Generative AI

TL;DR

This paper conceptualizes generative AI within socio-technical systems, discusses its applications, limitations, and unique challenges in information systems, and proposes future research directions for Business & Information Systems Engineering.

Contribution

It offers a novel perspective on generative AI in socio-technical contexts and highlights specific opportunities and challenges for BISE research.

Findings

Generative AI is transforming communication and work practices.

Current limitations include ethical, technical, and societal challenges.

The paper suggests impactful research directions for BISE community.

Abstract

The term "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. The widespread diffusion of this technology with examples such as Dall-E 2, GPT-4, and Copilot is currently revolutionizing the way we work and communicate with each other. In this article, we provide a conceptualization of generative AI as an entity in socio-technical systems and provide examples of models, systems, and applications. Based on that, we introduce limitations of current generative AI and provide an agenda for Business & Information Systems Engineering (BISE) research. Different from previous works, we focus on generative AI in the context of information systems, and, to this end, we discuss several opportunities and challenges that are unique to the BISE community and make suggestions for impactful directions for BISE research.