AI modeling can present a variety of challenges, ranging from data related to how AI components interact with each other, to training AI models on a viable dataset. Model design can sometimes be difficult and often times require extensive experimentation to find the best AI algorithms for the particular task at hand. AI data models sometimes lack interpretability, making it hard for stakeholders to gain insights into why AI is making certain decisions. There is also a need for ethical AI modelling, which requires artificial intelligence data scientists and engineers to carefully consider any potential implications of AI projects, such as societal or legal biases embedded in AI models. Overall, although there are many issues associated with AI modelling today, by continuously evaluating and improving algorithms, technical solutions and governance processes we can work towards overcoming these problems and develop trustworthy Artificial Intelligence systems.