23rd Conference of the International Federation of Operational Research Societies
Abstract Submission

1041. Artificial Intelligence in Customer Interactions of Small and Medium-Sized Enterprises: A Conceptual Framework

Contributed abstract in session HE-35: Innovative Applications of Knowledge, cluster Knowledge, Technology, and Innovation.

Thursday, 16:15-17:45
Room: FENH308

Authors (first author is the speaker)

1. Tim Kanis
International Management and Corporate Strategy, TU Bergakademie Freiberg

Abstract

Artificial intelligence (AI) has the potential to significantly improve how companies interact with their customers. For example, AI can enable a better understanding of customer demands, automated responses to routine requests and 24/7 service availability. However, implementing AI requires specialised skills and sufficient resources – a prerequisite that is particularly difficult for small and medium-sized enterprises (SMEs) to achieve. Research has shown that SMEs often lack a clear understanding of the benefits AI can bring to specific tasks, lack the necessary technological know-how and data, and face scepticism both internally from their employees and externally from their customers. Consequently, research has shown that SMEs struggle to take advantage of the potential benefits of this technology. I address this problem by providing a conceptual framework for (1) application scenarios of AI in SME customer interactions and (2) strategies SMEs can use to successfully implement AI. Regarding (1), for example, SMEs can use natural language processing applications such as chatbots to make appointments with customers, answer routine queries and sell products and services. This comes with the advantage of relieving the often scarce human resources of SMEs. In addition, machine learning can facilitate the analysis of purchasing behaviour and predict customers' buying and decision-making behaviour. This can lead to higher sales and competitive advantages. Regarding (2), for example, SMEs can address the lack of data by using not only internal but also external resources (e.g. cloud providers, competitors). Furthermore, SMEs can introduce key performance indicators (KPIs) to measure the success of AI implementation – a field which is understudied. For example, measuring customer satisfaction in the context of AI interaction could not only help SMEs better address customer needs, but also overcome scepticism. This study contributes to research on SMEs and AI in two ways. First, it provides a comprehensive overview of the application scenarios of AI for SMEs, an area that has not been sufficiently addressed in previous research. Second, it provides a better understanding of what strategies SMEs can adopt to successfully use AI in customer interactions.

Keywords

Status: accepted

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