Enhancing the explainability of Artificial Intelligence and Machine Learning (AI/ML) models is crucial for ensuring transparency, building trust, and aligning with ethical standards. This chapter explores various techniques and guidelines to improve the explainability of traditional AI/ML and Generative AI (GenAI) models. Key aspects include defining clear objectives, selecting appropriate models, documenting preprocessing steps, and employing model-specific and model-agnostic explainability techniques. Visualization tools and interactive interfaces play a vital role in making complex models accessible to stakeholders. Evaluation and validation processes such as assessing fidelity and usability are essential for maintaining the effectiveness of explainability frameworks. Techniques such as simple local analysis, deep model analysis, surrogate model development, and contrastive analysis provide insights into model behavior and decision-making processes. By implementing these explainability practices, organizations can foster transparency, mitigate biases, and ensure the ethical use of AI/ML and GenAI models. Improved explainability enables stakeholders to understand, validate, and trust model predictions, thereby facilitating the adoption of AI-driven solutions. Ultimately, prioritizing explainability aligns innovation with societal values and regulatory requirements, and positions organizations as responsible adopters of AI technology.