Artificial intelligence has emerged as a transformative force in organizational management, with contemporary research illuminating its profound impact on employee work patterns, daily operations, and organizational effectiveness. Recent studies published in Harvard Business Review and MIT Sloan Management Review document accelerating adoption among Fortune 500 companies, with over 60% now integrating AI systems into their operational frameworks.  Through comprehensive analysis of scholarly literature, I have conducted a review examining how AI is reshaping the everyday reality of employees and organizational dynamics. The literature reveals three critical domains where AI is fundamentally altering workplace realities.   First, AI adoption is transforming daily work routines and task allocation across various roles. In customer service environments, AI chatbots are handling routine inquiries, allowing human representatives to focus on complex problem-solving and relationship building. This shift is creating dual effects: reducing repetitive workloads while simultaneously requiring employees to develop enhanced interpersonal and critical thinking capabilities.   Second, AI implementation is reshaping organizational communication and collaboration patterns. In project management contexts, AI tools are automating status tracking and resource allocation, changing how teams interact and coordinate their efforts. This raises important questions about how these shifting interaction patterns affect workplace relationships and team cohesion.  Third, AI adoption is influencing employee work-life balance and psychological well-being in multifaceted ways. Research from organizations like Siemens demonstrates that while AI automation can reduce overtime by handling routine tasks, it can also create "always-on" expectations as work processes become more efficient. Employee satisfaction appears heavily influenced by organizational approaches to AI implementation, with transparent communication and skills development opportunities emerging as critical factors in positive adoption experiences.  For the next phase of this research, I will employ a dual-methodology approach combining comprehensive surveys with controlled experiments. The survey component will collect quantitative data from employees across multiple industries on AI interaction frequency, perceived impact on productivity, job satisfaction, and work-life balance. Simultaneously, I will conduct a series of controlled experiments where participants will complete standardized tasks with varying levels of AI assistance, allowing direct measurement of performance metrics, cognitive load, and satisfaction indices. This experimental design will enable causal inference regarding how specific AI implementations affect both individual work experience and organizational outcomes. By triangulating survey data with experimental findings, I aim to develop evidence-based frameworks that help organizations implement AI solutions that simultaneously enhance operational efficiency.

High performers’ voice offers numerous advantages to organizations and previous research has demonstrated that supervisors tend to endorse it. However, following social dominance theory, such a voice may be perceived as a threat to the established hierarchy, leading to diminished voice endorsement. Drawing on associative evaluation theory, I argue that the degree to which supervisors endorse a high performer’s voice is fundamentally determined by the supervisor’s organizational-focused associations that the voice elicits. I contend that there is a three-way interaction effect, involving high performers, coalition voice, and the supervisor’s power motivation, on managerial voice endorsement. More specifically, a high performer’s coalition voice is less likely to receive managerial endorsement via organizational-focused associations compared to a non-coalition voice, especially from supervisors with high power motivation. Results from a scenario-based experimental study sampling of 226 supervisors conducted in the United States, supported my hypotheses. I discuss the implications of my findings both theoretically and practically and offer directions for future research.