The correct order of steps in an AI project cycle begins with Data Acquisition, where relevant data is gathered. Next, Data Exploration takes place to understand the dataset and its properties. After that, Problem Scoping is done to define the problem and determine how the data can be used to solve it. Then, Modelling is performed, where machine learning models are created. Finally, Evaluation is done to assess the performance of the models. This sequence ensures a structured approach to building and deploying AI models.