Today, organizations are increasingly deploying artificial intelligence (AI) systems in highly sensitive and critical domains, such as medical diagnosis, autonomous driving, and cybersecurity. Reliance on AI models to perform vital tasks has opened up the possibility of large-scale failure with damaging consequences. AI incidents are growing significantly, averaging 79 incidents yearly from 2020 to 2023. These disasters question the efficacy of AI systems and call for developing robust models resistant to vulnerabilities. Model robustness is a machine-learning model's ability to withstand uncertainties and perform accurately in different contexts. Ensuring model robustness is necessary as we increase our reliance on AI models to perform critical jobs, reducing sensitivity to outliers, protecting models against malicious attacks, fairness, increasing trust, reducing cost of retraining models, and improving regulatory compliance. Building robust models involves several techniques, including data quality, automated data pipelines, data augmentation, adversarial training, domain adaptation, regularization, and explainability. Challenges of model robustness include increased model complexity, feature volatility, evaluation methods, and achieving model robustness with specialized platforms like Encord Active that provide automated evaluation features, development tools, active learning pipelines, and comprehensive explainability reports. Achieving model robustness is essential for leveraging AI's full potential to boost profitability, ensuring a robust model can maneuver uncertain real-world scenarios appropriately, improving data quality, domain adaptation techniques, and regularization's reduction of generalization error, and providing model explainability.