This research based on the case study method, shares the strategic implementation of AI for InnoEnergy staff involved in use cases related to both university related activities as well as industry enabled projects. The data of this study was gathered through a holistic analysis of technological integration of AI, capturing the organizational dynamics. The analytical dimensions are: the technological perspective, the organizational culture, the workflow transformation, and the individual professional development. The study looks at the gradual roll out of AI throughout the workplace taking into account AI use cases, workflow automation with AI, and using master students as interns to improve the overall understanding and use of AI.

AI is seen as a prolongation of InnoEnergy’s Masters school drive for innovation. InnoEnergy is an ecosystem consisting of high ranked technical and business universities within Europe, and companies with an interest in sustainable or renewable energy. Within this ecosystem we have 6 Master programs, so called Masters+ programs, with are focused on specializations in the sustainable energy sector. Our master students are taught green engineering skills, and business skills to enable them to turn their engineering prototypes into successful innovations or businesses often with an integrated AI element. This type of ecosystem puts all of the InnoEnergy staff in a professional environment that is highly driven by innovation, and quick changes, including AI savviness as a means to stay on top of the opportunities AI provides within the realm of sustainable energy engineering.

As the opportunities for using AI kept increasing, it was decided to build use cases that would familiarize staff with AI and enable more InnoEnergy staff to understand what the benefits could be. One of the key strategies was to allow individuals to come forward as potential ambassadors of AI. This allowed those members of staff who were favorable towards new technologies to explore and test out specific AI usage that might fit their own professional needs. Within the first few months of rolling out the option to explore AI tool for professional needs, five use cases were started. All these use cases increased the AI competencies of the staff involved. By using a strategy build upon creating AI ambassadors who are pioneering the AI use within the organization, we hoped to create a ripple effect across the different departments of InnoEnergy. By allowing the idea and implementation of AI to become part of every day conversations, we wanted to create an environment where the uptake of technology is a collaborative effort, taking into account individual stances towards technologies that can vary form early adoption enthusiasm to critical analysis before taking up new technologies.

After a year of exploring, building, and increasing the uptake of AI by the InnoEnergy AI ambassadors, it resulted in a rich texture of experiences that resounded throughout the organization. As the experiences with these new AI tools and the overall AI technologies grew, a holistic analysis of the technological integration of AI could be investigated. This paper will share the analytical dimensions of the analysis, dig deeper in some of the discussions that arise while using AI in an educational organization aimed at speeding up the transition towards clean energy, and offer conclusions of the ripple effect approach used in this case study.

Keywords: AI, master student interns, workplace learning, AI literacy, digital literacy, collaboration.