Překonávání překážek integrace umělé inteligence do projektového řízení

This thesis explores the impact of resistance to change on the implementation of artificial intelligence in project management. AI has the potential to transform the field, yet it faces challenges, one of which is resistance—a phenomenon that naturally arises when new technologies are introduced. However, there is currently no comprehensive analysis of this phenomenon in the context of AI in project management. In this study, the author conducts a literature review to identify key factors influencing resistance to change. Subsequently, based on qualitative research, specifically semi-structured in-depth interviews with eight participants (project management professionals with more than three years of experience in the field and familiarity with AI tools), conclusions are drawn about the factors influencing resistance specifically in the context of AI implementation in project management. The findings indicate that personality traits, skills and education, uncertainty and perceived threat, colleagues’ attitudes toward change, and leadership do not act as resistance factors when implementing AI in project management. The participants identified themselves as open to change and technically skilled professionals who, thanks to their expertise and education, have realistic expectations of AI and do not perceive a risk of job loss as a result of its adoption. Furthermore, the positive attitudes of their colleagues toward AI, along with their role as early adopters who serve as idealized influencers of leaders supporting new technologies, contribute to a lower level of resistance to the integration of AI into project management. However, perceived transition costs, the relationship between employees and management, the integration of employees and management in the decision-making process, and organizational culture do act as resistance factors when implementing AI in project management. This is primarily due to the ambiguous value added by AI: while it facilitates routine tasks, it is not seen as sufficiently capable of fully handling such tasks without supervision. Resistance is also driven by insufficient management engagement and the low priority given to AI integration. Additionally, organizational cultures that prioritize stability and avoid risk—often reinforced by regulatory or internal constraints—further slow down AI implementation in project management and contribute to resistance. The results of this research expand knowledge about the use of AI in project management and can serve as a foundation for further academic studies. They also provide valuable insights for developing practical measures and strategies aimed at ensuring the effective and successful implementation of AI in organizations. As an enhancement to the thesis, the appendix (attachments) offers the reader an insight into the use of specific AI tools in project management, based on the analysis of secondary sources. Additionally, it presents findings on the competencies and roles of project managers in the AI era, derived from data collected during interviews.