Understand what you want to achieve with your AI application. Define the problem you're solving, the tasks the AI will perform, and how success will be measured.

AI models depend on data. Depending on your goal, you may need to collect or generate appropriate data. Then, clean and pre-process your data to ensure it's suitable for training an AI model.

Select the tools and frameworks you'll use for developing your AI application. There are numerous open-source AI libraries and frameworks available, such as TensorFlow, PyTorch, Keras, and Scikit-learn.

Using your prepared data, develop your AI model. This usually involves choosing an appropriate algorithm (or multiple algorithms), training your model on your data, and then testing it to assess its performance.

Based on your model's performance, you may need to refine it. This could involve tuning hyperparameters, adding more data, choosing a different algorithm, or even redefining your problem.

Once your model is performing well, implement it in your application. This will require software development skills to create an interface for your model, integrate it with other components of your application, and ensure it performs correctly in real-time.

Thoroughly test your application to ensure it works as expected and the AI component is performing well in a real-world environment.

Deploy your application and monitor its performance over time. Depending on how your application is used, you may need to update your AI model periodically to maintain its performance.

AI application development is a continuous process. Based on feedback and performance metrics, continue refining and improving your application over time.