AI adoption in music publishing

Over the past year, many conversations in music publishing seem to begin with the same question:

What should we be doing about AI right now?

The pressure to respond is real, coming from vendors, partners, and sometimes from within our own organizations.

What has stood out to me, though, is how rarely these conversations are actually about technology.

More often, they are about workflows, data, and responsibility.

In one discussion, different teams were imagining very different outcomes from the same initiative.

Editorial teams were thinking about catalog understanding and context.

Operations hoped to reduce manual processes.

Leadership was looking for strategic insight.

Each perspective made sense, but alignment had not yet happened.

In another case, a publisher exploring automation discovered that small variations in how repertoire data had evolved over time became more visible once new tools entered the picture.

Nothing had been done incorrectly. The organization had simply grown organically, as most publishing organizations do.

Experiences like these suggest that AI adoption in publishing may be less about innovation and more about stewardship.

Publishing decisions often shape how works are managed and understood for decades.

That makes the first step less about experimentation alone, and more about building shared understanding:

• how work moves through an organization

• where data lives

• which decisions carry long-term consequences

In a field built on care for repertoire and artists, continuity is often the clearest form of progress.