| dc.description.abstract | The management consulting businesses’ of today maneuver through increasingly complex and dynamic landscapes, they encounter rising competition and innovation. The introduction of generative AI into management consulting offers a promising pathway to improve the effectiveness of consulting methods and increase innovation. This thesis investigates the multifaceted implications of deploying generative AI in management consulting, examining how management consultants use the technology today and investigating the perceived future of generative AI and management consulting. Through a combination of theoretical frameworks and empirical analysis, this study categorizes and examines the diverse methods through which these models are integrated into consulting practices. The results indicate that consultants primarily employ generative language models to optimize workflows, boost productivity, and streamline tasks like text summarization, data extraction, language refinement, and inspiration. Additionally, these models aid in managing extensive datasets, facilitating deeper business insights and innovative problem-solving. Despite their widespread use, generative language models are predominantly employed as a support function rather than a standalone solution, underscoring a widespread deficiency in proficiency and confidence among users. Furthermore, future projections indicate that generative language models will significantly transform the management consulting industry by augmenting consultants’ capabilities, enhancing problem solving processes, and delivering greater value to clients. While these tools will make consulting more accessible and efficient, the importance of human expertise, particularly in areas requiring social skills and nuanced knowledge, will remain essential. Finally, the thesis presents a framework derived from the empirical findings, termed the AI Proficiency and Confidence Grid. This framework is designed to illustrate the relationship between proficiency in generative technology and confidence in its use. In essence, it highlights the significance of understanding generative AI and incorporating appropriate training. | sv |
