AI for Management: An Overview

Gil, Darı́o; Hobson, Stacy; Mojsilović, Aleksandra; Puri, Ruchir; Smith, J. R.

doi:10.1007/978-3-030-20680-2_1

Cited by 37 publications

(12 citation statements)

References 9 publications

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“…As a result, organizations can attain a competitive advantage through AI‐driven management (Schrettenbrunnner, 2020). Recent developments in AI are applied to management tasks (Gil et al, 2020) and functions such as strategic management, marketing management, and customer management.…”

Section: Introductionmentioning

confidence: 99%

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

Kar¹,

Kar

Gupta

2021

Intell Sys Acc Fin Mgmt

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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.

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Section: Introductionmentioning

confidence: 99%

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

Kar¹,

Kar

Gupta

2021

Intell Sys Acc Fin Mgmt

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“…Artificial intelligence (AI) is a well-known technology that has been used in every field from manufacturing industries to agriculture, healthcare to human resources, and gaming to businesses [ 55 , 56 , 57 , 58 , 59 ]. Artificial intelligence enables machines to mimic the human brain and it learns from the experience just like a human does.…”

Section: Metaverse Framework and Building Block Technologiesmentioning

confidence: 99%

Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security

Ali

Abdullah

Armand

et al. 2023

Sensors

123

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Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor’s environment, the patient’s environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.

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“…Effective time management is imperative as people’s daily lives are very busy, and time management is a task for which AI can be an effective tool. Applying the appropriate AI tools to time management can effectively support the user [ 35 ]. In this sense, AI-facilitated exercise services based on customer data will be an effective tool for busy people who value exercise.…”

Section: Literature Reviewmentioning

confidence: 99%

How to Increase Sport Facility Users’ Intention to Use AI Fitness Services: Based on the Technology Adoption Model

Chin

Kim

2022

IJERPH

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Artificial intelligence (AI) has recently been introduced as a new way of analyzing and predicting sport consumer behavior. The goal of this study was to investigate the relationships among the perceived usefulness, perceived ease of use, the importance of exercise, attitudes towards use, and the behavioral intention to use AI services based on the technology adoption model. The authors recruited 408 participants who participated in an experiment designed to provide a deeper understanding of AI fitness services. After screening, the collected data were screened through assumption tests, and we conducted a confirmatory factor analysis and structural equation modeling to analyze research hypotheses. The results indicated that three types of consumer evaluations (i.e., perceived usefulness, perceived ease of use, and importance of exercise) positively influence their attitudes toward AI fitness services. In addition, the positive attitudes regarding AI services positively influenced the intention to use AI services. The results of this research contribute to our knowledge of the consumers’ attitudes and behaviors toward AI services in the sport industry based on the technology acceptance model. Furthermore, this study provided the empirical evidence critically needed to increase our understanding of AI in the sport industry and offered new insights into how sport facility managers can predict their consumers’ intention to use AI services.

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AI for Management: An Overview

Cited by 37 publications

References 9 publications

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

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

Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security

How to Increase Sport Facility Users’ Intention to Use AI Fitness Services: Based on the Technology Adoption Model

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