In 2023, businesses shifted their focus from expanding tech stacks to maximizing efficiency and cost-effectiveness, especially with the rise of AI, which presents new risks and opportunities. The importance of retrieval-augmented generation (RAG) is highlighted as a way to reduce AI model hallucinations by using real-time, contextual data, which will become essential for businesses seeking to power generative experiences with AI. The shift from a model-centric to a data-centric AI approach is anticipated, emphasizing the need for high-quality, real-time data to improve AI model outputs and decision-making processes. Multimodal large language models (LLMs) and databases are expected to drive AI applications across various industries by enabling efficient querying across diverse data types, especially in sectors like healthcare, robotics, and e-commerce. The integration of AI with edge computing will enhance real-time analytics and decision-making while maintaining data privacy. As businesses globally unlock new AI-driven opportunities, they are encouraged to embrace data-centric strategies, leverage augmented data management, and utilize AI copilots for faster insights and better decision-making.