Redesigning and Reinvention of Retail Industry Through Artificial Intelligence (AI)

Jaheer Mukthar, K. P.; Sivasubramanian, K.; Ramirez Asis, Edwin Hernan; Guerra-Munoz, Martha Esther

doi:10.1007/978-3-030-99000-8_3

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…It aids in identifying inefficient processes and potential areas for automation or improvement using AI technologies. Furthermore, it is essential for regulatory and compliance needs as well as in business process re-engineering, where the focus is on redesigning existing processes for greater efficiency or effectiveness [29]. The success of these AI-based tools heavily depends on the richness and quality of the data and how accurately it reflects the business processes.…”

Section: Current Processesmentioning

confidence: 99%

Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation

Aldoseri,

Al-Khalifa,

Hamouda

2024

ASI

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In an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and capabilities to effectively leverage AI technologies. This research focuses on the assessment elements required to evaluate an organization’s current state in preparation for AI-based digital transformation. This research is based on a literature review and practical insights derived from extensive experience in industrial system engineering. This paper outlines the key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation. This emphasizes the need for a comprehensive approach to assess the organization’s data infrastructure, governance practices, and existing AI capabilities. Furthermore, the research work focuses on the evaluation of AI talent and skills within the organization, considering the significance of fostering an innovative culture and addressing change management challenges. The results of this study provide organizations with elements to assess their current state for AI-based digital transformation. By adopting and implementing the proposed guidelines, organizations can gain a holistic perspective of their current standing, identify strategic opportunities for AI integration, mitigate potential risks, and strategize a successful path forwards in the evolving landscape of AI-driven digital transformation.

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Section: Current Processesmentioning

confidence: 99%

Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation

Aldoseri,

Al-Khalifa,

Hamouda

2024

ASI

View full text Add to dashboard Cite

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“…It aids in the identification of inefficient processes and potential areas for automation or improvement with AI technologies. Furthermore, it is essential for regulatory and compliance needs, as well as in business process reengineering where the focus is on redesigning the existing processes for more efficiency or effectiveness [29]. The success of these AI-based tools heavily depends on the richness and quality of the data, as well as how accurately it reflects the business processes.…”

Section: Process Documentationmentioning

confidence: 99%

A Holistic Guidelines for Assessing Organizational Current State for AI-based Digital Transformation

Aldoseri,

Al-Khalifa,

Hamouda

2023

Preprint

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“…A significant number of publications are devoted to general, review issues of AI applications in retail (Cao, 2021;Kreutzer & Sirrenberg, 2020) and to individual highly specialized studies (Querejeta Lomas et al, 2021;Pereira et al, 2022), often focused on a particular type of store (Jain & Gandhi, 2021;Margaje & Rasal, 2021;Rodgers et al, 2021;Xu et al, 2020) or on the specifics of a particular region (Bedi et al, 2022;Ersoy, 2022;Van Tuan et al, 2021). At the time of theoretical literature analysis, academic researchers' interest in ethical (Giroux et al, 2022;Mahmoud et al, 2020) and legal issues in AI application in retailing was very high, as modern scientific thought recognizes the need to apply ethical standards in AI application (Jaheer Mukthar et al, 2022). In doing so, Perrault et al (2019) identified 12 major ethical issues that must be addressed when implementing AI, including:…”

Section: Literature Reviewmentioning

confidence: 99%

Using Artificial Intelligence for Retail Value Chain

Burakhanova,

Baizhaxynova,

Duisebayeva

et al. 2023

International Journal of Service Science, Management, Engineering, and Technology

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The present study aims to prove hypotheses regarding artificial intelligence integration in retail value chains in the post-Soviet economic space. Hypotheses were proven within a comprehensive research project based on the use of quantitative research methods (questionnaires), which allowed studying the opinions of 512 retail managers in Azerbaijan, Kazakhstan, and Tajikistan. A specially designed questionnaire eliminated ambiguity in results interpretation by including both simple closed questions with a single choice and questions using a Likert scale. All the formulated hypotheses were proven, leading to the conclusion that the retail market of the post-Soviet economic space is not ready for the introduction of robotization and full automation of retail stores. The study results can be used by retail managers in the post-Soviet economic space as they choose the direction of artificial intelligence integration.

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Redesigning and Reinvention of Retail Industry Through Artificial Intelligence (AI)

Cited by 5 publications

References 26 publications

Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation

Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation

A Holistic Guidelines for Assessing Organizational Current State for AI-based Digital Transformation

Using Artificial Intelligence for Retail Value Chain

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