Organizations developing AI/ML-based systems need to build and institutionalize a set of development capabilities to manage data, models, and deployment processes effectively. Maturity models provide structured approaches to assess and guide this capability development; however, existing models vary in how well they capture the distinctive characteristics of AI/ML-based development. Despite the growing number of such models, there is limited understanding of how they define maturity levels, structure capability dimensions, and align with AI lifecycle processes. To address this gap, we conducted a Multivocal Literature Review (MLR) to systematically identify both academic and practitioner-oriented maturity models relevant to AI/ML development. Ten models were evaluated against seven criteria: purpose and fitness for use, development method, depth of maturity level definition, coverage of capability dimensions, internal consistency, alignment with ISO/IEC 5338:2023 as the process reference model for AI systems, and validation evidence. The results reveal a consistent pattern of partial specification across the field. Models grounded in established process assessment frameworks performed more strongly across multiple criteria, while practitioner-oriented models consistently functioned as adoption roadmaps rather than rigorous assessment instruments. Most models show only implicit alignment with ISO/IEC 5338:2023, and only one addresses trustworthy AI dimensions such as bias, fairness, or explainability. These findings indicate a significant gap between the current state of AI/ML maturity models and the standards-aligned capability assessment organizations need, particularly given the regulatory requirements introduced by the EU AI Act.
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