The field of intelligent systems and data management is witnessing significant advancements, driven by the integration of large language models and traditional data management systems. Researchers are exploring innovative architectures and frameworks that combine the strengths of these two paradigms, enabling more efficient and automated decision-making processes. Notably, the integration of large reasoning models and large action models has the potential to transform service composition into a fully automated, user-friendly process. Additionally, the application of emerging technologies such as data spaces, peer-to-peer data management, and blockchains is being investigated to address the challenges of big data management in energy systems. Furthermore, the importance of interdisciplinary collaboration between artificial intelligence and operations research experts is being emphasized, with a focus on overcoming cultural differences and maximizing societal impact. Some noteworthy papers in this regard include: Initial Steps in Integrating Large Reasoning and Action Models for Service Composition, which proposes an integrated LRM-LAM architectural framework. Composable Effect Handling for Programming LLM-integrated Scripts, which introduces a novel approach to separate workflow logic from effectful operations, enabling modularity and performance optimization. The Incomplete Bridge: How AI Research (Mis)Engages with Psychology, which provides a comprehensive map of interdisciplinary engagement between AI and psychology, highlighting areas for deeper collaboration and advancement.