Booster or Barrier? Exploring Generative AI’s Impact on Management Consultants

Sammanfattning

Artificial Intelligence (AI) is increasingly influencing the way work is structured and executed across knowledge-intensive industries. With recent advancements in generative AI (GenAI), the technology is now able to perform tasks that not long ago relied on human intelligence. Characterized by its lack of standardized contexts, complex problem solving, and client-facing nature, management consulting is widely considered archetypical knowledge work. Despite this, there is a lack of up-to-date research on the impact of AI in management consulting, where GenAI in particular is set to fundamentally change the nature of work. The purpose of this thesis was to explore how the use of GenAI impacts the modern-day management consultant. As opposed to most previous research, which has either been conducted at an organizational level or through experiments in controlled environments, this study contributes with insights from an individual-level perspective in uncontrolled real-world settings. In this qualitative study, semi-structured interviews were conducted with six Swedish management consultants, reached through a combination of purposive and snowball sampling. The collected data was analyzed using thematic analysis, from which patterns and themes were developed which later served as the basis for the discussion and conclusion. The findings reveal that GenAI has had a significant impact on how management consultants perform their work, introducing both clear benefits and new challenges across different aspects of their work practices. The use of GenAI contributes to consultants’ work in two key ways: by automating routine tasks while augmenting human capabilities in more complex activities. However, as tasks become more complex and require deeper contextual understanding, GenAI often struggles to deliver relevant or accurate outputs. Trust is another issue, as consultants remain cautious towards GenAI, often double-checking outputs and avoiding to use it when managing sensitive data. Finally, this study suggests that while GenAI is likely to continue to automate certain aspects of management consultants’ work practices, it is unlikely to fully replace the human consultant in the foreseeable future. In the long term, however, potential issues such as over-reliance on GenAI tools and increased workload might offset the benefits of the technology. Adding to existing research, these findings provide further support for the idea that automation and augmentation are often combined in practice. The findings also align with recent field experiments of GenAI in knowledge work, which suggest that the technology can significantly improve productivity for some tasks, but that it can harm performance for tasks outside its abilities. In addition to these findings, this study provides deeper insights into the challenges that management consultants face when using GenAI tools in uncontrolled real-world settings.