Innovation management is crucial for the competitiveness and success of companies across various sectors. Incorporating sustainability into innovation is increasingly essential as stakeholders demand more sustainable solutions. This integration requires a comprehensive approach, considering the complexities of sustainability and the innovation process. Artificial intelligence (AI) offers promising tools to enhance sustainability in innovation management, improving efficiency, and facilitating decision-making. However, AI also presents challenges such as ethical, environmental, and legal implications, necessitating responsible and transparent use. The energy industry exemplifies the intersection of innovation, sustainability, and AI. Driven by Germany's energy transition initiative (“Energiewende”), which began in 2011, the industry is transitioning from traditional to renewable energy sources like wind, solar, and biomass. This transition requires utility companies to adopt innovative approaches, moving from centralized to decentralized energy generation. This study explores AI applications within one of Germany's top five utility companies, with a significant share of renewable energy. The company’s activities span various sectors including energy networks, telecommunications, IT, mobility, and hydrogen, presenting multiple AI application fields. The research investigates how AI can drive innovation, and the capabilities required for leveraging AI in innovation. The methodology includes a literature review and a case study, involving semi-structured interviews with experts from different departments of a utility company. Eight interviews were conducted and analyzed using qualitative content analysis. The results show that AI can play a significant role in fostering innovation throughout the innovation process, if it is combined with the application of specialized AI skills, as well as domain-specific knowledge and process understanding. Effective use of AI could drive not only process and product innovations but also the transformation of entire business models. Practical recommendations include providing employees with hands-on experimentation opportunities, implementing AI toolkits to reduce concerns, and launching pilot projects to identify use cases. Furthermore, organizations must build digital competencies and maintain a realistic understanding of AI's capabilities to leverage its long-term benefits effectively. To address limitations and test the generalizability of findings, future research could involve quantitative studies across a larger sample of energy utility companies and expand to other industries for a broader understanding of AI's role in supporting innovation processes.