AI x Music Publishing: Threat or Opportunity?

AI is clearly having a moment.

Photo by Possessed Photography

From image generation to songwriting tools, the emergence of generative AI has sparked widespread debate in the creative industries. In music publishing, much of the discussion has understandably centred around authorship, copyright, and how to safeguard the value of human creativity in a world where machines can mimic it.

But that’s only one side of the story.

If we zoom out and look beyond the courtroom, AI has the potential to quietly revolutionise the parts of publishing that have long struggled to keep pace with the digital era: rights management, data matching, royalty attribution, and revenue recovery. In fact, some of the most urgent problems in music publishing today aren’t creative- they’re operational. AI might just be the best tool we’ve ever had to solve them.

A system buckling under its own complexity

As the number of revenue opportunities has multiplied, from streams and syncs to TikTok snippets and in-game music, the administrative load on publishing has grown exponentially. Every usage should be tracked, matched to a composition, tied to accurate ownership data and ultimately paid out.

It’s no secret that systems are struggling to play catchup. Rights matching remains relatively manual, error-prone, and painfully slow, with royalty flows painfully fragmented. Data conflicts are often resolved by teams of humans eyeballing spreadsheets and PDFs. It isn’t hard to see the scalability issues- especially in a world where one song can generate millions of usage data points in a single day.

AI isn’t the de-facto bad guy

Let’s strip out the hype and look at practical applications. AI, especially in the form of machine learning, is already being used in other industries to automate complex, high-volume, and especially repetitive processes. In music publishing, we have no shortage of those! AI can make a real difference:

• Improved recording-composition matching: Matching sound recordings to the correct musical works is still one of the biggest challenges in the industry. AI can analyse audio, lyrics, metadata and contextual data relationships to make smarter, faster, and more accurate matches at scale.

  
  

• Intelligent metadata reconciliation: Conflicting or incomplete rights data is a major source of friction and revenue loss. AI models can identify patterns in how metadata errors occur, suggest corrections, and flag discrepancies before they convert to payment delays.

  
  

• Usage pattern detection: By analysing data across platforms and regions (i.e. comparing royalty statements and usage data), AI can surface where songs are being used but not yet claimed- recovering income that would otherwise fall through the cracks. It can uncover discrepancies in patterns across right types too- for example comparing publishing and recording sales data.

  
  

• Fraud detection and anomaly flagging: AI can also be trained to detect suspicious usage spikes or unusual royalty flows that human reviewers might miss. This helps protect revenue integrity and gives rights holders greater confidence in the ecosystem. AI is already being deployed by fraudsters, but we can fight fire with fire (... burning down the house!) in a similar way to how it is already used to detect AI generated content.

  
  

• Scalable ingestion and attribution: As new platforms emerge, so do new formats and reporting standards. AI can help ingest and normalise diverse datasets, reducing the integration burden and ensuring older/legacy formats marry up neatly with next generation exports and reporting (especially when it comes to data communications).

None of these are theoretical. Some publishers, CMOs, and tech platforms are already piloting or rolling out these tools (as a shout out, Fix Music has already developed and is deploying AI infrastructure to measurable effect). The opportunity is still wide open, and still under-leveraged.

Isn’t it ironic… 

There’s a common, and understandable, fear in creative circles that AI will devalue human artistry. But in the context of publishing administration, it’s primed to do just do the opposite.

If AI helps us reduce friction in the royalty chain, clean up broken metadata, and match more uses to more works- then more songwriters will get paid accurately, on time, and for the full value of their contributions. With these use cases it doesn’t diminish their role. It strengthens it. AI won’t ever replace human creativity- but it could help make sure that it’s properly accounted for.

This critically means it can shift human effort to higher-leverage work. Instead of spending time buried in spreadsheets, teams can focus on supporting writers, strategising licensing deals, or building new partnerships. AI isn’t a replacement for humans, but a force multiplier.

Considered adoption over copious court cases

AI isn’t magic. It needs cleaner data, human in-the-loop integration, and proper governance to be effective. But it’s a tool we should be welcoming into our publishing toolbox- not just automatically fighting as default spawn of the devil.

Parallels with early piracy and the music streaming evolution are obvious- the barrier is often not technical but cultural. As an industry, we need to become comfortable experimenting with innovative processes, partnering with startups and researchers, and crucially investing in the kind of infrastructure that makes forward thinking tech deployable. The aim isn’t to build an AI-powered cornucopia (note specific avoidance of the word ‘Utopia’....). It’s to build a more agile, efficient, and scalable ecosystem- one that serves rights holders and creators better and recovers the value currently lost in complexity.

AI as an opportunity for quiet revolution

While the legal debates around generative AI will continue to dominate headlines, there’s a quieter revolution waiting to happen under the hood. Publishing doesn’t need more flame-fanning about dystopian futures. It needs practical tools to meet the challenges of today, and of tomorrow.

AI, when applied well, can help music publishing finally live up to the complexity of the digital era- and help creators thrive within it.

What if AI’s biggest impact on music wasn’t the songs it helps create- but the money it helps recover?