AI-Driven Sales Strategies in Industrial B2B Technology Markets : Understanding How AI Reshapes Sales Work in Industrial B2B Technology Firms

Sammanfattning

This thesis explores the integration of artificial intelligence (AI) into the sales strategies and day-to-day practices of industrial business-to-business (B2B) technology companies. With the shift of AI from a supplementary tool to an essential resource in business networks, this study intends to examine how such a change impacts the nature of work done by sales professionals and the buyer-seller relationship. In order to gain insight into the changes and relations described above, the Resource Interaction Approach is adopted from the Industrial Marketing and Purchasing (IMP) tradition. It treats AI as a resource that creates value only when combined with human judgment and daily business routines. The research uses a qualitative approach. It is centered on ten semi-structured interviews with professionals from industrial B2B technology markets. The choice of the methodology helped to uncover the dynamics of AI implementation in high-stakes negotiations where building trusting relations plays a significant role. Following the analysis of empirical data, three distinct layers in which AI is applied in sales processes emerged. Efficiency, insight, and strategic. The findings reveal that AI significantly reduces administrative burdens and enhances prospecting precision. It also presents a differentiation challenge which comes from the over-reliance on standardized AI outputs. This lack of differentiation risks eroding a firm’s unique brand voice and authentic trust. Further, the study demonstrates that successful integration of AI relies on a Human-in-the-Loop (HITL) approach, leading to what is termed “Collaborative Intelligence.” In this collaborative intelligence model, the salesperson’s identity shifts from brokering pure product or service specific knowledge to more focusing on interpreting complex social nuances that AI cannot yet replicate. The results suggest that organizational silos and a lack of identity-preserving training remain the primary bottlenecks preventing firms from utilizing collaborative intelligence to its fullest. For industrial B2B tech firms to maintain a competitive advantage in the foreseeable future, the thesis concludes they should balance AI’s analytical power with the human element of B2B relationship management. This research contributes to theory by expanding the RIA framework to digital resources. It also contributes to practice by providing guidance for industrial B2B sales managers to navigate the transition toward AI- augmented sales organizations.