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Artificial intelligence maturity model: a systematic literature review.

Raghad Baker Sadiq1,2, Nurhizam Safie1, Abdul Hadi Abd Rahman3

  • 1Center for Software Technology and Management, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.

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Summary
This summary is machine-generated.

This study reviews artificial intelligence (AI) maturity models, finding most are domain-specific and focus on development over validation. Organizations need to strengthen AI capabilities and maturity.

Keywords:
Artificial IntelligenceMaturity modelOrganizationSystematic literature review

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Area of Science:

  • Computer Science
  • Information Systems

Background:

  • Organizations widely adopt artificial intelligence (AI) maturity models for systematic development.
  • Existing research focuses on developing AI maturity models, with varying empirical validation.

Purpose of the Study:

  • To systematically review state-of-the-art studies on AI maturity models.
  • To understand methodological issues in AI maturity model development, validation, scope, and characteristics.

Main Methods:

  • Systematic literature review of AI maturity models.
  • Analysis of model objectives, development/validation methods, scope, and characteristics.

Main Results:

  • Most AI maturity models are developed for specific domains and purposes, often without empirical validation.
  • A bottom-up design approach is common, with descriptive characteristics and a five-level continuous representation trending.
  • A significant portion of studies (46%) assess the technology aspect of AI maturity.

Conclusions:

  • Organizations require improvements in AI capability and maturity.
  • This review contributes to understanding AI maturity models for organizations evolving with AI.