
A good perspective for capturing value at radio stations is a (commercial) station’s typical value chain.
Radio’s prime business is still creating compelling content and selling the audience to advertisers.
AI can dock on many value chain positions and add corresponding value.
Radio stations have gathered a lot of expertise in using artificial voices for content creation, with mixed results. Although the quality of voices is generally very good, especially with news content, it is less the case with emotional talk and music breaks. There aren’t many successful cases where AI has entirely replaced human presenters. Instead, AI voices are often used as tools rather than complete presenters. AI as a presenter also comes with multiple obstacles, such as the lack of sourcing content, inconsistent text quality, mispronouncing names, a lack of intimate local knowledge, not integrating into workflows, missing software integration and overall quality control. Successful cases exist, often combined with other platforms or media, with a clear value-add in mind: More audio inventory.
Increasing the audio inventory
German news TV station ntv is a good example. It uses AI to turn content from TV and websites into audio, adds it to digital platforms and creates audio news bulletins for digital dashboards in cars. Consequently, it increases the company’s audio inventory and generates new digital audio revenues.
Similarly, German news giant Springer has produced an AI-fueled international version of its political podcast “Ronzheimer” using speech-to-speech translation technology. The result is a podcast with an audience potentially doubled in size.
AI is also a driver for improved production processes. It allows us to never start from scratch again and brings human creativity to a higher level because creativity can focus on the “art” and not the “laundry and dishes.” This works even better when AI is fully integrated into workflows and systems.
Using integrated AI with production software allows editing podcasts or radio in less time. By trimming the audio at the beginning and end, normalizing the audio and improving the overall quality of the audio, AI boosts the sound quality of the radio station and drives efficiency in the production department.
What are radio’s “laundry and dishes,”
and what is its “art”?
Adding context to audio
AI can add much more to audio. Metadata based on the transcription can offer multiple ways to use the audio — for better curation and searchability and ad-related context like categories and tags that can be used to target commercial messages.
Perhaps AI’s most prominent ability is to leverage sales. Based on a significant amount of data, it creates more sales opportunities by increasing the available inventory, adding targeting possibilities and — maybe the most potent application — personalizing messages. German supermarket chain Penny put this into action and delivered micro-localized audio spots to all its local markets, irrespective of how small or remote they were. The spots, created by the audiostack.ai artificial voice tool, contained local branding and featured products and dynamic pricing information, all updated weekly.
Such examples are just the beginning of AI’s implementation in radio station sales departments. The more digital the radio distribution will become, the bigger the potential force of AI.
AI might be a commodity at many radio stations, but when used wisely, it can significantly improve radio operations.
When everything is possible, it is a good idea to focus on the parts where AI can add potential value.
The author is chief digital officer at RTL Radio Deutschland.
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