Amidst growing environmental challenges and the imperative for sustainable solutions, this study explores how generative artificial intelligence (AI) can drive innovation in process engineering. It investigates the effectiveness of different prompting techniques and their integration with nature-inspired principles (NIP) in fostering sustainable innovation. The study employs a comparative methodology to assess the effectiveness of two distinct prompting techniques: basic and AI-automated prompting. It also examines the influence of integrating NIP derived from various natural ecosystems on the generated solutions. Experiments were conducted using a generative AI model and analysing the output, focusing on the number of unique and overlapping ideas. Furthermore, the quality of AI-generated solution concepts was evaluated using four parameters, such as feasibility, novelty, usefulness, and sustainability, each rated on a scale of 0 to 2. Three case studies within the process engineering domain were used, each representing a different problem-solving scenario. The results showed that the integration of NIP, particularly through the “one by one” strategy in AI-automated prompting, significantly increased the number of unique ideas compared to basic prompting, demonstrating its effectiveness in enhancing idea diversity and quality. Concepts generated through this approach also scored higher in novelty and sustainability, aligning with sustainable innovation goals. These findings have practical implications for developing innovative and sustainable engineering solutions, particularly in the early phases of design, offering insights into effective strategies for leveraging AI in eco-innovation.