From Business Problems to AI Solutions: Where Does Transformation Support Fail

2026-04-20

AI Summary

This study addresses the “Analytical Translation Problem” (ATP)—the lack of systematic support in translating business problems into technical machine learning solutions—by formally defining ATP for the first time. Through a structured narrative literature review, the authors classify and multidimensionally compare 18 existing methods from requirements engineering and ML project management. They develop a comprehensive analytical framework comprising four method families, six input–output categories, and seven transformation stages, revealing that most approaches offer little to no systematic guidance for deriving ML tasks and algorithms (only four provide limited support). Based on these insights, the paper proposes five novel recommendations: multi-solution exploration, task derivation guidance, constraint-to-algorithm filtering, probabilistic traceability, and data-triggered revision, thereby establishing a theoretical foundation and actionable pathways to bridge the gap between business needs and ML practice.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Analytics Translation Problem
• requirements engineering
• machine learning
• business-to-AI translation
• ML task specification

Innovation

• Analytics Translation Problem
• requirements engineering
• machine learning lifecycle
• structured narrative review
• transformation framework