# AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
## 5 Case Studies: Success Stories and Lessons Learned  
### Detailed Case Studies from Various Industries  
The successful integration of domain knowledge in AI/ML is vividly demonstrated across various industries, each showcasing innovative applications and transformative outcomes. These case studies highlight the profound impact of embedding specialized knowledge into AI systems, addressing complex challenges, and enhancing decision-making processes. Materials Science  
The study \"Embedding Domain Knowledge for Machine Learning of Complex Material Systems\" by [PERSON] (2019) [117] explores the integration of domain knowledge into ML applications within materials science. Materials systems often suffer from limited datasets, hindering the effectiveness of traditional ML algorithms that rely on large amounts of data. By embedding domain knowledge, researchers can significantly reduce data requirements for training ML models, making it feasible to apply these techniques to a broader range of materials. This approach not only enhances model performance but also improves interpretability, providing clearer explanations of how various factors influence material properties. The study reviews strategies for embedding domain knowledge, offering examples of their implementation in ML algorithms and discussing applications across different materials systems. Manufacturing Sector  
In the manufacturing sector, integrating domain knowledge into AI systems has proven essential for optimizing processes and achieving significant performance gains. The study by [PERSON] (2022) [109] presents an innovative approach to integrating human expert knowledge into neural network models to enhance decision-making in manufacturing processes. The interpretable neural network (INN) model incorporates domain knowledge as interpretable features and rules, crucial for constructing rule-based systems that support manufacturing decision-making, such as process modeling and quality inspection. By using a center-adjustable sigmoid activation function, the INN efficiently optimizes these rule-based systems, ensuring predictions are accurate and interpretable by human experts. This approach addresses the limitations of traditional ML models, which often lack transparency and are difficult for domain experts to understand. Companies like Ford have utilized AI-based predictive maintenance through digital twins, incorporating detailed engineering knowledge about machinery operations and failure modes. By embedding specialized information, AI models can more accurately predict equipment failures and optimize maintenance schedules, leading to reduced downtime and energy savings that generic AI models without such knowledge might miss [132]. Without integrating domain-specific insights, AI might not effectively interpret machine behaviors, resulting in less efficient maintenance strategies. Similarly, L'Oreal leverages AI for demand forecasting by integrating domain knowledge about consumer behavior and market dynamics. Utilizing diverse data sources like social media trends, point-of-sale data, and weather patterns, and understanding how these factors influence purchasing decisions, AI models can more precisely predict demand and optimize market strategies. An AI system without this domain context might fail to correlate diverse data points effectively, leading to inaccurate forecasts and suboptimal inventory management [132; 133]. The study by [PERSON] (2024) [118] discusses the transformative role of AI in construction management. It highlights how AI technologies, particularly predictive analytics and ML, are leveraged to address common challenges such as project complexity, delays, and communication issues. Through various case studies, the paper showcases successful implementations of AI, demonstrating significant benefits like increased project efficiency, cost savings, and improved safety. These examples illustrate how AI can optimize project planning, scheduling, and risk management, leading to more streamlined and effective construction processes.