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AI is a branch of research that aims to develop machines or systems that can do tasks that typically require human intelligence (Guingrich & Graziano, 2024; Martini et al., 2024; Qadri et al., 2024; Waly, 2024). AI has emerged as a prominent subject of discussion in modern boardrooms and social gatherings (Eroğlu & Karatepe Kaya, 2022; Möslein, 2018). It encompasses the ability of a system to effectively interpret external input, acquire knowledge from such data, and utilize that knowledge to accomplish predetermined goals and tasks using adaptable modifications (Mikalef & Gupta, 2021; Nemati et al., 2002; Triguero et al., 2024). Nevertheless, the field of AI continues to present numerous unresolved issues, such as its application to breast cancer, product making and innovation, IoT security, language translation problems, online human algorithm interaction, and health issue diagnosis (Alwahedi et al., 2024; Shin, 2024; Shin et al., 2024a).
Gaining a comprehensive understanding of AI necessitates the adoption of a more refined methodology (Kee et al., 2024). Four discrete academic and professional scientific groups have historically outlined the concept of “artificial intelligence:” individuals focused on “rational action,” “human action,” “rational thinking,” and “human thinking” (McAfee & Brynjolfsson, 2017a; Tataj & Muhamet, 2021).
According to McAfee and Brynjolfsson (2017a), AI is categorized into two main types: narrow, or weak, AI; and strong AI, also known as artificial general intelligence (AGI). This classification is supported by Fjelland (2020) and Gobble (2019). Most practical advancements in AI stem from narrow AI (Wahl et al., 2018), which operates within limited frameworks to mimic human intelligence. Examples include AI personal assistants, partially autonomous vehicles, International Business Machines Corporation (IBM)’s Watson, Google search, and image recognition software (Ghorpade, 2020; Mich, 2020; Škavić, 2019).
In contrast, AGI refers to AI systems with human-level intelligence (Kumpulainen & Terziyan, 2022; Landgrebe & Smith, 2019), though progress toward AGI has been limited (Baum, 2017; Summerfield, 2022). Advances in narrow AI have significantly impacted various business management and service domains, leading to substantial transformations (Dwivedi et al., 2021; Fu et al., 2023; Gazi, 2024) and enabling the exploration of new applications. Davenport and Ronanki (2018) highlighted ML, deep learning, signal processing, and natural language processing, natural language understanding, and natural language generation as crucial AI technologies. Notable applications include speech recognition, image recognition, and computer vision (Majumder, 1988; Marcus et al., 2022; Michie et al., 2017; Najam et al., 2022). The increasing use of AI technologies, such as generative AI tools, in organizations underscores the importance of understanding surrounding issues such as technical competency, ethical considerations, and associated risks (Bankins et al., 2024; Rane et al., 2024).