Recent developments in the field of artificial intelligence (AI) have enabled new paradigms of machine processing, shifting from data-driven, discriminative AI tasks toward sophisticated, creative tasks through generative AI. Leveraging deep generative models, generative AI is capable of producing novel and realistic content across a broad spectrum (e.g., texts, images, or programming code) for various domains based on basic user prompts. In this article, we offer a comprehensive overview of the fundamentals of generative AI with its underpinning concepts and prospects. We provide a conceptual introduction to relevant terms and techniques, outline the inherent properties that constitute generative AI, and elaborate on the potentials and challenges. We underline the necessity for researchers and practitioners to comprehend the distinctive characteristics of generative artificial intelligence in order to harness its potential while mitigating its risks and to contribute to a principal understanding.

Generative Artificial Intelligence (GAI) is a prevalent topic in recent research and business, seemingly taking the position of a disruptive technology that has the potential to significantly transform industries ranging from productivity (e.g., ChatGPT-4) to creativity (e.g., DALL-E). While the emerging scientific discussion on GAI covers a variety of fields and issues, such as privacy, accuracy, and application scenarios, this paper sheds light on the business side of GAI by investigating the morphologic nature of start-ups and incumbents leveraging GAI. Based on the structured analysis of 100 real-world instances, we report on a taxonomy of GAI applications and services that advances our practical understanding, strengthens the distinguishability, as well as adds clarity to the discourse of GAI potentials. We provide an initial framework and five types of GAI, namely Generator, Reimaginator, Synthesizer, Assistant, and Enabler, that are informed by the core characteristics of the technology paradigm.

Context

With rapid technological advancements, artificial intelligence (AI) has become integral to various sectors. Generative AI (GenAI) tools like ChatGPT or GitHub Copilot, with their unique content creation capabilities, pose transformative potential in Software Engineering by offering new ways to optimize software development processes. However, the integration into current processes also presents challenges that require a sociotechnical analysis to effectively realize GenAI's potential.

Objective

This study investigates how GenAI can be leveraged in the domain of Software Engineering, exploring its action potentials and challenges to help businesses and developers optimize the adoption of this technology in their workflows.

Method

We performed a qualitative study and collected data from expert interviews with eighteen professionals working in Software Engineering-related roles. Data analysis followed the principles of Grounded Theory to analyze how GenAI supports developers' goals, aligns with organizational practices, and facilitates integration into existing routines.

Results

The findings demonstrate several opportunities of GenAI in Software Engineering to increase productivity in development teams. However, several key barriers were also identified, that should be accounted for in successful integrations. We synthesize the results in a grounded conceptual framework for GenAI adoption in Software Engineering.

Conclusions

This study contributes to the discourse on GenAI in Software Engineering by providing a conceptual framework that aids in understanding the opportunities and challenges of GenAI. It offers practical guidelines for businesses and developers to enhance GenAI integration and lays the groundwork for future research on its impact in software development.

The global talent shortage has become a universal challenge, prompting practitioners and researchers to explore digital innovations as potential solutions for acquiring the right talents. However, the role of emerging technologies like generative artificial intelligence (AI) in human resources (HR) remains largely uncharted territory. This article investigates generative AI's transformative potential to augment recruiters' daily operations. Through a qualitative interview study, we derive and illuminate the opportunities of generative AI within the recruitment domain, shedding light on its promising opportunities but also addressing inherent challenges. The findings of this study propose a theoretical model of generative AI in recruitment and how it empowers recruiters in their daily tasks to recruit tomorrow's talents.

As global challenges, such as pandemics, population growth and widespread illnesses, continue to rise, healthcare systems are facing greater strain, resulting in a shortage of resources and increased demands for medical care. Effective communication between healthcare professionals and patients is essential for the provision of good services to prevent confusion and induced anxiety of patients, particularly when medical jargon is employed and not understood. Generative AI (GAI) presents a chance to transform healthcare communication by providing language processing capabilities that enhance patient-centered services. This paper examines how GAI-based conversational agents for explaining medical jargon in healthcare should be designed. We derived eleven design principles from a systematic literature review and evaluated them with nine clinical cardiological scenarios through a prototypical instantiation of an LLM-based conversational agent. The results provide insights for researchers and healthcare providers in form of prescriptive design knowledge to improve patient communication using GAI.

Generative artificial intelligence (GenAI) has emerged as a transformative technology capable of creating novel content across multiple formats. Its rapid advancement fuels innovations and implications for various fields, including the unexplored domain of enterprise architecture. This study investigates the potential applications of generative AI in enterprise architecture through semi-structured expert interviews, analyzed using the theoretical lens of affordance theory. The research identifies four key affordances that generative AI offers enterprise architects: information research and synthesis, text generation and refinement, insight generation and decision support, and architectural content creation. The findings reveal that generative AI functions as a collaborative partner for enterprise architects, providing information and inspiration that, when combined with professional knowledge and expertise, enables the creation of high-quality and innovative enterprise architecture content. This technology shows promise in enhancing productivity, efficiency, and effectiveness in enterprise architecture practices.