From Business Problems to AI Solutions: Where Does Transformation Support Fail
For practitioners and researchers in ML project management and requirements engineering, the paper identifies a critical but unsupported step in the AI development lifecycle.
The paper reviews 18 approaches for translating business problems into ML solutions, finding that none provide systematic guidance for deriving ML task or algorithm specifications, a gap termed the Analytics Translation Problem (ATP).
Translating business problems into well-specified machine learning solutions is a prerequisite for successful AI systems, yet this upstream translation is still one of the least supported steps in existing methodologies. We conduct a structured narrative literature review of 18 approaches spanning requirements engineering (RE), machine learning (ML) project management, and automation. We organize these approaches into a taxonomy of four families and compare them across six input artifact categories, six output artifact categories, and a transformation framework of seven stages, grounded in RE refinement theory and ML lifecycle process. Our study shows that most approaches list ML task or algorithm specification among their expected outputs, yet only four provide partial guidance for deriving it, and none provides systematic guidance. We characterize this gap as the Analytics Translation Problem (ATP) and derive five research recommendations addressing multi-formulation exploration, task derivation guidance, constraint-algorithm filtering, probabilistic traceability, and data-triggered revision.