The study identifies six latent topics of GenAI application, including object detection and identification; medical applications; intelligent conversational agents; image generation and processing; financial and information security applications; and cyber-physical systems.
Authors
Shiwangi Singh, Indian Institute of Management Ranchi, Jharkhand, India.
Surabhi Singh, Assistant Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
Sascha Kraus, Free University of Bozen-Bolzano, Faculty of Economics & Management, Piazza Università 1, Bolzano, Italy; University of Johannesburg, Department of Business Management, Johannesburg, South Africa.
Anuj Sharma, Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
Sanjay Dhir, Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India.
Summary
This study aims to identify generative AI (GenAI) applications and develop a roadmap for the near, mid, and far future. Structural topic modelling (STM) is used to discover latent semantic patterns and identify the key application areas from a text corpus comprising 2,398 patents published between 2017 and 2023.
The study identifies six latent topics of GenAI application, including object detection and identification; medical applications; intelligent conversational agents; image generation and processing; financial and information security applications; and cyber-physical systems.
Emergent topic terms are listed for each topic, and inter-topic correlations are explored to understand the thematic structures and summarize the semantic relationships among GenAI application areas. Finally, a technology roadmap is developed for each identified application area for the near, mid, and far future. This study provides valuable insights into the evolving GenAI landscape and helps practitioners make strategic business decisions based on the GenAI roadmap
Published in: Journal of Innovation and Knowledge
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