In an era where Artificial Intelligence (AI) is rapidly advancing, AI planning emerges as a powerful tool for solving real-world problems across various domains. However, engineering AI planning based systems for industrial deployment presents significant challenges. This thesis embarks on identifying and analyzing the common engineering challenges faced when engineering planning based systems in industrial settings. An exploratory study is conducted to address the engineering challenges in AI planning systems. The first stage involves developing a conceptual model from the information grounded in existing literature. This model, comprising constructs, propositions, and explanations, forms the foundation for hypothesis testing. It guides the research, providing a structured approach to understand the complexities in the field. The second stage of the study employs a survey targeting industry professionals. This approach captures a wide range of experiences and perspectives on the challenges faced in AI planning. Key areas of focus include documentation, support, complexity and performance, tool interoperability, integration, standardization, and reusability. Finally, it reveals critical insights about the challenges faced in engineering AI planning applications. These insights are tested against the conceptual model for its validity. The insights also emphasizes the need for effective complexity management, and robust standardization practices. The thesis concludes that addressing these challenges is vital for the efficient and reliable functioning of AI planning systems.