Horizon Europe Series | Blog 2 of 4
One of the things we notice at Choirfarm is how many different kinds of working life sit inside the choir economy. There are paid music directors and volunteer section leaders. There are professional accompanists and amateur organisers who do the whole job on evenings and weekends. There are committee members handling finances, venue bookings and audience communications alongside people who simply come to sing. And that is just the choir. Behind it sits a whole ecosystem of venues, teachers, event managers, sound technicians, publishers and digital platforms.
When we zoom out to performing arts as a whole, that ecosystem becomes considerably more complex — and more interesting. Because every single role in it is going to be affected by AI in some way over the next decade. And right now, almost none of them have access to tools designed with their actual working life in mind.
This blog maps the key stakeholders across the performing arts landscape, looks honestly at the pressures each of them faces, and makes the case for what human-centred AI — AI designed for the realities of live cultural work — could do for each of them. This is not a speculative exercise. It is the foundation for understanding what a research and innovation project in this space needs to address.
The performing arts workforce is not one thing
Before mapping the stakeholders, it helps to be clear about what makes performing arts distinctive as a work environment.
Most sectors have a relatively clear separation between management and delivery. In performing arts, those layers are often collapsed into the same person, or distributed across a porous mix of paid staff, freelancers, part-time employees and volunteers. A community choir director might simultaneously be the artistic lead, the rehearsal manager, the communications officer and the primary point of contact for venue hire — and do all of that unpaid, after a full working day elsewhere.
This is not unusual. It is structurally characteristic of a sector where, across the EU, 32 percent of workers are self-employed and many organisations operate with thin or non-existent permanent staffing. That is the baseline reality into which any AI tool needs to fit. Not a well-resourced team with time to implement new systems. Often, a single person with fifteen minutes between rehearsal and the school run.
With that in mind, here is how the key stakeholder groups map out — and what better tools could actually offer each of them.
Performers and ensemble members
The people on stage or in the rehearsal room are not, in most cases, primarily concerned with administration. But they are affected by it. Poor scheduling wastes rehearsal time. Unclear communication creates friction in the ensemble. Inadequate planning for performances puts unnecessary pressure on the hours that should be about the music or the performance itself.
For professional and semi-professional performers, AI tools that streamline the logistics around their work — scheduling, communication, booking management, contract administration — could meaningfully reduce the non-creative overhead that currently competes with creative work. For amateur performers, particularly those in community groups, better coordination tools could lower the barrier to participation and reduce dropout caused by organisational friction rather than lack of interest or commitment.
There is also an educational dimension here. Performers at all levels are beginning to encounter AI-generated content — music, scripts, backing tracks — and have very little framework for thinking about what it means for their practice, their craft and their income. Tools that help performers understand, critically engage with and make informed decisions about AI in their creative field are not just useful. They are becoming necessary.
Choir directors and music directors
This is a role that Choirfarm knows particularly well, and it sits right at the intersection of artistic leadership and operational management.
A choir director is responsible for the musical quality of the ensemble, for the cohesion of the group, and for the long-term arc of repertoire and performance. They are also, in most community settings, the primary relationship-holder for every other stakeholder in the choir's economy: the venue, the accompanist, the committee, the members, the concert promoters, the schools they may work with.
The administrative burden is substantial and grows with the size and ambition of the choir. Scheduling rehearsals around a choir of 80 members across a 12-month performance calendar, managing the communications around multiple concerts, tracking music library loans, coordinating with venues — none of this is what a music director trained to do. But all of it falls to them.
Human-centred AI in this context would look like smart scheduling tools that can handle the complexity of ensemble coordination without requiring the director to become a software expert. It would look like communication drafting tools that understand the relationships and language of choral life. It would look like repertoire support that helps a director find music suited to their ensemble's level, voice configuration and programming needs. Practical, domain-specific, trustworthy tools — not generic productivity software that adds translation overhead.
Amateur arts organisers and committee members
This is probably the least-discussed and most important stakeholder group in the whole performing arts ecosystem.
Across Europe, an enormous proportion of performing arts activity — community choirs, amateur dramatic societies, folk clubs, local festivals, youth orchestras — is sustained almost entirely by volunteers. People who give up significant personal time to handle bookings, finances, communications, grant applications, ticket sales and audience relations for organisations that have no paid administrative staff.
These people are not performing arts professionals. They are a retired accountant, a part-time teacher, a marketing manager who joined the choir and ended up on the committee. They bring real skills. They also face a steep learning curve every time a new tool, a new process or a new requirement emerges.
For this group, the most valuable AI tools are the ones that require the least prior knowledge to use well. Drafting a funding application. Producing an annual report for an arts council. Writing a press release for a concert. Generating a budget template. Responding to a venue's insurance requirements. These are tasks that take skilled organisers hours and take generalist AI tools minutes — but only if the interface is genuinely accessible and the outputs are actually appropriate for arts organisations, not corporate ones.
This is a group that would benefit enormously from AI assistance and is currently almost entirely underserved by the tools that exist.
Arts managers and producing organisations
Professional arts managers — working in theatres, producing companies, arts centres, orchestras and larger ensembles — occupy a different position in the landscape. They have more resources, more capacity and more existing digital infrastructure than community organisations. They also operate in a more commercially and institutionally pressured environment.
For this group, AI tools have both immediate operational value and longer-term strategic significance. Operationally: production scheduling, budget modelling, audience data analysis, grant reporting, touring logistics. Strategically: understanding how AI is changing the economics of arts production, how audiences are being shaped by algorithmic recommendation, and how the skills profile of the arts management workforce is shifting.
There is also a workforce planning dimension that professional arts organisations are only beginning to engage with. The roles of producer, general manager, marketing director and development officer are all evolving as AI becomes part of the professional toolkit. Organisations that think ahead about this — that develop their staff's AI literacy and redesign workflows thoughtfully — will be in a significantly stronger position than those that wait for the change to happen to them.
Arts educators and music teachers
The performing arts education landscape — conservatoires, music schools, drama colleges, university arts programmes, community music education — has a particularly complex relationship with AI.
On one hand, AI tools offer genuine pedagogical value: adaptive learning support, assessment assistance, repertoire and materials discovery, accessibility tools for students with additional needs. On the other hand, the question of what AI means for the future careers of arts graduates is urgent and unresolved. A conservatoire student training now will graduate into a professional landscape shaped by AI in ways we cannot fully predict. Their educators have a responsibility to prepare them for that — but very few have the frameworks, the time or the institutional support to do so.
Human-centred AI in arts education looks like tools that augment the teacher-student relationship rather than replacing it. It looks like research into what AI literacy means specifically for a performing arts graduate — different from what it means for an engineering or business graduate, because the creative, interpretive and communal dimensions of arts work create different questions and different risks. And it looks like curriculum frameworks that help arts schools navigate this terrain without abandoning the human-centred core of what they do.
Venue operators and technical staff
Venues are the physical infrastructure of performing arts. Concert halls, theatres, arts centres, churches used for concerts, village halls used for rehearsals, festivals sites, outdoor stages — all of them involve their own operational complexity, and many of them serve multiple communities with different needs across the week.
For venue operators and technical staff, AI tools have clear applications in scheduling, maintenance planning, audience communications, energy management and accessibility provision. The more interesting question is what happens at the intersection of venue and community: how a venue can use data and AI tools to understand and deepen its relationship with the performing arts organisations it hosts, rather than treating them simply as hirers.
There is also a resilience dimension. Many smaller performance venues — particularly those serving community performing arts — are under sustained financial pressure. Tools that help them operate more efficiently, make the case for their value to funders and local authorities, and engage new audiences could be the difference between survival and closure.
Freelancers: musicians, technicians, stage managers and creative practitioners
Freelancers form a critical and often invisible part of the performing arts ecosystem. Professional musicians who play for community choirs, opera companies and recording sessions in the same week. Sound and lighting technicians who work across festivals, touring theatre and corporate events. Stage managers who move between productions and organisations with considerable expertise but very little institutional support.
This group operates at the sharpest end of the sector's precari. High self-employment rates, irregular income, no employer-provided tools or training, and a need to market, contract, invoice and account for themselves on top of their actual craft work.
AI tools for freelancers in performing arts look like smart contract and invoice management suited to multiple simultaneous clients. Scheduling and availability tools designed for the complexity of portfolio working. Marketing and communications support. Financial planning tools that understand the irregular income patterns of arts freelancing. And, crucially, tools that are affordable and accessible on a freelance budget — not enterprise software with enterprise price tags.
Community participants and audiences
The people who attend performances, take part in community singing, join open rehearsals and participate in arts workshops are not passive consumers. They are part of the ecosystem. Their participation creates the social value that justifies public investment in performing arts. Their data, aggregated and anonymised, is what tells the story of what this sector actually contributes to wellbeing, community cohesion and civic life.
AI has a role to play in understanding and deepening that participation: better audience data tools that go beyond ticket sales to capture genuine engagement; accessibility technologies that make performances and workshops available to people currently excluded; personalised communication that helps community organisations find the people who would most benefit from what they offer.
And there is a measurement dimension here that matters enormously. The social value that community performing arts generates — for health and wellbeing, for loneliness and social isolation, for confidence and education, for local economic activity — is real and substantial. But it is currently very poorly documented. Which creates a problem, because funders increasingly require evidence, and arts organisations that cannot produce it lose out to those that can.
We return to this in detail in Blog 3, where it connects to the specific challenges that festivals face. But it is worth flagging here as a whole-ecosystem issue: performing arts needs better tools for capturing and communicating its social value, and AI has a genuine role to play in making that possible.
Festival organisers and their teams
Festivals deserve their own section in this blog, and their own blog in this series. But within a stakeholder map, it is important to note that festival organisers occupy a uniquely pressured position in the performing arts landscape.
A festival director typically manages a compressed burst of intense multi-artform activity, coordinating performers, venues, volunteers, sponsors, local authorities, audiences and media across a period of days or weeks. They do this with a tiny year-round team — sometimes a team of one or two — scaling up dramatically through the festival period using freelance staff and volunteers who need rapid onboarding and clear coordination.
The operational challenges are significant. The administrative burden before, during and after the festival is enormous. And the pressure to demonstrate impact — to funders, to local authorities, to sponsors — grows every year. AI tools designed for this environment would need to understand its rhythms: the long quiet planning phase, the frantic activation phase, and the post-festival period when evaluation and reporting compete with the need to begin next year's planning immediately.
Blog 3 explores this in full.
What this map tells us
Mapping these stakeholders makes something clear that is easy to miss when performing arts is treated as a single homogeneous sector: the AI needs are genuinely different for each group, and the capacity to adopt new tools varies enormously.
A professional producing organisation has resources, infrastructure and the appetite to trial new tools. A community choir committee member has fifteen minutes on a Tuesday evening. A conservatoire can build AI literacy into its curriculum with institutional support. A freelance musician needs something that works on a phone, costs very little, and does not require onboarding.
Any AI tool designed for "the performing arts sector" that does not account for this range will end up serving the organisations that least need help and missing the ones that need it most.
That is the design challenge at the heart of the Horizon Europe project we are building — and it is exactly the kind of challenge that requires genuine co-creation with the sector, not just technology development at a distance.
If any of these stakeholder groups includes you, or people you work with, we would like to hear from you. The next blog in this series turns to festivals specifically — why they represent one of the most valuable R&D environments for this work, and why they face some of the most urgent challenges.
research at choirfarm dot com
Blog 2 of 4 in the Choirfarm Horizon Europe Series on AI and Performing Arts. Read Blog 1: Why performing arts needs its own future-of-work AI tools.
