Aleksandra Przegalinska

Aleksandra Przegalinska

Vice President at Kozminski University

    Aleksandra Przegalinska, Ph.D. is an Associate Professor and Vice-President at Kozminski University, and a Senior Research Associate at the Harvard Labour and Worklife Program. Aleksandra is the head of the Human-Machine Interaction Research Center at Kozminski University, and the Leader of the AI in Management Program. Until recently, she conducted post-doctoral research at the Center for Collective Intelligence at the Massachusetts Institute of Technology in Boston. She graduated from The New School for Social Research in New York. She is the co-author of Collaborative Society (The MIT Press), published together with Dariusz Jemielniak.

    All Sessions by Aleksandra Przegalinska

    Day 3 04/25/2024
    12:10 pm - 12:40 pm

    Optimizing Workplace with AI and Generative Bots

    <span class="etn-schedule-location"> <span class="firstfocus">Machine Learning</span> </span>

    This study investigates the interplay between artificial intelligence, human skills, and task characteristics, and their impact on organizational performance. Applying the Resource-Based View and Task Technology Fit theories, we explored how generative AI designed for collaboration, as both a firm resource and a capability, can enhance task execution across different dimensions - routine/creative tasks and easy/complex tasks. We conducted an experimental study involving the development of a marketing campaign with distinct subtasks reflecting these dimensions. Our findings show that firms can gain substantial benefits from integrating AI and that AI improves task outputs in automation, support, creation, and innovation. Our study also suggests a nuanced relationship between humans and AI in creative tasks with humans outperforming AI. The study highlights the value of upskilling and reskilling in AI, and proposes a strategic blend of AI and human creativity for optimal results. These findings have implications for understanding the role of AI in organizational tasks and formulating effective strategies for AI integration in business and beyond. Our exploration includes the innovative use of GPT models as decision-support tools, integrating diverse theoretical perspectives and a clear task division between humans and AI, to enhance both the efficiency and effectiveness of AI-human interactions in various decision-making contexts.

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