Assessing AI-powered Customer Relationship Management Systems' Impact on Sales Performance

Thesis Abstract

The rapid integration of artificial intelligence (AI) into customer relationship management (CRM) systems has transformed sales processes across various industries, yet empirical assessments of its impact on sales performance remain limited. This study investigates the extent to which AI-powered CRM systems influence sales outcomes in mid to large-scale manufacturing firms, addressing the critical gap in understanding the practical benefits and challenges associated with AI-driven customer engagement strategies. The primary aim is to evaluate the relationship between AI-enabled functionalities within CRM systems and key sales performance metrics, including sales volume, customer retention rates, and sales cycle duration. Specific objectives include assessing the perceived effectiveness of AI features in enhancing customer insights, predicting customer needs, automating communications, and streamlining sales workflows; examining the moderating role of employee technological competencies; and identifying potential obstacles to AI adoption within sales teams. Employing a mixed-methods research design, the study combines quantitative data collection through structured questionnaires administered to a stratified sample of 150 sales professionals across 30 manufacturing firms, with qualitative insights obtained via semi-structured interviews with 20 sales managers. The questionnaire items are scaled on a Likert basis to measure perceptions of AI system capabilities, user satisfaction, and perceived impact on performance. Data collection instruments are validated through expert reviews and pilot testing, ensuring content validity and reliability coefficients exceeding 0.85 for key scales. Quantitative data are analyzed using multiple regression analysis and Structural Equation Modeling (SEM), specifically utilizing AMOS software, to test hypothesized relationships as per the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Qualitative data from interviews are analyzed thematically using NVivo, aiming to deepen the understanding of contextual factors influencing AI integration. Expected findings suggest that AI-powered CRM functionalities significantly enhance sales performance metrics, with automation of customer communications, predictive analytics, and personalized marketing playing pivotal roles. The analysis is anticipated to demonstrate that sales teams’ technological proficiency positively moderates the relationship between AI system usage and sales outcomes, whereas organizational resistance and perceived complexity act as barriers. The study also hypothesizes that high perceived ease of use and perceived usefulness, as postulated by TAM, are critical mediators in translating AI system adoption into improved sales results. This research makes a substantive contribution to knowledge by empirically validating the impact of AI-driven CRM tools in a manufacturing context—a sector where digital transformation remains nascent—while integrating well-established theoretical frameworks to explain user acceptance and usage patterns. The findings provide actionable insights for sales managers and IT professionals seeking to optimize AI implementation strategies, emphasizing training initiatives and change management practices to enhance adoption rates. In conclusion, the study affirms that AI-enabled CRM systems serve as valuable strategic assets for augmenting sales performance, conditional on effective user engagement and organizational support. It recommends that firms invest in targeted training programs to elevate employees’ technological competencies, establish change management frameworks to mitigate resistance, and continuously evaluate AI system usability and impact. Future research should explore longitudinal effects and extend the analysis to other sectors such as retail and services, with larger sample sizes to validate and generalize findings across different organizational contexts.