Artificial intelligence maturity model: a systematic literature review

Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant...

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Bibliographic Details
Published in:PeerJ. Computer science 2021-08, Vol.7, p.e661-e661, Article e661
Main Authors: Sadiq, Raghad Baker, Safie, Nurhizam, Abd Rahman, Abdul Hadi, Goudarzi, Shidrokh
Format: Article
Language:English
Subjects:
Algorithms and Analysis of Algorithms
Artificial Intelligence
Automation
Competition
Competitive advantage
Computational linguistics
Domains
Empirical analysis
Forecasts and trends
Language processing
Literature reviews
Maturity model
Natural language interfaces
Organization
Organizations
Productivity
Semantic web
Software
State-of-the-art reviews
Systematic literature review
Technology assessment
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Summary:Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.661