Artificial Intelligence (AI) has historically relied on two key elements: data and algorithms. However, the traditional Model-Centric AI paradigm has typically emphasized algorithms, often handling data as static entities. Data are typically gathered, pre-processed, and kept unchanged, with significant efforts focused on refining learned models. This conventional approach has led to the development of increasingly complex and opaque decision models, requiring substantial effort in data training. On the other hand, the emerging Data-Centric AI (DCAI) paradigm focuses on the systematic and algorithmic generation of optimal data to fuel Machine Learning (ML) and Deep Learning (DL) techniques. The primary aim of the DCAI paradigm is to improve data quality, thereby achieving model accuracy deemed unattainable levels through model-centric techniques alone. In this talk we will discuss the transformative effects of recent advancements in the DCAI paradigm on the future use of AI, ML and DL in data science. The objective is to inspire further innovations in DCAI research, ultimately influencing the future landscape of in data science applications.