Tejaswini Bollikonda

Artificial Intelligence (AI) is revolutionizing industries through automation, data-driven insights, and enhanced decision-making. As AI adoption expands, the need for scalable, interpretable, and ethical models grows. This paper explores key AI advancements, including federated learning for privacy-preserving model training, generative AI for content creation, and explainable AI (XAI) for improving transparency. We examine modern AI architectures such as transformers, neural retrieval systems, and enterprise AI solutions that optimize intelligent querying and automation. Additionally, we discuss XAI methodologies like SHAP and LIME for model interpretability and ethical AI governance frameworks for responsible deployment. This study provides insights into emerging AI trends, challenges, and future directions for researchers, practitioners, and policymakers.