To achieve their goals and overcome challenges, appliedAI and EnBW developed a targeted approach to implementing AI in the organization.
First, EnBW conducted multiple AI workshops across various areas of the organization. These were based on the methodology of appliedAI to create a cohesive AI vision that applies to the entire organization and provides a comprehensive perspective. During the workshops, EnBW also assessed the status of their AI-enabling factors.
At the same time, appliedAI offered an engineering training with EnBW to train their software engineers in dealing with artificial intelligence. The training was aimed at 20 software engineers and included a week of on-site workshops. These workshops introduced the latest tools, frameworks, and approaches to use machine learning for real business problems. During these workshops, participants gained practical experience in Colab and practiced with real datasets. In in-depth discussions between EnBW's software engineers and appliedAI's machine learning engineers, questions were answered. The EnBW team was thus equipped with the right tools to apply AI independently and to help internal teams to define their AI use cases.
AI multipliers from various business units were then nominated and trained specifically on AI and how to identify use cases.
The designated multipliers participated in a two-day intensive training at appliedAI in Munich. This included a general introduction to machine learning and its requirements. In addition, train-the-trainer formats were used to teach methods for identifying AI use cases and conducting corresponding workshops.
Subsequently, the AI-multipliers were given access to an internal community in order to continuously build and expand it. In this way, the multipliers help to pass on AI knowledge to the teams and support as well as to develop a central view of possible use cases.
As a result, EnBW can now provide all relevant business units with appointed AI experts who bring their expertise to the teams and implement AI.