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Artificial intelligence (AI) applications’ usage in Chief Officers’ (CXOs’) decision-making is a topic of current research interest. A fundamental dilemma is carefully planning an effective combination of a CXO’s professional experiences and AI applications’ decision-making responsibility. However, the existing literature fails to specify the value of co-creation of AI applications and the human experience in managerial decision-making. To address this gap in the literature, the authors’ examine how an ideal cognitive-technology fit can be created between human experiences and AI-based solutions at CXO-level decision-making using the theoretical lens of the Service-Dominant Logic.

The authors’ employed a grounded theory approach and conducted a focus group discussion with seven participants to shed light on the factors that may balance AI applications’ usage and CXOs’ experience in making business decisions. This was followed by 21 in-depth interviews with employees from knowledge-intensive professional service firms to validate the findings further of a new phenomenon. Further, given the newness of the phenomenon, this approach allowed researchers a retrospective and real-time understanding of interviewees’ experiences of the phenomenon under consideration.

The advantages and constraints of both CXOs’ experiences and AI applications deserve due consideration for successfully implementing technology in knowledge-intensive professional service organizations.

This study may appeal to researchers and practitioners interested in the future of decision-making, as the authors’ study findings advocate for balancing CXO’s expertise and the use of AI in decision-making.

Based on the preliminary findings, the authors developed a theoretical framework to understand the factors that govern AI implementation in an organization and how a competitive strategy may emerge from value co-created by AI applications and CXOs’ experience, particularly in knowledge-intensive professional service firms.

The purpose of this paper is to identify and model critical barriers to cloud computing adoption (CCA) in Indian MSMEs by the interpretive structural modeling (ISM) approach.

In this paper, through a literature survey and expert opinions, 14 critical barriers were identified, and the ISM tool was used to establish interrelationship among the identified barriers and to determine the key barriers having high driving power.

After analyzing the barriers, it was found that three barriers, namely, lack of confidentiality (B8), lack of top management support (B3) and lack of sharing and collaboration (B2) were most significant.

The developed model is based on the expert opinions, which may be biased, influencing the final output of the structural model. The research implications of the developed model are to help managers of the organization in the understanding significance of the barriers and to prioritize or eliminate the same for the effective CCA.

This study is for the first time an attempt that has been made to apply the ISM methodology to explore the interdependencies among the critical barriers for Indian MSMEs. This paper will guide the managers at various levels of an organization for effective implementation of the cloud computing practices.