Steps to build AI software involve several key steps, starting with defining the problem and gathering relevant data. The process includes data preprocessing, where raw data is cleaned and prepared for analysis, followed by selecting and training suitable machine learning algorithms. Developers then validate and test the AI model to ensure accuracy and reliability. The final steps involve deploying the software into real-world environments and continuously monitoring its performance for improvements. Each phase requires collaboration between data scientists, software engineers, and domain experts to create effective and impactful AI solutions.