To create a common basis for the later prioritization of action areas, MTU employees from various functional areas and hierarchy levels participated in the systematic appliedAI Maturity Assessment. From the maturity assessment, a data-driven status quo overview was derived, and development potentials were identified. In addition, structured expert interviews were conducted to validate the results.
At the same time, appliedAI and MTU conducted a competition analysis related to AI, as this strongly influences the company-specific AI ambition: an understanding of AI-based competitive advantages, impact on products, processes, and business models, as well as general industry dynamics, is essential for this.
As a result, players in the aerospace industry are massively increasing the use of AI in upstream, equal and downstream stages of the value chain and are applying the technology to varying degrees of intensity in development, manufacturing and maintenance processes. In addition, AI is already established in adjacent high-tech industries to accelerate development simulations, improve component quality control, and increase gas turbine control efficiency.
In order to formulate a coherent target picture for MTU, this market analysis, MTU-internal expert opinions and an appliedAI AI technology potential assessment were consolidated in the AI ambition.
Building on a timeframe of three to four years and clearly defined strategic goals for scaling AI, MTU and appliedAI defined three synergistic application areas that form the focus for identifying AI use cases:
1. product development (virtual engine and test support): Acceleration of development times and utilization of development resources with highest possible efficiency.
2. production (process simulation and automation as well as work preparation, production control and employee support): Increased production efficiency and improved accuracy in predicting the influence of individual production process parameters on product quality
3. maintenance (digital inspection and advanced forecasting): Increased customer proximity and better synchronization of internal planning processes.
Building on the focal points of the AI ambition, MTU and appliedAI conducted three use case workshops. Use cases were first systematically developed and discussed for each business unit. They were then evaluated along the dimensions of value contribution and feasibility, and finally thematically clustered together with department representatives and steering committee members.
Within the individual clusters, particularly promising use cases were finally identified that could serve as potential lighthouse applications. Their underlying hypotheses will be thoroughly tested before Proof of Concepts (PoCs) are developed.
In order to sustainably embed AI in the organization and fully exploit the technology's value potential, MTU and appliedAI have identified a number of key factors in parallel:
- In terms of organizational and governance structures, a hybrid hub-and-spoke approach (with a central AI unit) was identified as particularly promising.
- Extensive role profiles, employee training, and new recruiting strategies were developed to build the required expertise
Together, initial initiatives were prepared to initiate the accompanying cultural change in the company.
In terms of
- The foundations for establishing a suitable AI infrastructure and data governance were defined in workshop formats.