1.Problem scoping:
Problem Scoping is the first and arguably the most important step in any AI project. This stage is about fully understanding the problem you want the AI to solve. It involves defining the goal, understanding why the problem exists, who will benefit from solving it, and whether AI is an appropriate solution. Problem scoping also considers the feasibility of the project, including available resources, data, and tools. Without proper problem scoping, even a technically perfect AI model may fail to solve the right problem or deliver useful results
For instance, a school may want to predict which students are at risk of failing exams. Here, the problem is clearly defined (identifying at-risk students), the beneficiaries are teachers and students, and it is feasible because the school has historical data on attendance, homework, and test scores. Proper scoping ensures that all subsequent steps are focused on solving the intended problem effectively.