Innovation Training Programme

Innovation Training Programme

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Sophia Zimmermann

Needs Analysis and Content

Before designing any content, it was crucial to understand who would participate, what they already knew, and what they wanted to learn. The needs analysis helped us align the training with real expectations instead of assumptions.

Set up the Needs Analysis
We defined clear goals for the survey: to find out about participants’ roles in the music industry, their level of AI knowledge, their current use of tools, the challenges they face, and their preferred workshop setup (format, duration, timing).
To keep the process simple, the survey took no more than three minutes, was fully anonymous, and used multiple-choice questions. We also added an optional comment box and offered a small incentive — free entry to our next networking event — to encourage participation.

Learning:
A short and focused survey increases participation. The lower the threshold, the more reliable the insights.

Communication

We shared the survey via the Hamburg Music newsletter, explaining in one short paragraph what it was about, why it mattered, and what participants would get out of it. This approach worked well, though limiting the outreach to one channel reduced reach. In the next round, we plan to use more diverse platforms and partner networks to reach additional target groups.

Learning:

We also learned that it makes a real difference to include a small reward or incentive. Among all participants, we raffled off free entry to one of our upcoming events — a simple idea that noticeably increased participation and attention.

Analyzation

The evaluation showed that most participants had basic to moderate AI knowledge, used AI rarely in daily work, but were interested in areas such as creativity, organization, and text tasks. This confirmed that our workshops should start from a more basic point and focus on practical applications and hands-on learning. Participants also preferred in-person, half-day formats with a mix of theory and practice – insights that shaped the curriculum directly.

Learning:
Keeping the survey short and positive lowered barriers and gave us useful insights, but promotion was too narrow. Even with 17 responses, the results were valuable: they showed us where the real needs were and helped us design a more practical, applied training.

Topic Areas

Based on the needs analysis, we identified practical topics that matched participants’ work reality. This led to workshops on data management, video, text, image, and storytelling, as well as a general crash course covering multiple aspects (legal, video, text, image, etc.).

A successful training starts with a clear understanding of who it’s for and what it should achieve. In our case, the goal was simple but ambitious: make innovation and AI approachable for everyone working in Hamburg’s music industry.

We deliberately kept the entry barriers low. No prior knowledge of AI was required — participants could join whether they were complete beginners or already experimenting with tools. This openness made the training accessible for a wide range of professionals, from labels and publishers to venues, managements, and freelancers — essentially the full spectrum of professionals represented by Hamburg Music

By defining the target group broadly, we ensured that the programme reflected the real diversity of Hamburg’s music scene. The focus was not on turning participants into AI specialists, but on helping them understand how technology can support creativity, workflows, and business development in practical ways.

Learning:
Clarity about the audience and goals determines everything that follows — from communication and course design to trainer selection. Keeping entry barriers low proved key to reaching the right people and creating an inclusive learning environment.

Find suitable Trainers and Format

Define Profile

Our ideal trainers combined expertise in AI and direct experience in the music industry. This was extremely hard to find – in the end, only two people met both criteria. We therefore broadened our approach. Many AI skills, such as prompting techniques or workflow automation, are transferable across sectors. Even trainers without a direct music background could add strong value when they understood how to adapt their examples to creative work.
At the same time, we made it a priority to improve female representation in our trainer pool. Expanding our search to new networks and communities helped us discover more diverse speakers, including those working at the intersection of AI, creativity, and digital culture.

Research Sources and Database
We brainstormed where to find relevant trainers. Our main channels included:

LinkedIn & Google, Universities & researchers, Festival & conference programs, Trade fairs, associations & networks,TED Talks, podcasts, start-ups in AI & music tech, as well as our own networks within the music industry

Learning:
A broad search strategy ensures diversity and brings new perspectives. Systematically updating a shared speaker database helps for future editions.

Workshop Format
We chose an in-person format with small groups of 10–15 participants and a duration of four hours per session. This setup created space for discussion, experimentation, and direct feedback.
Each workshop day was divided into two parts (morning and afternoon), and participants could register flexibly. The entire programme ran over one month, with one workshop day each week. This rhythm made it possible to attend several sessions without overloading schedules.
Although some were skeptical at first (expecting online formats), feedback showed that in-person crash courses worked far better, especially for hands-on practice

The Innovation Training bridged a major knowledge gap: how to make AI tangible for music professionals. Over six months, Hamburg Music built a hands-on training series focused on data, text, video, and image applications. Key decisions included lowering entry barriers, focusing on practical tools, and maintaining small, interactive groups. Partnering with experienced trainers and ensuring personal communication with participants led to a highly engaged community. “I think the AI series is great! Thank you!” — Participant “Always very informative and a great opportunity to exchange ideas and build knowledge.” — Participant “Thank you for organizing everything so well!” — Participant The training series raised awareness for AI in the music industry. Continuous feedback confirmed the high relevance of such accessible learning formats: 100 % of participants wanted the series to continue.

Innovation Training Programme

Sophia Zimmermann