The increasing integration of artificial intelligence (AI) in businesses is transforming service delivery. AI systems now handle tasks previously performed by human employees, raising questions about the impact on employee job satisfaction and career progression. Existing research presents contradictory findings: some studies suggest AI reduces employees’ perception of career achievement due to job displacement and insecurity, while others propose that AI enhances career achievement by improving efficiency and service quality. This study aims to resolve this contradiction by introducing the concept of 'intelligent service strategy' – the organizational approach to integrating AI into service processes. The central research question is how different intelligent service strategies (specifically, AI substitution and AI collaboration) affect employees’ perception of career achievement. Understanding this impact is crucial because frontline service employees, often lacking tangible rewards, rely heavily on intrinsic fulfillment for motivation and performance. Their job satisfaction directly affects customer experiences and business outcomes. The study hypothesizes that the intelligence substitution strategy, replacing human interaction with AI, negatively impacts career achievement, while the intelligence collaboration strategy, supplementing human work with AI, positively impacts it. The study further explores the moderating role of organizational innovation climate, suggesting that a supportive innovative environment may mitigate the negative impacts of AI substitution.

The literature review examines existing research on career achievement perception, self-perception theory, and the impact of AI on the workplace. Research on career achievement focuses on factors like job performance, organizational climate, and individual characteristics (e.g., gender, age, self-efficacy). However, this research is often fragmented and lacks a comprehensive understanding of the impact of AI integration. Self-perception theory provides a framework for understanding how individuals infer their internal states (like career achievement) by observing their behaviors and the context. Studies have also shown AI's impact on employees can be dualistic: While AI can improve efficiency, it can also cause job insecurity and a sense of displacement. However, the literature has mostly addressed consumer responses to AI and ignored the impact on employee intrinsic motivation, creating a research gap addressed in this paper.

The study employs a multi-method approach using three experiments involving a total of 736 frontline employees from various service sectors (experience-based and trust-based services). Study 1 uses a single-factor between-subjects design to compare the effects of intelligence substitution and collaboration strategies on career achievement, mediated by human-human and human-machine interactivity. Participants were presented with scenarios describing different organizational strategies and measured their perceptions of career achievement, human-human interactivity, and human-machine interactivity. Control variables included gender, education, and experience with AI. Study 2 adopts a 2 x 2 between-subjects design, adding organizational innovation climate as a moderator variable. Participants were exposed to scenarios representing high and low innovation climates and the two service strategies, with similar dependent variables as Study 1. Study 3 uses field data from employees in a provincial branch of the Agricultural Bank of China, again comparing the two service strategies and innovation climate using similar measures as the previous studies. Data analysis for studies 1 and 2 involved ANOVA, MANOVA, and regression analysis with PROCESS model 4 for mediation analysis. Study 3 utilized PLS-SEM with multigroup analysis to examine the moderating effects.

Study 1 found that the intelligence substitution strategy significantly reduced employees' perception of career achievement compared to the collaboration strategy. This effect was mediated by both human-human and human-machine interactivity: substitution reduced human-human interaction and increased human-machine interaction, which negatively influenced career achievement. Human-human interactivity showed a positive relationship with career achievement, while human-machine interactivity had a negative relationship. Study 2 confirmed the results of Study 1 and revealed that organizational innovation climate significantly moderated the relationships between interactivity and career achievement. In a high-innovation climate, the negative impact of human-machine interaction and positive impact of human-human interaction were lessened. Study 3, using field data, replicated the findings of the previous two studies, confirming the robustness of the results. PLS-SEM analysis showed a significant positive effect of the intelligence collaboration strategy on career achievement, mediated by the two types of interactivity. The moderating role of organizational innovation climate was also confirmed, with the moderated mediating effects being stronger in environments with low innovation climates. Specifically, the negative impact of the substitution strategy on career achievement was stronger in low-innovation climate organizations. In high-innovation environments, the negative effect was mitigated because employees adapt more readily to new technologies, mitigating the negative impact on their sense of career achievement.

The findings strongly support the hypotheses. The type of intelligent service strategy significantly impacts employees’ perception of career achievement, mediated by the level of human-human and human-machine interactivity. The substitution strategy, by reducing human contact, undermines employees' sense of value and contribution, leading to lower career achievement. The collaboration strategy, leveraging AI to augment human capabilities, maintains a sense of human involvement and fosters higher career achievement. The moderating role of organizational innovation climate highlights the importance of organizational context. In innovative environments, employees adapt more effectively to AI, mitigating the negative impacts of human-machine interaction and reducing the positive effects of human-human interaction on career achievement. These findings resolve the existing contradictions in the literature by demonstrating that the effect of AI on career achievement depends heavily on the implementation strategy and organizational culture. This nuanced understanding is crucial for organizations seeking to harness AI's potential without compromising employee morale and productivity.

This research makes several key contributions. First, it addresses the gap in the literature by focusing on the impact of AI on employees’ intrinsic motivation, specifically career achievement. Second, it provides a comprehensive model explaining the differential effects of AI substitution and collaboration strategies. Third, it highlights the crucial moderating role of organizational innovation climate. Future research could investigate the role of customer feedback in shaping employees’ perception of career achievement, explore the moderating effect of employees' psychological job demands, and expand the classification of interactivity to include three-way interactions between employees, customers, and AI.

The study relies on self-reported data, which might be susceptible to biases. The sample, while large, may not fully represent all service industries and organizational contexts. Future studies could use more diverse samples and incorporate objective performance measures alongside self-reported data to strengthen the validity and generalizability of findings. The study focuses on frontline employees, and findings might not directly generalize to other roles. The specific scenarios presented in the experiments might have influenced participant responses; future studies could explore this further. Finally, the time-lag between the implementation of AI strategies and the measurement of career achievement perceptions could be studied in future research.