The full potential of Industry 4.0’s deep learning models cannot be realized without explainable AI (XAI). Such models operate as black boxes, although they are known for their excellent predictive abilities, making it hard to build trust, ensure accountability, or align them with human values. This chapter looks at some XAI principles that can help address the challenges presented by these black-box models in industrial settings, including but not limited to potential biases, difficulties in debugging, production delays, and regulatory compliance problems. The various XAI techniques are discussed here, which range from model-agnostic methods like feature importance analysis, LIME, SHAP, etc., to model-specific methods such as saliency maps for CNNs and layer-wise decomposition for RNNs, showing that they can be used to demystify deep learning models across different industrial domains. It also considers the needs to be considered when implementing XAI successfully, like the trade-off between explanation and accuracy computation cost, among others, thus encouraging collaboration between AI engineers and subject matter experts who can provide valuable domain knowledge integration. This chapter helps reader to know the model to make trustworthy, transparent, and accountable AI systems that promote innovation while still upholding ethical values in Industry 4.0 and beyond by accepting XAI principles.