Abstract
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.
Highlights
Artificial intelligence (AI) is considered the lifeblood of an organization in different industries
The critical success factors identified from 13 studies are Data, Analytics, Technology and Tools, Intelligent Automation, Governance, People, and Organization, which could be considered the most critical dimensions
AI is a vital technology that is used in a variety of organizations
Summary
Artificial intelligence (AI) is considered the lifeblood of an organization in different industries. It is a disruptive technology that changes the conventional manner of working in an organization sustainably and radically (Gentsch, 2018). There are various technologies under AI, including machine learning, deep learning, natural language processing models, computer vision, and machine reasoning, which can produce this multimodal knowledge (Dahlan, 2018). AI could be defined as the ability of a system to correctly interpret external data, produce knowledge from those data to accomplish. Artificial intelligence maturity model: a systematic literature review.
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