WorkDifferentWithAI.com Academic Paper Alert!
Written by Emir Kučević, Constantin von Brackel-Schmidt, Tom Lewandowski, Stephan Leible, Lucas Memmert, Tilo Böhmann
Category: “AI for IT”
Article Section: Advanced AI Topics; Generative AI Applications
Publication Date: 2024-01-02
SEO Description: “Implementing generative AI for value co-creation and knowledge enhancement in enterprise settings.”
AI-Generated Paper Summary
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The article discusses the concept of Generative Artificial Intelligence (GenAI), an emerging technology that researchers and practitioners find valuable in promoting innovation, resolving problems, and optimizing day-to-day tasks and organizational processes. Despite its potential, there are knowledge gaps about the use and exploration of GenAI for creating value in novel ways. This issue is attributed to the limited understanding and experience most have with these new AI-based information systems. To counteract this problem, authors conducted a design science research project, creating a tool known as Prompt-a-thon (PaT). This tool offers a format for hands-on experience and encourages human-AI co-creation. When tested, results showed that most participants’ knowledge about GenAI use increased and they were satisfied with the PaT format. Therefore, this tool becomes a replicable format that enables value co-creation within the dynamic field of GenAI for both research and practice.
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Here is a summary of the key points and implications from the second paper:
Main Points:
- The paper proposes a new human-AI co-creation format called “Prompt-a-thon” (PaT) inspired by hackathons. The goal is to explore and utilize generative AI models like ChatGPT for value co-creation and problem solving.
- The PaT format has 5 phases: preparation, intro, execution, retro, and analysis. It brings together diverse participants to collaborate with AI on challenges, exchange learnings, and collect data.
- The format was tested through 5 real-world instantiations with over 120 participants in total. Surveys and interviews found it increased AI literacy and enabled value co-creation.
Implications:
- Provides a novel, structured format for human-AI collaboration that is transferable across contexts. Can serve as a framework for further research on generative AI.
- Enables hands-on experience with AI for practitioners and citizens. Promotes innovation, problem solving, networking. Increases awareness and skills.
- Combines interests of diverse stakeholders (researchers, practitioners, citizens) for mutual benefit through participatory formats like citizen science.
- Demonstrates the potential of generative AI to enhance capabilities and accelerate outcomes in time-bounded collaborative settings.
- Highlights complexities of human-AI collaboration, like managing expectations of AI capabilities. Provides insights into prompt engineering.
- Opens up design research agenda around human-AI formats for value co-creation. Areas like fostering agency without control need exploration.
- Overall, contributes both an actionable format and descriptive insights to guide effective and ethical integration of emerging generative AI systems.
Generative artificial intelligence, value co-creation, Prompt-a-thon, design science research, exaptation approaches
Author’s Abstract
Generative artificial intelligence (GenAI) has emerged as a valuable technology for researchers and practitioners to promote innovation, problem-solving, and optimization of everyday tasks and organizational processes. Despite considerable technological hype, notable knowledge deficits exist regarding the exploration and utilization of GenAI to co-create value for exaptation approaches. Limited experience and understanding of these novel artificial intelligence-based information systems hinder their exploitation. To address this, we conducted a design science research (DSR) project to build an artifact that allows researchers and practitioners to facilitate hands-on experience. To this end, we propose a human-AI co-creation format called Prompt-a-thon (PaT), which was applied in five different instances. The results of the format evaluations of these instances show that most participants increased their knowledge regarding GenAI usage while being satisfied with the PaT format. We thus contribute a replicable format enabling value co-creation for research and practice in the highly dynamic field of GenAI.