The Generative AI Sales Paradox (GASP)-Enhancing Sales Scalability at the Cost of Human-Led Relationship Building in B2B Markets

This study examines a growing concern in B2B sales, which it refers to as the Generative AI Sales Paradox (GASP). Generative AI tools help sales teams move faster and reach more clients. Still, they can unintentionally weaken the kind of personal relationships that matter most in high-value, trust-based transactions. This research focuses on industries such as manufacturing, professional services, and enterprise technology, where long-term client trust is essential. Both qualitative and quantitative methods are used in this research. On the qualitative side, six B2B firms using Generative AI in their sales operations were studied. While sales leaders praised the improvements in lead follow-up and speed, they also shared customer concerns about the loss of personal interaction. Clients often felt that AI generated responses lacked empathy or genuine understanding. Survey data from 250 companies backed this up. While most firms achieved efficiency gains of 25-40%, many also reported a decline in customer satisfaction, particularly those that relied heavily on AI. In these cases, growth in long-term revenue either stalled or declined. The problem appeared to stem from weaker client connections. To help explain these findings, this study used the Scalability vs. Authenticity Trade-Off Theory (SATOT). This framework suggests that while AI is useful for scaling tasks, it struggles with the emotional and social complexity involved in building lasting relationships. In response, this study proposes a blended approach. Let AI handle repetitive tasks, but keep humans in charge of complex, trust-building conversations. This hybrid model can help firms gain efficiency without sacrificing client loyalty. In short, firms that manage AI use carefully, without letting it replace human interaction, are more likely to thrive in the long run.International Conference on Machine Learning, Artificial Intelligence and Data Science (ICMLAIDS 2026)www.srcmeetings.com12Conference Proceedings DOI: doi.org/10.47363/JAICC/ICMLAIDS2026/2026(5)12