Nguyen, Phong and Mogaji, Emmanuel ORCID: 0000-0003-0544-4842 (2023) Artificial Intelligence for seamless experience across channels. In: Sheth, J, Mogaji, Emmanuel ORCID: 0000-0003-0544-4842, Jain, V and Ambika, A, (eds.) Artificial Intelligence in Customer Service: Next Frontier to Personalized Engagement. Palgrave, Cham, Switzerland. (In Press)

PDF (Accepted book chapter (forthcoming)) 38384_MOGAJI_Artificial_Intelligence_for_seamless_experience_across_channels.pdf - Accepted Version Restricted to Repository staff only Download (161kB) | Request a copy

Abstract

This chapter analyses the use of AI for seamless experiences across channels. Implementing seamless experiences across different channels is necessary, given the increased utilisation of different retail channels. 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 utilised 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
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 Faculty of Business > Department of Marketing, Events & Tourism Faculty of Business > Marketing Research Group (MRG)
Last Modified:	05 Jan 2023 10:23
URI:	http://gala.gre.ac.uk/id/eprint/38384

Actions (login required)

View Item

Downloads