We are also planning to explore machine learning and AI. Where should we start?

Identify business problems: Once you have a basic understanding of the technology, you can start to identify specific business problems that can be addressed through machine learning and AI. This may include automating repetitive tasks, predicting customer behavior, detecting fraud, or optimizing business processes. Gather data: Machine learning and AI rely heavily on data, so it's important to have high-quality data that is relevant to the problem you are trying to solve. This may involve extracting data from existing IT systems, collecting data from sensors or other devices, or partnering with third-party data providers. Build models: With data in hand, you can start building machine learning models using software tools such as Python, R, TensorFlow, or PyTorch. These tools allow you to create models that can learn from the data and make predictions or recommendations based on that learning. Evaluate and refine: As you build and test your models, it's important to evaluate their performance and refine them as needed. This may involve tweaking the algorithms or adjusting the data inputs to improve accuracy, precision, or recall. Exploring machine learning and AI requires a combination of technical skills, business acumen, and a willingness to experiment and learn from your successes and failures. With a structured approach and a commitment to continuous improvement, you can leverage the power of machine learning and AI to drive innovation and growth in your organization.