Aim/Purpose
This study explores how generative AI is being integrated into unified communications (UC) platforms, focusing specifically on Microsoft Copilot as implemented in Microsoft Teams. It explores how generative AI enhances UC functionalities, identifies key adoption challenges, and provides insights into implementation strategies. Unlike traditional technologies that followed a gradual adoption curve, Copilot’s integration into Teams has the potential to accelerate its adoption, necessitating organizations to be proactive in their planning for its use.

Background
UC platforms have transformed enterprise communication by integrating multiple tools into a single interface. The integration of generative AI into UC introduces automation of complex routine and time-intensive tasks, enhanced decision support, and workflow optimization. However, adoption dynamics, user experiences, and long-term organizational impacts remain underexplored.

Methodology
This study employs a meta-analytic approach, synthesizing findings from peer-reviewed articles, conference proceedings, and industry reports. The analysis categorizes user perceptions of AI usefulness, key adoption barriers, and best practices for integration.

Contribution
This study evaluates the emerging literature on generative AI in UC platforms, focusing on initial user impressions and adoption challenges. Given the technology’s early stage, the findings provide preliminary insights to help organizations plan for effective AI integration in UC environments.

Findings
The findings indicate that generative AI in UC platforms enhances productivity, streamlines workflows, and improves decision support through features such as meeting summarization, transcription, and AI-driven content generation. However, adoption challenges, including resistance to change, data privacy concerns, and integration complexities, remain key barriers.

Recommendations for Practitioners
Preliminary findings indicate that users recognize the value of UC platforms integrated with generative AI and anticipate increasing benefits over time. However, successful adoption requires strategic planning to address implementation challenges and ensure effective deployment.

Recommendations for Researchers
As AI technologies evolve, further research is needed to assess the long-term impact of generative AI in UC platforms on workplace efficiency, productivity gains, user adaptation, and organizational transformation. Comparative research across industries can provide domain-specific best practices, while investigations into human-AI collaboration should examine the balance between automation and human oversight to optimize AI’s role in workplace communication.

Impact on Society
The integration of generative AI in UC platforms has far-reaching implications for enterprise communication, workforce collaboration, and digital transformation. AI-driven automation is poised to enhance workplace efficiency, but responsible governance and deployment are crucial for ensuring fair and transparent adoption.

Future Research
Future research is needed to explore the evolving role of agentic AI and its impact on enterprise workflows and strategic decision-making. Studies should assess its role in reducing cognitive load and enhancing team coordination while also addressing adoption challenges such as ethics, automation reliability, and user trust in autonomous AI systems.