Drivers, Barriers and Social Considerations for AI Adoption in Business and Management: a Tertiary Study

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The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews. The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise the findings related to drivers, barriers and social implications of the AI adoption in business and management. The methodology used for this tertiary study is based on Kitchenham and Charter's guidelines [14], resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2021 primary studies. These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain, health and safety, project management, decision-support, systems management and technology adoption). While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders'perspectives. Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors of the AI adoption. In addition to increased focus on social implications of AI, the reviews are recommending more rigorous evaluation, increased use of hybrid solutions (AI and non-AI) and multidisciplinary approach to AI design and evaluation. Furthermore, this study found that there is a lack of systematic reviews in some of the early AI adoption sectors such as financial industry and retail.

Original languageEnglish
Article number101257
JournalTechnology in Society
Early online date23 Apr 2020
Publication statusPublished - Aug 2020


  • artificial intelligence, business, machine learning, management, systematic literature review, tertiary study
  • Tertiary study
  • Machine learning
  • Management
  • Artificial intelligence
  • Business
  • Systematic literature review


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