23rd FVI Expert Breakfast

Announcement

At the 23rd Forum Vision Instandhaltung e.V. Expert Breakfast on Friday, June 6, 2025, we take a clear look at a topic that moves many – but is often tackled in an uncoordinated way:

👉 Artificial Intelligence in Maintenance & Service

The foundation is the new AI Guide developed at DHBW Mannheim’s Service Engineering program. 📘

It offers, for the first time, structured guidance for introducing AI – including a maturity model, checklists, and typical pitfalls.

Our guests: Prof. Dr. Lennart Brumby – DHBW Mannheim
Kai Körber – Airbus Defence and Space
Baris Aktas – Schaeffler

📅 When: Friday, June 6, 2025
🕤 Time: 09:30–10:30
🌐 Where: Online – live & interactive

Moderation: 🎙 Marcel Hahn
🎙 Jens Reißenweber

What to expect: ✅ AI maturity: Where does your company really stand?
✅ Starting with a plan – instead of vague ideas and actionism
✅ Fresh perspectives – from science, industry, and education

Summary

• AI Guide:
  Kai and Baris presented their AI Guide, which aims to help companies implement AI projects efficiently and sustainably. The guide includes various chapters covering everything from goal definition to continuous improvement.

  • Guide structure: The AI Guide consists of several chapters, from goal definition to data preparation, model selection, and continuous improvement. Each chapter contains guiding questions and stop-or-go decisions to track project progress.
  • Stakeholder integration: The guide emphasizes the importance of involving stakeholders in the early phases of the project. A visual shows which stakeholders are relevant in which chapter.
  • Data quality: A key focus of the guide is ensuring a consistent data foundation and high data quality. Without quality data, AI projects cannot succeed.
  • Practical orientation: The guide is practice-oriented and based on extensive literature research, use-case analyses, and expert interviews. It includes many practical tools and measures to support AI implementation.

• Stakeholder integration:
  Kai and Baris emphasized the importance of early stakeholder involvement in AI projects. They explained that stakeholders are addressed in the early chapters to ensure all requirements and goals are clearly defined.

• Data quality and AI projects:
  Kai and Baris explained that data quality and access are critical for AI project success. Without high-quality data and proper access, projects are likely to fail.

  • Data access: Kai and Baris stressed that data access is crucial. Without appropriate permissions and role definitions, a project cannot proceed.
  • Data quality: The quality of the data is another key factor. They explained that without high-quality data, AI models cannot be properly trained, leading to project failure.
  • Stop-or-go decisions: The guide includes stop-or-go checkpoints to ensure that data quality and access are validated before continuing. This helps save resources and improve project efficiency.

• Feedback and continuous improvement:
  Kai and Baris highlighted the importance of feedback and continuous improvement in AI projects. They recommended implementing a feedback loop to keep improving the models and enhance usability.

• Sustainability and AI:
  Lennart and Robin discussed the role of sustainability in AI projects. They noted that interest in sustainability topics is currently low, even though sustainable solutions are often cost-effective.

• Experiences with AI projects:
  Tina and Denes shared their experiences with AI initiatives. Tina emphasized the importance of involving employees, while Denes pointed out challenges in data quality and convincing IT staff.

  • Employee involvement: Tina stressed that involving employees in AI projects is critical. She noted that employees often offer the best insights for solving problems and that their engagement is key to success.
  • Challenges in data quality: Denes highlighted how difficult it is for many companies to gather and use the right data, which often leads to project failure.
  • Convincing IT staff: Denes explained that it is often hard to convince IT personnel of the necessity and value of AI projects. This is another common reason for project failure.