Are you solving the right AI problem?
Solving the right problem is as important as solving the problem right
Every problem is not suitable for AI! Problem framing is important to ensure success in designing and deploying an AI product. In developing AI products, the following is my checklist to ensure we are solving the “right” problem.
Verified there is quantifiable business value in solving the problem
To deliver a well-tuned robust ML model deployed in production can range from 6–24 months depending on the complexity. Being crystal clear about the strategic value of the project is critical.
Verified that simpler alternatives (such as hand-crafted heuristics) are not sufficient to address the problem
Simpler alternatives are often overlooked. Also, these heuristic-based approaches should be used as a baseline for accuracy to compare the effectiveness of the AI solution
Ensure that the problem has been decomposed into the smallest possible units
Instead of thinking of the business problem as one single model, instead, the goal should be to think of the overall solution that should be decomposed as a sequence of models and methods.
