Managing Uncertainty in Innovation Process Through AI Integration

The integration of AI into the hospitality industry is altering operating paradigms and guest service standards, especially within the Food and Beverage (F&B) departments of premium hotels. This study presents a complete framework for AI-driven process and service innovation in the F&B departments of premium hotels located in the National Capital Region (NCR) of India. By exploring current adoption trends, technological enablers, and innovation outcomes, the research aims to identify how AI transforms core processes such as inventory management, kitchen automation, and supply chain coordination, while simultaneously enhancing customer-facing services through personalization, smart menus, and conversational interfaces. Grounded in the Innovation Diffusion Theory, the “Technology-Organization-Environment (TOE) Framework,” and Service-Dominant Logic, the study adopts a mixed-methods approach integrating quantitative surveys and qualitative interviews across chosen premium hotels. The resulting AI innovation framework not only highlights the important facilitators and results of AI adoption but also provides strategic insights for hotel managers, policymakers, and technology providers aiming to boost competitiveness and operational excellence in the hospitality sector. The paper finishes with practical implications, obstacles of implementation, and future research possibilities, notably in regard to workforce change and the scalability of AI solutions.

Trends in the environment are leading to the emergence of more and more mechanisms that bring us closer to the circular economy. However, there are a number of risks that delay various industries from fulfilling their goals. A key factor in overcoming them is the introduction of circular design. In this paper, circular design is used as a key tool for reducing risk in the circular economy. Here, the reader can get acquainted with a model for using circular design in intelligent risk management in the circular economy, called CDIRMCE. The model unites four interconnected stages that form a continuous cycle of data, analysis, action and feedback. The first stage is aimed at intelligently providing data that is interpreted through NLP and machine learning. The second is related to risk management in circularity, and it relies on predictive models and adaptive algorithms. The third stage transforms priority risks into specific circular design tasks and generates solutions through generative models, simulations and digital twins. The fourth stage provides continuous monitoring of key circularity indicators, automated reporting and self-learning of the system. In its entirety, CDIRMCE demonstrates how the integration of AI and circular design can systematically reduce uncertainty, improve product sustainability and support organizations in achieving circular economy goals. The model offers an applicable framework for both scientific research and practical management of innovation processes, combining technological intelligence, regulatory compliance and a strategic orientation towards sustainability

Role of AI in Innovation

The integration of generative AI (GenAI) into corporate innovation processes represents a significant shift in ideation methodologies. This study examines the comparative effectiveness of AI-generated ideation and traditional expert workshops. In collaboration with BSH Home Appliances Group (BSH), ideas were generated and evaluated using both expert-based and AI-based methods in the context of sustainable packaging. The main quantitative analysis focuses on the quality dimensions of novelty, value, and feasibility. The results indicate that GenAI models such as ChatGPT not only match, but also occasionally outperform, those generated by expert sessions in terms of generating highly novel ideas. However, this increased novelty comes with a trade-off in perceived feasibility, highlighting a critical balance that must be managed in innovation efforts. A complementary qualitative analysis provides insights into potential barriers to integrating AI into ideation at the personal and organizational levels. Depending on the innovation setting, AI-based idea stimulation may limit the creativity and motivation of experts. Therefore, the form of AI integration should be adapted to the company's innovation context and should contribute to organizational learning. On the basis of these findings, guidelines are provided on how to effectively apply, and benefit from the use of, AI as a non-human intermediary to enhance the ideation process.