Empowering B2B Sales Through AI-Driven Human Resource Strategies: Bridging Marketing and HR for Enhanced Sales Performance
The processes governing business-to-business (B2B) sales have witnessed slow evolution. The conjunction of AIs and planned HR practice provides a window of opportunity to heighten sales performance. This study emphasizes the cooperation of AI, HR, and Marketing in B2B sales processes. AI will disrupt talent acquisition, training, and performance evaluation in selling. AI will help an organization find a winning profile of its candidate and help personalize training solutions while also offering real-time insights for performance analytics to build a responsive sales team. Integration will bring a closer alignment of marketing and HR functions to a cohesive consumer engagement approach. Furthermore, the authors discussed how, through the conventional customer relationship management (CRM) framework, AI makes it easier for marketers to understand their clients and appreciate their preferences and behavior thoroughly. This knowledge will enable them to develop personalized marketing strategies and approaches that improve consumer satisfaction and loyalty. The integrative approach bridges Marketing and HR, promoting a continuous improvement culture steered by AI, according to the authors. This study argues that competitiveness in B2B selling hinges greatly on collaboration between disciplines and the underlying support of higher-level AI technologies. This investigation strengthens the existing body of research by offering a more detailed examination of the impact of AI on the HR and advertising departments in B2B sales. This study presents managers with a foundational understanding of how to integrate technology to increase sales performance.
Authors
- Meenal Pendse (ORCID: https://orcid.org/0000-0001-7225-311X)
- Meghasham Chaudhari (ORCID: https://orcid.org/0000-0002-0210-7990)
- Varsha Murhe
Institutions
- MIT World Peace University (IN)
- Professional Beef Services (US)
Publication Details
- Published
- 2025-04-04
- DOI
- https://doi.org/10.1109/iccsai64074.2025.11064015
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00