Abstract

Digitalization is a driving force for innovation in the business-to-business (B2B) environment and profoundly changes the way companies do business. It affects the entire value chain of a company and can be used for automating human tasks. For instance, previous research indicates that 40% of all sales tasks can be automated. Thus the digital transformation in sales has the potential to improve a firm’s performance. Depending on its development level, digitalization in sales can assist or even replace numerous sales tasks. Therefore, using digital solutions in sales can be seen as an essential trigger to competitive advantage. Recent developments in research and practice have revealed that especially artificial intelligence (AI) has gained increasing attention in the sales domain. A challenging issue in this domain is how AI affects the sales process and how it can be applied meaningfully in B2B sales. Thus, our paper aims to investigate how AI can be used along the sales process and how it can improve sales practices.To explore this, we conduct systematic literature research in scientific databases such as Business Source Premier, Science Direct, Emerald, Springer Online Library, Wiley Online Library, and Google Scholar, supplementing the findings with a qualitative research approach. Analyzing this literature focused on digital transformation in sales, we find that the application and benefits of AI depend on the sales process step. For this reason, we conduct research on B2B sales process models, compare them, and choose a reference model for the evaluation of AI in B2B sales. Moreover, we present common definitions of AI and show how this technology is usually applied in B2B sales. Afterward, we combine the sales process with use cases of AI. For each step, we present use cases in detail and explain their benefits for sales. For instance, we find that especially tasks with traditionally high human involvement are challenging to automate. In particular, in complex sales situations, the human salesperson may not be entirely replaced by digital technologies, while routine tasks can be carried out with the help of digital technologies. Our paper closes with a discussion and conclusion. Summing up, the proposed paper analyzes different viewpoints of the sales process in the digital sales literature. We can conclude that the main focus of our paper will be presenting the application of AI along the sales cycle. Our research closes with a discussion and conclusion and gives recommendations for practice and academia.

Abstract

Abstract Understanding the impact of technology use on the business-to-business (B2B) sales profession has been one of the priorities for scholars for over 20 years. While the extant sales literature has focused mainly on stand-alone technologies like customer relationship management and social media, few studies have taken a holistic approach to understand how technology has transformed the sales function and the corresponding impact this change has had on the salesperson and sales organizational level. Employing morphological analysis (MA), we conduct an extensive review of the last two decades of B2B sales research to highlight emergent research topics on how technology use is continuing to influence B2B selling and identify research gaps that still need to be addressed by sales scholars. We characterize the literature on technology use in B2B sales in terms of 6 ‘dimensions’ and 22 ‘variants’ and represent it as an MA framework. Using this framework, we identify 49 research gaps that can inform future research on technology use in B2B sales. These gaps were prioritized using inputs from academics and practitioners and 10 gaps rated high by both groups were identified. We conclude with theoretical and practical implications of our research.

Abstract

Abstract The B2B sales process is undergoing substantial transformations fueled by advances in information and communications technology, specifically in artificial intelligence (AI). The premise of AI is to turn vast amounts of data into information for superior knowledge creation and knowledge management in B2B sales. In doing so, AI can significantly alter the traditional human-centric sales process. In this article, we describe how AI affects the B2B sales funnel. For each stage of the funnel, we describe key sales tasks, explain the specific contributions AI can bring, and clarify the role humans play. We also outline managerial considerations to maximize the contributions from AI and people in the context of B2B sales.

Abstract

Continuous advances in information technologies, such as Artificial intelligence (AI), are opening up new and exciting opportunities for value co-creation between economic actors. However, little is known about the mechanisms and the process of value co-creation enabled by AI. While scholars agree that AI technology significantly changes human activities and human resources, currently we do not have an adequate understanding of how humans and AI technology interact in value co-creation. This is the central phenomenon investigated in this article. Specifically, using Service-Dominant Logic (S-DL) as a lens, this study investigates the activities, roles and resources that are exchanged in Al-enabled value co-creation, using the creation of competitive intelligence as a research context. The analysis suggests that Al-enabled value co-creation processes are complex interactions between human and non-human actors who perform any of six different roles either jointly or independently. This article contributes to SD-L and provides a deeper understanding of the activities (the ‘how’), the actors (the ‘who’), and the resources (the ‘what’) in Al-enabled value co-creation, thus helping to close an identified gap in the literature.

Abstract

AI-driven strategies are changing training and performance within the ever-changing sales environment. Our quantitatively-based empirical research in Pakistan explores the impact of AI upon salespersons. Data was collected using Simple random sampling from 178 pharma representatives along with managers via email, telephone, and physical questionnaires. SEM-PLS was the analytical instrument we used. The findings provide important new information. Training solutions with AI-driven techniques provide customized instructional methods by evaluating the efficiency information for particular sales representatives. On the basis of one’s abilities and areas for development, particular training materials, programs, and activities are suggested. Sales representatives can quickly adjust approaches with immediate reaction. AI adapts training materials constantly according to achievement, preserving a rigorous and fruitful educational setting. Role-playing exercises are made easier by AI-generated authentic selling situations. Representatives acquire meaningful expertise above typical scenarios by practicing managing difficult clients and negotiating complex scenarios. AI improves creating strategies and making decisions by analyzing sales encounter information. Strategies that work and those that could use better are made clear. Irrespective of staff size, based on artificial intelligence training scalable effortlessly to provide identical standards throughout sales departments. Organizations need to implement these game-changing strategies as AI proceeds to alter sales in order to survive in a cutthroat industry. Lastly, this paper provide practical implications for different stakeholders and future research directions related to this study.

Abstract

This study aims to deeply examine the role and impact of AI implementation in the B2B sales process, including implementation challenges. The method used is a qualitative approach with descriptive studies, through a literature review of scientific journals, industry reports, and relevant previous studies. The analysis results show that the application of AI in B2B sales not only improves the accuracy of marketing strategies and lead conversion, but also strengthens the concept of value co-creation through collaboration between salespeople and AI systems. Technologies such as machine learning, predictive analytics, and NLP-based chatbots have been proven to accelerate sales cycles, expand service reach, and increase productivity by up to 30%. However, implementation challenges remain, including limited digital infrastructure, a lack of competent human resources, and organizational resistance to technological transformation. Therefore, optimal AI integration requires institutional readiness, adaptive strategies, and continuous investment in technology and human resource development. These findings provide theoretical and practical contributions to the development of AI-based B2B sales strategies, particularly in the context of the digital industry in Indonesia. Furthermore, the application of AI in B2B sales also opens up new opportunities for service personalization. With the support of real-time data analysis, companies can better understand the specific preferences and needs of their business partners. This enables the development of more targeted communication strategies and the enhancement of long-term relationships with business customers. AI also plays a role in reducing human error through automated systems that can validate data, provide predictive recommendations, and support faster and more accurate strategic decision-making. Furthermore, the adoption of AI in B2B requires clear regulations and governance, particularly regarding ethical data use and information security. Companies must be able to balance the use of technology to increase efficiency with the protection of privacy and consumer rights.