AI is supercharging majors' data analysis. Indies need to assemble or get left behind

21 March 2025 - Editorial

Comment: Henrik Hansen - Business Lead, Rough Bones

Much of the conversation around artificial intelligence in music has centered on its existential threat to human creativity. We've all seen the headlines about AI-generated songs mimicking artists' voices and the looming copyright battles that will inevitably follow. But there's another AI revolution happening quietly behind the scenes that could permanently alter the industry's power dynamics.

What nobody is talking about is how AI will create an even bigger disparity between majors and independents when it comes to data analysis.

Make no mistake, data is the most valuable commodity in the music industry today. With enough data, and the tools and expertise to leverage it effectively, a record label can crunch the numbers to not only validate an A&R approach but illuminate brand new opportunities that were previously under the radar.

At my own independent label, Rough Bones, our recent expansion into Afrobeat was greatly informed by data. Using our own in-house algorithms and a mass of publicly available information, we've been able to create a robust model to determine ROI for each A&R decision we make. This has allowed us to answer critical questions: Is this artist worth signing? What's a fair advance? Are there emerging markets we should be targeting?

AI has greatly enhanced our ability to gather and interpret this data. When an A&R executive brings an artist they believe in, we can now run comprehensive analyses that either validate that "gut feeling" or suggest we look elsewhere. In an industry where margins are increasingly tight, this approach maximises our limited resources.

It's no secret that the major music corporations are investing heavily in AI-powered data analysis. They also have incredible banks of information, based on their own catalogs alone, with which to power that analysis. With their technological resources and financial firepower, they're developing increasingly sophisticated algorithms that provide ever more accurate forecasting and market exploration.

In the music industry and beyond, data is now the biggest weapon for companies competing for the next big thing. When we see major acquisitions like UMG snapping up Downtown and PIAS, we think of them as grabbing market share - perhaps more importantly, however, is the fact that with each new acquisition, they're adding to their data banks. How much invaluable information does the addition of FUGA alone give UMG?

Consider also the potential data advantages that could come from special relationships between majors and DSPs. While we don't know the exact details of deals between the biggest music companies and their streaming equivalents, it's not unreasonable to wonder whether such partnerships might include privileged access to data that indies simply don't have.

The shift is already visible in the majors' strategies. They've largely moved away from developing artists, focusing instead on marketing and acquisition. Why take risks developing unknown talent when data can tell you exactly which established artists and labels to acquire?

On an individual basis, independent labels are sorely lacking when it comes to data and analytical capabilities. Anecdotally speaking, based on conversations I have had with peers, I'm often surprised by how little thought even the bigger independent companies have given to data-driven technologies. Without tech backgrounds, many label heads don't fully appreciate what modern data analysis can accomplish.

So how can indies make sure the distance between themselves and the majors in this great data race doesn't become insurmountable?

We need to come together. Data is no good when it's fragmented. Just as the majors consolidate data through acquisition, we need to consolidate our data through collaboration. The tools are already available to leverage that data set once it is up to scale.

Even a small number of labels with a combined catalogue of, say, one thousand recordings, could create a powerful dataset. With the right anonymised data-sharing framework, independents have the ability to create systems that help them identify market gaps, forecast song performance, and discover emerging talent uniquely positioned for growth.

Such a system could even suggest unexpected collaborations across genres or labels that might generate additional income and new opportunities that individual labels would never have discovered on their own.

There will, of course, be a finer details to work out: finding the right partners of similar size and complementary catalogs for initial proof-of-concept collaborations will be important.

What's certain is that the independent sector needs to act soon. As majors continue to develop their AI capabilities and acquire more data, the gulf will only widen. Without a unified front from independents to leverage collective data power, we risk being left behind in an increasingly data-driven music industry.

The technology exists. The potential benefits are clear. All that remains is for independents to recognise that, in the AI age, collaboration might be our only path to competitiveness.

The question is: Who is willing to share their most precious commodity?