Artificial Intelligence for seamless experience across channels
Nguyen, Phong and Mogaji, Emmanuel ORCID: 0000-0003-0544-4842 (2023) Artificial Intelligence for seamless experience across channels. In: Sheth, Jagdish N., Jain, Varsha, Mogaji, Emmanuel ORCID: 0000-0003-0544-4842 and Ambika, Anupama, (eds.) Artificial Intelligence in Customer Service: Next Frontier to Personalized Engagement. Palgrave Macmillan - Springer International Publishing, Cham, Switzerland, pp. 181-203. ISBN 978-3031338984; 978-3031338977; 978-3031339004 (doi:https://doi.org/10.1007/978-3-031-33898-4)
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Abstract
This chapter analyzes the use of AI for seamless experiences across channels. Implementing seamless experiences across different channels is necessary, given the increased utilization of different retail channels. Furthermore, with the growing amount of data and computer processing capabilities, it is essential to explore how businesses can integrate artificial intelligence (AI) and machine learning (ML) with the data to enhance their business operations. This information would be useful for business owners contemplating their digital transformation strategies, AI developers working on different algorithms for businesses, and policymakers ensuring an enabling environment to support digital transformation. The chapter entails a literature review to enumerate key findings later utilized to devise managerial implications and policy recommendations. Significant managerial implications include the need for business leaders to invest in AI-enabled value chains, evaluate the deployment of AI in the business, and make necessary enhancements by collaborating with AI experts to create better customer service programs provided in the chapter. Likewise, the chapter offers policy recommendations that revolve around implementing policies that support consumer data protection, skills development, and talent acquisition in AI.
Item Type: | Book Section |
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Uncontrolled Keywords: | customer experiences; Artificial Intelligence; Omni-channel; retail experience; personalised engagement marketing; machine learning |
Subjects: | B Philosophy. Psychology. Religion > BF Psychology H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management T Technology > T Technology (General) |
Faculty / School / Research Centre / Research Group: | Faculty of Business |
Related URLs: | |
Last Modified: | 07 Dec 2023 10:48 |
URI: | http://gala.gre.ac.uk/id/eprint/38384 |
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