Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective

Abstract

Summary Artificial intelligence (AI) in business processes and academic research in AI has significantly increased. However, the adoption of AI in organizational strategy is yet to be explored in extant literature. This study proposes two conceptual frameworks showing hierarchical relationships among the various drivers and barriers to AI adoption in organizational strategy. In a two‐step approach, the literature study is first done to identify eight drivers of and nine barriers to AI adoption and validated by academic and industry experts. In the second step, MICMAC (matrice d'impacts croises‐multiplication appliqúe a un classment or cross‐impact matrix multiplication applied to classification) analysis categorizes the drivers and barriers to AI adoption in organizational strategy. Total interpretive structural modeling (TISM) is developed to understand the complex and hierarchical associations among the drivers and barriers. This is the first attempt to model the drivers and barriers using a methodology like TISM, which provides a comprehensive conceptual framework with hierarchical relationships and relative importance of the drivers and barriers to AI adoption. AI solutions' decision‐making ability and accuracy are the most influential drivers that influence other driving factors. Lack of an AI adoption strategy, lack of AI talent, and lack of leadership commitment are the most significant barriers that affect other barriers. Recommendations for senior leadership are discussed to focus on the leading drivers and barriers. Also, the limitations and future research scope are addressed.

On the Journey to AI Maturity: Understanding the Role of Enterprise Artificial Intelligence Service

“…One study, amongst others, has recognized several barriers and their associations with each other, each negatively impacting the enterprise-wide scaling of AI [42]. It suggests that challenges, such as a lack of reusable models, reside on the first level, followed by a lack of usable data and trust in AI on the second level.…”

“…It suggests that challenges, such as a lack of reusable models, reside on the first level, followed by a lack of usable data and trust in AI on the second level. The threat of job security, poor infrastructure for AI model deployment, and a paucity of the AI talent and skills required are some other barriers to AI scalability [42]. In a quantitative study [43], the main obstacle is identified as a lack of adequate AI skills and talent to deploy AI models successfully [43].…”

“…Even though, at an overarching industrial level, AI has already been successfully adopted by a wide range of firms, not all specific requests made by individual users can be fully addressed by AI service providers. Previous studies have identified a lack of skills on the user side as a barrier to AI adoption [43], especially for small-and mediumsized firms [44], while other studies claim that AI models should be further improved, in order to expand their scalability [42].…”

Deep Learning in Business Analytics: a Clash of Expectations and Reality

“…Deep learning should be regarded as a powerful addition to the existing body of ML models instead of a "one size fits all " solution. Earlier studies ( Chui et al, 2018 ;Grover, Kar, & Dwivedi, 2022 ;Kar et al, 2021 ;Samek & Müller, 2019 ) have identified different barriers to AI adoption as computational complexity, missing big data infrastructure, lacking transparency, skill shortage, leadership commitment, and missing strategic guidance. All of those findings have the potential to impact the adoption speed of DL across different domains.…”

“…(4) Skill Shortage: Talent ( Kar, Kar, & Gupta, 2021 ) is required to implement those models as well as subject matter expertise to define use cases ( Henke et al, 2016 ). The current supply and demand gap for ML experts makes it difficult for small-and medium-sized corporations to utilize advanced AI.…”