AI-assisted music raises new transparency risks for brands

When a familiar chorus climbs the charts, most listeners are not thinking about the production workflow. They are thinking about where they first heard it, what it meant back then, and why it still hits now. That is the backdrop for a Queensland-based producer’s cover of Madonna’s “Like a Prayer” that topped Australian commercial radio charts and also reached No. 1 on the global iTunes electronic music charts. The track later became a flashpoint for a question that is spreading fast across music and advertising: if AI helped, how should creators and brands say so? The cover’s success points to something music industry leaders keep returning to: audiences are not emotionally responding to AI itself. They are responding to a song that already carries decades of cultural meaning. Level Two Music’s managing director, Jen Taunton, framed it as “emotional equity” built through Madonna, nostalgia, familiarity, and songwriting. AI can emulate aspects of the sound, but it cannot recreate the history that gives a song its lasting value. In that reading, AI is not creating the connection, it is borrowing it. That distinction matters for marketing because so much advertising audio is designed to shortcut to feeling: recognition, comfort, irony, hype. AI can make it easier to generate variations, but it does not automatically create meaning. Meaning is still inherited from culture, context, and people. After speculation about AI use, producer Josh Fawaz said on Instagram that he uses the technology “as a tool,” and he updated the song’s Spotify credits to reflect AI usage. That sequence is part of what made the moment sticky: listeners were not only reacting to what they heard, they were reacting to what they learned after. Several sound and music executives highlighted that curiosity about process is not new. As Squeak E. Clean Studios’ head of sound, Paul Le Couteur, put it, people often want to know how a track was created because “no one likes to be fooled.” Mosaic Music and Sound’s head of creative, Adam Moses, argued the bigger issue is not the tool but the silence around how it was used. In his view, audiences connect to the belief that a person meant something. If that belief collapses, the connection can collapse too. This is where AI-assisted audio becomes different from earlier “machine” eras like synths, auto-tune, or sampling. Those technologies triggered moral panics too, but the current friction is about authorship and provenance: what is human, what is assisted, and what is fully generated. For marketers, the ambiguity becomes practical risk. Moses described fully generated music as a legal grey zone, with unclear training data, unclear ownership, and unclear indemnity. When real media spend is involved, “we’re not entirely sure who made this or whether we own it” is the kind of uncertainty brands tend to avoid. At the same time, Australia saw a very different kind of viral hit: “Eastern Rosellas,” written by seven-year-old Miles Phillips about the birds he saw on holiday. The song was co-created with his dad, Joel Phillips, and it was later covered by well-known Australian artists including Ben Lee, Ball Park Music, and Megan Washington. Part of the song’s appeal is the story attached to it. Taunton’s point fits here too: audiences rarely connect with how music was made, but with what it represents. In this case, it is wholesome, specific, and human in a way that feels legible without any technical explanation. Placed next to an AI-assisted chart-topper, “Eastern Rosellas” reads like a cultural palate cleanser. It is a reminder that in moments of technological uncertainty, people often gravitate toward work that signals clear authorship and uncomplicated sincerity. That does not mean audiences will reject AI music outright. It means context and trust will shape how people feel about it, especially when the backstory becomes part of the conversation. If AI audio is going to be a bigger part of advertising, the deciding factor is likely to be trust, not novelty. The industry voices in this story all return to the same pressure point: transparency. Treat disclosure as part of the creative, not a legal footnote If audiences feel misled, the backlash is not about technology. It is about being tricked. For brands, that becomes a reputation story, and those travel fast. Assume provenance questions will show up earlier in the process Moses noted agencies and clients are already asking what is human, what is assisted, who owns what, and how AI fits into their work. Expect these questions to move upstream into briefing, not just approvals. Do not let “AI-assisted” become an ambiguity shield The Fawaz situation was notable because “nobody actually knows what was AI and what wasn’t.” If a brand cannot explain what it licensed or commissioned, it increases both legal and trust risk. Build due diligence around “hot tracks” and identity signals Moses also warned that chart data, radio quotas, and “Australian artist” definitions are being gamed. If a campaign relies on cultural signals like local identity or authenticity, verification becomes part of media safety. Use AI for flexibility, but compete on taste and originality Squeak E. Clean Studios’ head of music, Max Wilkinson, expects brands to become more intentional rather than simply pro or anti AI. He also argued human collaboration stays valuable, because originality and taste become stronger differentiators when tools are widely available. Zooming out, AI is pushing advertising audio into a new phase where “how it was made” can matter as much as how it sounds, especially once a track leaves the studio and hits the comment sections. The bigger shift is cultural: consumers are becoming more interested in where creative work comes from. Brands that act like provenance is a real part of the story will likely navigate this era more smoothly than brands that treat AI as something to hide until someone notices.