This paper examines when the deployment of machine learning (ML) in a business process is economically justified and when alternative automation approaches may be more appropriate. Although ML adoption is widely discussed in technical and implementation-oriented literature, less attention has been paid to compact decision models for assessing ML suitability in specific business-process contexts. To address this gap, the paper proposes a multi-criteria assessment framework that combines fifteen literature-derived decision criteria with the Analytic Hierarchy Process (AHP). The criteria were weighted by three domain experts and applied to three real-world business processes: tutoring settlement, matching bank-statement transactions to customers, and conducting classes for students. The results show that the framework differentiates effectively among processes with different levels of ML suitability. The transaction-matching process achieved the highest score, the tutoring-settlement process was conditionally justified, and the process of conducting classes for students was not justified for ML deployment. These findings indicate the practical usefulness of a systematic, criteria-driven approach to ML deployment decisions.