[Keeping Tempo With Music Biz] — The Catalog Intelligence Gap Costing Music Companies More Than They Know: Op-Ed from Vistex’s Aashish Pathak

How AI turns music catalog data into a revenue strategy

Every year, Luminate drops a report that should set music company strategy for the next twelve months. It’s detailed, publicly available, and packed with signals most organizations never act on. Not because the data is hard to find, but because turning market signals into catalog decisions requires a kind of cross-functional thinking most companies aren’t structured to do.

This gap is quietly costing the industry more than anyone wants to admit.

Take a few findings from Luminate’s most recent report. Jazz represents 21.7% of physical consumption against a 7% industry average — a three-times over-index. Children’s music accounts for 10.6% of video stream share, the highest of any genre. And in the global Top 5 video streams, you’ll find K-Pop, Bollywood, and a children’s brand. Not a lot of English-dominant artists. The market is telling us something about where appetite lives — by format, territory and genre.

The uncomfortable question is: What is your company actually doing with that information?

Why the Data Rarely Drives Decisions
It’s not primarily a technology problem. Inside most mid-size labels and publishers, catalog data is fragmented before it even gets near a strategy conversation. There’s no shared vocabulary, no single source of truth, and no clean line between what the market is signaling and what a company actually owns and can exploit.

At Vistex, we see this consistently in our work with music publishers, labels and distributors: even when the right data exists, it doesn’t always live in one place, and sometimes not in a format that actually informs revenue decisions.

Layer on top of that a culture where employees worry new tools might eliminate their roles, and you get slowed adoption, deeper knowledge silos, and a widening gap between the companies reading the room and the ones still sorting spreadsheets.

The result is that publicly available market intelligence sits on the shelf while catalog decisions get made the way they always have: on instinct, relationships and habit.

What AI Can — And Can’t — Do
I hear a lot of noise about AI right now, most of it focused on content generation: AI writing songs, cloning voices, and flooding DSPs with synthetic tracks. Those are real conversations worth having. But from where I sit, they’re pulling attention away from a more immediate and arguably more valuable AI application: pattern recognition at catalog scale.

In environments where rights, royalty, and contract data are already structured and connected, that kind of analysis is not just possible, but repeatable.

I want you to think about what it would take to manually cross-reference streaming velocity, territory performance, format history, rights terms, and genre metadata across tens of thousands of titles. More than a Tuesday afternoon project, to be sure. It’s the kind of work that either doesn’t get done or gets done partially by one analyst who’s already buried in three other priorities. AI handles it in minutes. Methodically, not creatively. That’s the point.

Here’s what that looks like against the Luminate data:

• Jazz and physical formats. Jazz dramatically over-indexes on physical consumption. An AI layer could flag jazz titles in your catalog with no recent physical release, rank them by streaming velocity and territory, and deliver your physical team a prioritized shortlist rather than a research project. The intelligence is already in your data. AI connects the dots.
• Children’s content and video. Children’s music leads all genres in video stream share, but which titles in your catalog have strong audio streaming numbers and no music video? Those are your highest-probability video investments. A human could spend weeks building that list manually; AI can surface it in minutes.
• Non-English catalog and global markets. When K-Pop, Bollywood, and children’s content dominate global video rankings, it’s a clear signal that audience appetite has shifted. AI can flag non-English titles based on language and territory metadata, surfacing them in high-growth markets for A&R or licensing conversations that might never have happened otherwise.

In my opinion, none of these use cases requires exotic technology. They require a pattern-recognition layer connecting market signals to catalog data, and the organizational will to act on what it finds.

The Licensing Opportunity You Don’t Know You’re Missing
Here’s a scenario I see playing out in music licensing: A music supervisor comes looking for something “moody, ethereal, and evening-ish.” If your catalog hasn’t been tagged with descriptive metadata, you don’t lose that placement because you never even knew it existed. You were invisible in the search.

AI-assisted metadata enrichment closes that gap. It’s not glamorous work, but the upside is concrete: more discoverability, sync placements, and revenue from a catalog that’s already been recorded and paid for.

This is where rights management becomes undervalued as a data source. In practice, the companies that treat rights, royalties, and contract data as interconnected rather than siloed are the ones that can actually act on these opportunities at scale.

The Honest Caveat: Bad Data Gets Worse Faster
Before anyone rushes to layer AI on top of their catalog operations, let me stress this plainly and clearly: AI doesn’t compensate for poor data hygiene; it amplifies whatever you feed it. Bad input produces confident-sounding bad output faster and at a greater scale.

The most common culprits are missing or incorrectly entered metadata. Gaps cost time: tracking down missing information pulls resources away from higher-value work. Errors create liability: wrong songwriter credits mean royalties flow to the wrong people, and those mistakes damage relationships and invite legal exposure.

Ideally, companies get their data house in order before pursuing AI-driven catalog intelligence. In practice, many organizations are doing both simultaneously. Either way, the data foundation is the thing everything else is built on.

At Vistex, we see that foundation as more than just cleanliness — it’s about having the operational discipline and systems in place to capture, connect, and maintain data integrity across the entire revenue lifecycle.

This Is a Compounding Advantage
The companies that figure this out first will have better dashboards and will make better capital allocation decisions faster, with less internal friction. If one company extracts even 5% more exploitable value from its catalog each year, the compounding effect over 5-10 years is significant. Licensing decisions made on real data instead of instinct. Physical release budgets allocated to genres where consumption actually supports them. Sync pitches targeted to supervisors who are already searching for what you have.

The companies that don’t build this capability will keep doing what they’ve always done. Some will get lucky. But increasingly, they’ll be operating on instinct in a market where their competitors are operating on intelligence.

Where to Start
For most music companies, the starting point is the same: bring together the people in your organization who understand your catalog, data architecture, and rights landscape. Agree on a shared data standard before anything else.

In our work with these companies, the next step is turning that alignment into action — bringing rights, royalty, and contract data into a shared operational framework. Then map the gap between your current catalog visibility and the market signals you already have access to.

The future advantage in music will come from decision velocity: the ability to connect market signals to catalog assets and act on them before the window closes or your competitors do. That’s not my prediction about where AI is going; it’s a description of what the fastest-moving companies are already doing.

Increasingly, the difference comes down to whether your rights, royalty, and contract data function as a connected system or continue to limit the decisions you can make.

Written by Aashish Pathak, Music Solutions Engineer at Vistex— Pathak matches rights, royalties, and catalog management software solutions to the needs of music publishers and record labels.

You can read past Keeping Tempo’ articles via the portal linked here. And, stay tuned for more insightful discussions from our members and partners from across the industry!