At the outset of these posts on music technology, I asked a top-level question: What kind of music technology ecosystem will emerge that uses artificial intelligence (AI) and remains fit for purpose for the industry?
The subsequent series of posts on "AI and Music" began with nine questions for music industry leadership. Those questions looked at AI through different lenses: creativity and authorship, licensing, attribution, product development, fraud, infrastructure, and education. Having addressed those nine questions, what remains to be answered?
Those earlier questions and answers taken together point toward reflecting and acting on a larger question: Who governs the broader and increasingly interconnected music technology ecosystem in which AI and other technologies are being deployed in so many ways?
Framed this way, AI itself is not the issue but a "forcing function" for that wider question to be addressed. It encompasses creators, producers, studios, technology developers, labels and publishers, distributors, streaming services, rights organizations, standards bodies, and others involved in the creation, production, distribution, consumption, and reuse of music. It also includes the technologies and infrastructure through which music, rights, identities, permissions, metadata, and payments move.
AI exposes how interdependent those systems have become.
An AI tool used during creation can affect information later required for attribution or licensing; a provenance claim created in one piece of software may need to survive distribution through several other systems; and an automated platform decision can determine whether a recording is distributed, monetized, or characterized as fraudulent.
One company's decisions about training data can have consequences for creators and rights holders elsewhere in the ecosystem. No single company can fully solve these problems.
This is where ecosystem governance becomes important. Governance at this level does not mean regulation or centralized control. It means agreeing on common principles, standards, responsibilities, processes, and mechanisms that allow independent organizations to interact reliably within a framework built on trust.
The International Organization for Standardization (ISO) Standard on organizational governance puts an organization's purpose at the center of governance and surrounds it with value generation, strategy, oversight, and accountability, supported by stakeholder engagement, risk governance, and responsible decision making.[1] A second standard adds an important lesson for organizations that use AI: as with any other tool or technology, the use of AI does not displace human and organizational accountability for decisions and outcomes.[2]
Although both standards focus on governance within organizations, they can also help frame questions about governance across an ecosystem of independently governed organizations. Together, they raise the following questions that need to be addressed across the music technology ecosystem.
1. What desired outcomes require collective action? Where are interoperability, transparency, and trust better achieved collectively rather than through individual company action alone?
2. Who needs to participate? How should creators, technology companies, rights holders, platforms, independents, standards organizations, and other affected stakeholders contribute to shared solutions?
3. What information needs to accompany a piece of music or other creative work throughout its life cycle? Can identity, credits, rights, permissions, provenance, and AI-use information move reliably from creation through distribution and reuse?
4. Where does accountability sit when technology crosses organizational boundaries? If several companies contribute to an AI-enabled workflow, how is responsibility apportioned between them?
5. How should existing standards work together? Does the industry need more standards—or better coordination between the standards, identifiers, and protocols that already exist?
6. How should ecosystem risks be managed? What happens when a particular risk (fraud, cybersecurity, privacy, provenance, or data quality) is introduced or encountered, but the resulting costs or consequences are borne elsewhere in the ecosystem?
7. What does meaningful transparency require? What information should be visible to business partners, creators, auditors, regulators, or consumers, and what should legitimately remain confidential?
8. How should trust be demonstrated? Can common commitments be supported by testing, assurance, certification, dispute resolution, and mechanisms for correction?
9. Can trust become interoperable? Could organizations using very different technologies and business models nevertheless rely on a common framework for identity, accountability, provenance, and responsible conduct?
These questions extend well beyond AI. AI has simply made them harder to ignore.
The next challenge for music technology is therefore not only to continue to innovate; it is also to develop the shared infrastructure and governance that allow independent organizations and technologies to nonetheless work together with confidence.
The objective is an ecosystem in which interoperability, transparency, and trust are not dependent solely on individual relationships or company policies but are properties of the system itself. Industry bodies—such as the new Music Tech Coalition—can help achieve that objective.
[1] ISO 37000:2021, Governance of organizations: Guidance.
[2] ISO/IEC 38507:2022, Information technology: Governance of IT: Governance implications of the use of artificial intelligence by organizations.