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Why AI labels and metadata now matter in licensing
A new industry report from Cyanite, MediaTracks, and Marmoset reveals how professionals are navigating the rise of AI-generated music. Read here.
AI’s move to the mainstream has changed what people expect from music catalogs. Licensing teams now look for clearer data about the music they review. They want to know whether it’s human-made or AI-generated, and they also look for details that help place the music in the right creative or cultural setting. Many check these cues first, then move on to mood or tone.
At Cyanite, we partnered with MediaTracks and Marmoset to understand the level of transparency and cultural context music licensing professionals expect when reviewing AI-generated music. MediaTracks and Marmoset surveyed 144 people across their professional communities—including music supervisors, filmmakers, advertisers, and producers—and we worked with them to interpret the findings and publish this report.
The responses revealed that most people want clear labeling when AI is involved. Yet, despite this shared desire for transparency, only about half of the respondents said they would only work with human-made music.
The full study goes deeper into these findings and shows how they play out in real licensing work.
Why we ran this study
We wanted a clear view of how people make decisions when AI enters the picture. The conversation around AI in music moves fast, and many teams now ask for context that helps them explain their selections to clients. This study aimed to find out which parts of the metadata give them that confidence.
It also looked at how origin details and creator context guide searches and reviews. We wanted to see where metadata supports the day-to-day licensing process and where there are gaps.
Transparency is now a baseline expectation
97% of respondents said they want AI-generated music to be clearly labeled, and 37% used the word “transparency” in their written responses. They want a straightforward read on what they’re listening to. Some tied this to copyright worries. One person put it simply:
“I’m concerned that if it were AI-generated, where did the AI take the themes or phrases from? Possible copyright infringement issues.”
Transparency doesn’t just apply to the AI label. We found that respondents also see context as part of that clarity—knowing who made the music and where it comes from. This information helps them assess whether the music is a good fit for the project. They use it during searches to filter for cultural background or anything else that’s relevant to the brief.
What these findings mean for the industry
These findings show how much clarity now shapes day-to-day work in music catalogs. People expect AI music to be labeled accordingly, and they lean on context to move through searches and briefs without second-guessing their choices. Human-made music is still highly valued. The real change has been in how teams use origin details to feel sure about their selection.
This sets a new bar for how catalogs present their music. Teams want dependable information, including context that helps them avoid missteps in projects that depend on cultural accuracy or narrative alignment.
This finding ties into how Cyanite supports catalogs today. Our audio-first analysis gives people a clear read of the music itself, which sits alongside the cultural or creative context they already rely on. It helps teams search with more clarity and meet the expectations that are now shaping the industry.
How Cyanite’s advanced search fits in
The study showed how important cultural background and creator context are when people review music. Teams often keep their own notes and metadata for this reason. Cyanite’s Advanced Search supports that need by letting catalogs add and use their own custom information in the search.
Custom Metadata Upload – one of many features of our new Advanced Search, lets you upload your own tags – such as cultural or contextual details that don’t come from the audio analysis – and use them as filters. You can set your own metadata criteria first, and the system will search only within the tracks that match those inputs.
When you then run a Similarity- or Free Text Search, the model evaluates musical similarity inside that filtered subset. As a result, search and discovery reflects both the sound of a track and the context around it.
You can search your catalog for “upbeat indie rock” but you can also search for “upbeat indie rock, human-produced, female-led, one-stop cleared, independent.“
Read the full report
The survey drew responses from people who license music often as part of their work and feel the impact of unclear metadata. Their answers show how they think about AI involvement, creator background, and the context they need when they search.
The full report brings these findings together with information about the study—who took part, how often they search, the questions they answered, and how responses differed by role. It also includes partner insights from MediaTracks and Marmoset, along with charts and quotes that show how transparency and context shape real choices in licensing.
You can read the full study here.
