Generative artificial intelligence

Banh, Leonardo; Strobel, Gero Article — Published Version Generative artificial intelligence Electronic Markets Provided in Cooperation with: Springer Nature Suggested Citation: Banh, Leonardo; Strobel, Gero (2023) : Generative artificial intelligence, Electronic Markets, ISSN 1422-8890, Springer, Berlin, Heidelberg, Vol. 33, Iss. 1, https://doi.org/10.1007/s12525-023-00680-1 This Version is available at: https://hdl.handle.net/10419/306304 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) 1 3 Electronic Markets (2023) 33:63 https://doi.org/10.1007/s12525-023-00680-1 FUNDAMENTALS Generative artificial intelligence LeonardoBanh1 · GeroStrobel1 Received: 26 June 2023 / Accepted: 7 November 2023 / Published online: 6 December 2023 © The Author(s) 2023 Abstract 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. Keywords Generative AI· Artificial intelligence· Deep learning· Deep generative models· Large language models JEL Classification C8· M21 Introduction “A groundbreaking fusion of data-driven creativity and artificial intelligence, poised to redefine the boundaries of innovation and transform the future of digital landscapes.” -ChatGPT on GPT-4, Mar 23 Version, on Generative AI In an era where applications like ChatGPT set records for the fastest-growing user base by demonstrating unprecedented domain-independent expertise (Hu, 2023), the concept of “Generative Artificial Intelligence” (GAI) emerges as a disruptor in the digital landscape (Dwivedi etal., 2023; Teubner etal., 2023; Wessel etal., 2023). With capabilities to generate high-quality, contextually relevant content almost indistinguishable from human-created work, discussions arise on whether this new technology even holds early signs of artificial general intelligence (Bubeck etal., 2023; The Washington Post, 2022). Regardless of discussions on AI’s sentiency, the latest advancements in machine learning (ML) and deep learning (DL) have extended traditional, data-driven AI tasks such as predictions, classifications, or recommendations toward the generation of unique, realistic, and creative content. Prevalent collaborations between humans and intelligent systems in businesses and private life have been shaped by the adoption of AI in various ways, especially in the field of process optimization and decisionmaking (Brynjolfsson & McAfee, 2016; Burström etal., 2021; Moussawi etal., 2021). GAI addresses this development by providing novel augmentation and automation prospects in form of innovative services and business models (Huang & Grady, 2022; Mondal etal., 2023), e.g., by assisting customer support employees with suggestions of appropriate conversation responses (Brynjolfsson etal., 2023). The fast-changing and continuously evolving landscape of GAI calls for an extensive conceptualization of the properties and capabilities to fathom the phenomenon (Dwivedi etal., 2023; Strobel etal., 2024). Responsible Editor: Christian Matt * Leonardo Banh [email protected] Gero Strobel ger[email protected] 1 University ofDuisburg-Essen, Universitätsstraße 2, Essen45141, Germany Electronic Markets (2023) 33:63 1 3 63 Page 2 of 17 While GAI research and development is continuing to invest toward better, faster, and more capable models (e.g., Microsoft, 2023), studies on the fundamental principles, applications, and socio-economic impact remain largely unexplored in the academic discourse (Strobel etal., 2024; Susarla etal., 2023; Wessel etal., 2023). GAI provides innovation opportunities for various domains (e.g., networked businesses and digital platforms) but also comes with challenges (e.g., transparency, biases, and misuse) that need to be addressed for successful implementations (Houde etal., 2020; Schramowski etal., 2022; van Slyke etal., 2023). However, an examination of the key concepts is yet to be conducted, leaving a clear image and understanding of generative AI undefined. To overcome that shortcoming, this article provides an introduction to the fundamentals of generative AI, with its concepts, applications, and challenges. To do so, we exploratively synthesize recent literature on the technical foundation leading toward generative AI in combination with contemporary empirical examples of generative AI models and applications. Our aim is to conceptualize the key properties of GAI and differentiate them from ML and DL methods, to foster the understanding of the theoretical foundations of generative AI, and to guide further endeavors in examining as well as designing generative AI-based systems. The remainder of the article is structured as follows: In the next section, we conceptualize generative AI and provide a distinction between related AI methods as well as outline the technological foundations. Afterward, we elaborate on the prospects and applications of the generative AI value chain and examine the impact of different generation modalities. Then, we address the potential challenges of adopting generative AI before concluding with a brief research outlook. Conceptualization The field of artificial intelligence (AI) has taken a prominent place in research and practice across various disciplines for the past decades. Especially in information systems (IS) research, the socio-technical impact of AI as a phenomenon is at the core of investigation (Ågerfalk etal., 2022; Berente etal., 2021). These technologies have transformed the way we interact with data and make decisions, leading to uncharted ways in society as well as economy (Fügener etal., 2021; J. Li etal., 2021; van den Broek etal., 2021). However, as we venture into the new era of generative AI, it becomes increasingly crucial to understand the core concepts and distinctions within GAI as a rapidly evolving technology. To differentiate GAI from other AI concepts and provide a fundamental conceptualization, we will present a brief overview of AI and its subfields, machine learning (ML) and deep learning (DL), in the next section (Fig.1). Afterward, we continue to elaborate on how DL has driven the development of deep generative models to enable distinct GAI characteristics and capabilities, ultimately leading to an even broader array of AI applications and opportunities for various fields. From artificial intelligence todeep learning Artificial intelligence is considered an umbrella term, spanning over different computational algorithms capable of performing tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, and learning from experience (Castelvecchi, 2016; Winston, 1993). Early AI systems, such as expert systems and knowledge bases, were rule based and aimed at supporting users and businesses in decision-making Fig. 1 Generative AI and other AI concepts (inspired by Goodfellow etal., 2016, p.9; Janiesch etal., 2021, p.687) Artificial Intelligence Machine Learning Deep Learning Generative AI e.g., expert systems, knowledge bases, … e.g., support vector machines, decision trees, k-nearest neighbors, … e.g., neural networks, convolutional neural networks, … e.g., large language models, generative adversarial networks, variational autoencoders, latent diffusion models, … Electronic Markets (2023) 33:63 1 3 Page 3 of 17 63 (Harmon, 1985; Patterson, 1990). Machine learning as a subfield of AI deals with the development of algorithms capable to autonomously solve tasks through exposure to data without being explicitly programmed—i.e., learning (Brynjolfsson & Mitchell, 2017). In the realm of ML, there are several types of learning approaches based on the nature of the data and the desired outcome. Supervised learning is a common approach, e.g., for applications in commercial contexts, as algorithms are trained on labeled datasets to classify or forecast (business) data (Janiesch etal., 2021). The algorithm learns to map inputs to outputs and, thus, is capable of making predictions on new, unseen data. Moreover, unsupervised learning (i.e., discovering hidden structures or patterns within unlabeled data) and reinforcement learning (i.e., learning optimal decision-making by interacting with an environment and maximizing cumulative rewards over time through trial and error) are further learning strategies in ML (Kühl etal., 2022). What ML algorithms share in common are their discriminative properties, i.e., the goal of processing data to conduct classification, regression, or cluster and determine decision boundaries. Exemplary algorithms include decision trees, k-nearest neighbors, or support vector machines (Ray, 2019). Deep learning is a more advanced subset of ML and leverages artificial neural networks to model complex data representations and automatically detect correlations and patterns in large datasets (Janiesch etal., 2021; Samtani etal., 2023). Neural networks are computational models inspired by the structure and function of the human brain, consisting of interconnected layers of artificial neurons (Goodfellow etal., 2016). In DL, neural networks comprise of multiple hidden layers in a nested architecture to learn hierarchical feature representations from the data, leading to improved performance on various tasks. Thus, DL is capable of processing high-dimensional data in various domains, ranging from one-dimensional data like signals and texts to multidimensional data such as images, video, or audio (LeCun etal., 2015). These advances have enabled a plethora of use cases across different domains, from societal good, such as improving healthcare and environmental sustainability (Piccialli etal., 2021; Schoormann etal., 2023; Strobel etal., 2023), to electronic markets, where DL can optimize pricing, serve as recommendation systems, forecast demands, and detect fake consumer reviews (Ferreira etal., 2016; M. Li etal., 2022; Zhang etal., 2023b). Toward generative AI Fueled by advancements in DL techniques, deep generative models (DGMs) have emerged as a class of DL models to generate new content based on existing data, creating a variety of new possibilities for AI applications (Lehmann & Buschek, 2020; Tomczak, 2022). These models are trained to understand complex data distributions, which allows them to produce outputs that closely resemble real-world data. By leveraging statistics, the goal of DGM training is to learn high-dimensional probability distributions from a finite training dataset and create new, similar samples that resemble an approximation to the underlying class of training data (Ruthotto & Haber, 2021). While discriminative models focus on modeling the relationship between input features and output labels, generative models learn the inherent data structure and generation processes (Jebara, 2004). Generative models have been around for decades, with, for example, hidden Markov models or Bayesian networks aiming to model statistical problems involving time series or sequences (Gm etal., 2020). Nonetheless, DGMs relying on neural networks have paved the way for significantly higher-quality generated content in recent advancements in the field of so-called generative AI. Thus, the goals of DGMs differ from traditional discriminative AI models (e.g., in ML) because the focus lies on the probabilistic generation of new data instead of determining extant data’s decision boundaries (e.g., classification, regression, or clustering) (Tomczak, 2022; Weisz etal., 2023). In the following, we will focus on DGMs as the underpinning of GAI and give an overview of four core DGMs that have shaped the evolution of GAI in Table1. To leverage DGMs in GAI applications, they can be trained to generate new data and enable a variety of use cases (we refer to DGMs implemented in GAI applications as GAI models). Training a GAI model can be different than a discriminative AI model due to semi-supervised learning, a combination of learning techniques leveraging a small amount of labeled data (i.e., supervised) followed by extensive unlabeled data (i.e., unsupervised) (Kingma etal., 2014). For instance, recent GAI models apply techniques like supervised fine-tuning (SFT), reward models, and reinforcement learning via proximal policy optimization (PPO) to achieve an alignment of the model with the developers’ intentions and values (OpenAI, 2023; Ouyang etal., 2022). This unique approach allows the training of very large datasets required for GAI models without the need for difficult complete labeling. The application system functions as an interface for the user to interact with a GAI model. Prompting is an interaction technique and unique GAI property that enables end users using natural language to engage with and instruct GAI application (e.g., LLMs) to create desired output such as text, images, or other types (Dang etal., 2022; Liu & Chilton, 2022). Depending on the application, prompts vary in their modality and directly influence the mode of operation. For instance, text-to-image applications use textual prompts describing the visuals of the desired image, while imageto-image applications rely on an input image to steer the generation process. Electronic Markets (2023) 33:63 1 3 63 Page 4 of 17 By design, the outputs of generative AI models are probabilistic and not replicable compared to the deterministic outcomes of discriminative AI—i.e., variance (Weisz etal., 2023). For one exact input prompt, a GAI application will generate varying outputs each time it is prompted, but the results remain valid and prompt fulfilling. On the other hand, different input prompts can lead to the same goal. Hence, formulating a meaningful prompt that leads to the desired outcome is based on trial-and-error process, e.g., by rephrasing textual prompts with the same keywords. The field of prompt engineering deals with systematically constructing prompts to improve the generated outputs (Liu & Chilton, 2022). Based on the heuristic approach of prompt engineering and the inherent variance in the generated content, GAI users continuously and iteratively specify their desired tasks as input prompts to generate outputs until their task is solved. The primary goal of generating new, probabilistically produced data (i.e., content) with varying outputs based on the same input distinguishes generative AI from discriminative AI, which pursues boundary determination by analyzing data and making a decision (see Fig.2). Hence, a primary difference lies in the role of data, as GAI leverages very large datasets in its generative model to produce diverse content, while discriminative AI processes user data based on a (pretrained) algorithm. Prospects andapplications ofgenerative AI Complementing discriminative AI, GAI has recently emerged as a novel tool with a wide range of new possibilities impacting multiple sectors, from education and healthcare (Brand etal., 2023; Burger etal., 2023; Cooper, 2023) Table 1 Overview of core deep generative models Deep generative model Description Generative adversarial network (GAN) Generative adversarial networks consist of two competing neural networks: a generator and a discriminator (Goodfellow etal., 2020). The generator creates realistic data samples, while the discriminator distinguishes between real and generated samples (Pan etal., 2019). Both neural networks are trained together until the discriminator is not able to differentiate both samples (Janiesch etal., 2021). This adversarial competition results in the generator improving its data generation capabilities over time, eventually producing high-quality, realistic outputs. Hence, GANs find various applications, for instance, in image generation and manipulation, object detection and segmentation, and natural language processing (Aggarwal etal., 2021; Gui etal., 2023) Variational autoencoder (VAE) Variational autoencoders employ a neural network to learn encoding compressed input data into a lower-dimensional latent space and then decode the data by reconstructing the original data from the latent space representation (Kingma etal., 2014). By optimizing a variational lower bound on the data likelihood in a probabilistic approach, VAEs can generate new samples that resemble the original data distribution. Typical use cases for VAEs can be seen in the synthetic generation and reconstruction of data such as images, in anomaly detection, and recommendation systems (Wei & Mahmood, 2021) Transformer Transformer models have become the basis for many state-of-the-art natural language processing tasks and succeeding models. They are a specific type of neural network architecture that employ selfattention mechanisms to capture long-range dependencies in the data, making them well-suited for large-scale language modeling tasks (Vaswani etal., 2017) Generative pre-trained transformers (GPT) build on the transformer architecture and were trained with large datasets of unlabeled data (Brown etal., 2020). Due to their large size (i.e., a very large number of trainable parameters), GPT trained on text data are often referred to as large language models (LLMs) (Schramowski etal., 2022). The goal of LLMs is to generate novel, coherent, contextually relevant human-like text by predicting which token is most likely to occur after the prior tokens in a sentence (Brown etal., 2020; H. Li, 2022). Hence, LLMs can serve as the foundation for conversational AI tools like ChatGPT (Teubner etal., 2023). Besides conversing, the large amount of information stored in LLMs can be used for text generation, writing, or even programming, e.g., to support scholars (Cooper, 2023; Lund etal., 2023) Latent diffusion model (LDM) Latent diffusion models are transformer based and build on the concepts of denoising score matching and contrastive divergence to learn a stochastic data generation process (Rombach etal., 2022). In LDMs, the generation process starts with a simple initial distribution, such as Gaussian noise. Then, the data gets gradually refined through a series of noise-reduction steps following a predefined diffusion process through a latent space (Ho etal., 2020). The key advantage of LDMs is their ability to learn complex data distributions without requiring adversarial training (as in GANs) or optimizing variational lower bounds (as in VAEs). They also feature improved stability over other DGMs during training to be less prone to issues like mode collapse (Kodali etal., 2017; Rombach etal., 2022), making them well-suited for high-quality and detailed outputs, such as high-resolution image synthesis (Ho etal., 2020) Electronic Markets (2023) 33:63 1 3 Page 5 of 17 63 to networked businesses (Dwivedi etal., 2023; Wessel etal., 2023). These emerging applications inherit the generativity and variance properties of GAI and, therefore, are capable of producing unique and creative content, going beyond mere assistance. Hence, GAI becomes increasingly multidisciplinary, enabling disruptive innovations and automating even traditionally creative tasks, e.g., by generating customized contextual texts or images, facilitating new opportunities for businesses to innovate and differentiate themselves in the competitive economic landscape (Dwivedi etal., 2021; Lund etal., 2023; Pavlik, 2023). Generative AI finds its utility across various modalities, including the generation of text, image, video, code, sound, and other produced content, such as molecules or 3D renderings (see Table2). For example, GAI applications aim to create tailored marketing content, generate realistic (product) images or videos, and even assist in software development by generating code (Bakpayev etal., 2022; Elasri etal., 2022; Kowalczyk etal., 2023). Several modalities can serve as the input for GAI models. Distinguishing the different modality types, unimodal models generate the same output type as their input type, e.g., text-to-text or imageto-image generation, whereas multi-modal models combine different input and output types, for instance, in a text-toimage or code-to-text scenario. Different multi-modal models can subsume as x-to-modality models (e.g., x-to-text or x-to-image). Examining the architecture of GAI-based systems, three major component layers can be identified: model layer, connection layer, and application layer (see Fig.3). These parts embed generative AI in its information systems context and draw a boundary from external entities that can be interacted with its environment (e.g., users, organizations) and data (i.e., public and enterprise data) (Samtani etal., 2023). Inside the boundaries of GAI-based systems, the prevalent characteristics of generativity and variance persist and affect all layers and processes. The model layer comprises the pre-trained, deployable GAI artifact (i.e., a DGM) for application systems. Depending on the training dataset, general purpose models aim at solving a wide range of tasks in multiple domains (e.g., GPT-4), whereas customized models are designed for domain-specific tasks and were, therefore, trained on highly specific data (e.g., CodeBERT). Integrating these models into a system environment that affects people and organizations leads to the application layer of generative AI. By providing a proper context for the artifact, users are able to leverage the capabilities of GAI models for a specific application use case. Observing the trend of various recently emerging GAI applications building on top of existing models, it becomes apparent to further distinguish between end-to-end applications that are based on undisclosed, proprietary models (e.g., Midjourney) and open applications that are built around open-source models or leverage publicly accessible pre-trained models (e.g., Jasper and Elicit using OpenAI’s GPT-3 (Elicit, 2022; Jasper, 2022)). Huang and Grady (2022) describe GAI applications as a “UI layer and ‘little brain’ [i.e., application layer] that sits on top of the ‘big brain’ that is the large general-purpose models [i.e., model layer].” This perspective emphasizes that new business models and applications can be developed without the need to train large GAI models from scratch by leveraging publicly available application programming interfaces (API) or AI-as-a-service platforms (Burström etal., 2021; Janiesch etal., 2021; Lins etal., 2021). Indeed, the accessibility and availability of pre-trained GAI models foster value co-creation and can be leveraged via a connection layer (e.g., Hugging Face). Fully integrated GAI systems, on the other hand, employ their own, custom-trained proprietary models. In many cases, end-to-end GAI applications represent fully integrated systems (e.g., GitHub Copilot), while open GAI applications leverage external models via APIs (e.g., Stable Diffusion). Overall, GAI models may aim for general purposes or customized tasks regardless their connection or application characteristics (see Fig.3). To enrich GAI-based systems with additional data beyond their training state and the GAI boundary, external data sources Generate Discriminative Model Prompt Generative Model Data Decision Creation Variance Generativity Content Boundary Determination Specify Generative AI Discriminative AI Fig. 2 Procedural differences of generative AI and discriminative AI Electronic Markets (2023) 33:63 1 3 63 Page 6 of 17 can be connected. Enterprise data (e.g., internal documents, enterprise resource planning (ERP) systems, knowledge bases) and public data (e.g., the Internet, libraries, social media) may serve as complementary, contextual data that GAI applications can further draw upon for more relevant and personalized results. Employing GAI in enterprises can extend the level of assistance for workers and open up opportunities for augmentation and automation of the job, leading to new forms of collaborations between humans and machines (Einola & Khoreva, 2023). Furthermore, GAI transforms the way businesses operate in their daily tasks, innovate, and interact Table 2 Overview of different output modalities for generative AI applications Modality Description Text X-to-text applications are centered around text generation and natural language processing. The goal is to generate human-like written text that fits the user’s input prompt by providing a meaningful answer within the context. For instance, chatbots like OpenAI’s ChatGPT imitate textual conversations with the user and can be guided to output text artifacts as desired (OpenAI, 2023). Furthermore, text-producing applications can be leveraged for content creation (e.g., copywriting or specific writing in e-commerce contexts) (Bakpayev etal., 2022; Brand etal., 2023). Moreover, text generation can support processes in sales or support by providing the ability to produce customized texts tailored toward the requests (Mondal etal., 2023). Systems integrating GAI models with further knowledge bases (e.g., enterprise data and Internet access) extend the available information beyond the model’s initial training dataset Image X-to-image applications generate images based on the user’s prompting. Relying on GANs or diffusion models as DGMs, synthetic images are created that find use cases in marketing, design and fashion, or creative fields in the form of new visual art (Haase etal., 2023; Mayahi & Vidrih, 2022; Zhang etal., 2023a). For instance, Stable Diffusion is an open-sourced x-to-image model that enables the generation of images in multiple GAI applications (Rombach etal., 2022). Moreover, generated synthetic images can act as training data for further ML models to train classifiers (e.g., medical images to detect diseases (Ali etal., 2023)). Besides a text-to-image creation process, image editing capabilities are possible, e.g., via image-to-image systems that manipulate and extend images according to the user’s prompting (Oppenlaender, 2022) Video X-to-video applications deal with the creation of synthetic videos, i.e., dynamic motion images. New video clips are generated by describing the content of the desired video footage (text-to-video) or applying the style and composition via text or image prompt to a source video (video-to-video) (Esser etal., 2023). These prospects allow the fast and convenient creation and editing of videos via natural language and other modalities (Zhan etal., 2021). Thus, not only videographers benefit from x-to-video applications but also people without filming and editing skills are enabled to creatively express themselves due to an accessible creation process (Anantrasirichai & Bull, 2022). Besides recreational and entertainment purposes, x-to-video GAI models find application in sales and marketing (e.g., product marketing videos), onboarding and education (e.g., virtual avatars in training videos), or in customer support (e.g., how-to videos) (Leiker etal., 2023; Mayahi & Vidrih, 2022). As an exemplary application, Synthesia is a video creation platform specialized in generating professional videos with virtual avatars and synthetic voiceovers (Synthesia, 2023) Code In the realm of software development, x-to-code GAI applications offer transformative potential in how developers work and code by providing x-to-text capabilities specific to programming languages. Models like CodeBERT (Feng etal., 2020) or GraphCodeBERT (Guo etal., 2021) were trained on programming code to generate source code from natural language or modeling languages for new software programs. Several x-to-text models also offer coding capabilities because general-purpose LLMs are trained with increasingly large datasets that contain code (e.g., Stability.ai, 2023). Programmers using applications such as GitHub Copilot are supported by automatically written chunks of code, ideas converted into actionable scripts, auto-completion functions, generated unit tests, duplicate code detection, and bug fixing (Sun etal., 2022). These automation potentials allow developers to focus on higher-level tasks and problem solving, enhancing their productivity and the final product’s overall quality, reducing time-to-market, supporting rapid prototyping, and promoting continuous innovation for the product and business Audio X-to-audio applications focus on audio content generation and comprise, for instance, the generation of speech with synthetically generated human-like voices (Borsos etal., 2022; Wang etal., 2023). Especially text-to-speech and speech-to-speech models are being heavily researched and can be used to power various applications, ranging from digital assistants and customer services to audiobook and training narration and accessibility tools (Moussawi etal., 2021; Qiu & Benbasat, 2005). GAI models like Microsoft’s VALL-E (Wang etal., 2023) offer a more personalized and engaging user experience by enabling realistic voice modeling. Moreover, x-to-sound models find application in music creation. By specifying genres or melodies via prompts, unique pieces of music can be generated that respect the original intent (Agostinelli etal., 2023). GAI models such as MusicLM (Agostinelli etal., 2023) help musicians in their creative process, offering inspiration and aiding the composition of complex pieces. Businesses in the music industry can leverage high-fidelity music generation to create customized soundtracks for marketing, movies, or video games, significantly reducing the cost and time associated with traditional music production (Anantrasirichai & Bull, 2022; Weng & Chen, 2020) Other The applications of GAI extend beyond the stated modality types and domains, impacting multiple other, specific areas. For instance, x-to-molecules models like AlphaFold (Jumper etal., 2021) and OpenBioML (Murphy & Thomas, 2023) generate viable protein structures and design new molecules by generating valid, novel molecular structures, supporting drug discovery and bioengineering researchers (Walters & Murcko, 2020). 3D modeling is also impacted by GAI applications such as DreamFusion (Poole etal., 2023), Nvidia GET3D (Gao etal., 2022), and Point-E (Nichol etal., 2022), which generate realistic and complex 3D models that facilitate a range of applications from product design and architecture to virtual reality and game development Electronic Markets (2023) 33:63 1 3 Page 7 of 17 63 with their customers (Brynjolfsson etal., 2023; Mondal etal., 2023). Thus, the prospects of value co-creation come in hand with potential changes in human work roles, requiring workforces in various domains to adapt their tasks as a diverse set of tasks could be impacted by generative AI (Brynjolfsson & McAfee, 2016; Eloundou etal., 2023). The ongoing diffusion of AI into businesses gets accelerated by GAI applications, resulting in a possible replacement of human jobs on the one hand but also the creation of new jobs (e.g., for prompt engineers or with new business models) on the other hand (Einola & Khoreva, 2023). Hence, the effect on the labor market by the disruption needs to be discussed, and businesses should seek to understand and embrace the potential of generative AI (Eloundou etal., 2023; Willcocks, 2020). Challenges forgenerative AI‑based systems While generative AI holds transformative potential for individuals, organizations, and society due to its vast possible application space, the technology also inherits various challenges that parallel those of traditional ML and DL systems. The domain of electronic markets is a prime example that moved into the center of transformation due to its latest focus on data-driven efforts (Selz, 2020). Outlining and emphasizing these challenges relevant for research and practice helps to raise awareness of the constraints as well as supports future efforts in developing, implementing, and improving GAI-based systems. Bias Because of GAI’s data-driven nature, data quality plays an essential role in how GAI-based systems perform and, thus, how feasible their adoption for real-world scenarios in business contexts is. Similar to their traditional discriminative AI relatives, GAI models are prone to bias causing biased decisions, disadvantages, and discriminations (Ferrara, 2023; Schramowski etal., 2022). Biases manifest in different ways and evolve primarily during two development phases of an AI-based system: training and inference. Data bias gets injected during the model’s training phase and leads to biased results because of faulty datasets. Factors such as non-representative, imbalanced sampling, incorrect labeling, and mismeasured features during the selection and processing of datasets hinder an unbiased training of the GAI model, ultimately leading to biased algorithmic outcomes (Mehrabi etal., 2022; Ntoutsi etal., 2020). The development of large-scale training datasets is especially Fig. 3 Conceptual framework of generative AI Application Layer Model Layer Connection Layer Environment Generative AI Data Knowledge Base InternetLibraries Public Data Enterprise Data API Open General Purpose Fully-Integrated End-To-End Customized ERP Social Media Documents Electronic Markets (2023) 33:63 1 3 63 Page 8 of 17 important for GAI models and often involves strategies of scraping public-available data on the Internet (Schuhmann etal., 2022). This approach is usually performed unsupervised and autonomously, which complicates the dataset’s quality assurance because of its large quantity of unstructured data. Since GAI models are often trained to be general-purpose and multi-modal, they require and rely even more on such training datasets. Hence, moderating potential data bias is crucial for applications in business contexts like electronic markets due to the closeness to customers (e.g., points of contact via advertisements, social media, or customer support). Furthermore, social bias as a form of data bias can cause distorted views in generated texts or images and should be considered as well as mitigated (Baeza-Yates, 2018). Algorithmic bias is introduced during the inference phase, independent from the model’s training dataset (Mehrabi etal., 2022). In this case, the models have been trained on diverse, unbiased input data, and either the model’s algorithm or the application around it introduces biases affecting users. Overfitting is a typical phenomenon that originates from the chosen learning strategies or optimization functions and causes biased algorithmic outcomes (Danks & London, 2017; Hooker, 2021). In this case, GAI models might introduce biases not reflected in the data because they fail to learn the data distribution correctly. Likewise, the presentation of and the user interaction with GAI-based systems can cause biases, such as when only selected generated content (e.g., one image out of multiple variants) is shown to the user (Baeza-Yates, 2018). Thus, generative AI applications exerting biased results influence users’ opinions and judgement and require control mechanisms (Jakesch etal., 2023a). Strategies should be developed to prevent, detect, and mitigate biases in order to safeguard users and ensure the service quality and reputations of a company. One approach to steer the quality of outputs from GAI models is via reinforcement learning from human feedback (RLHF) (Christiano etal., 2017; Griffith etal., 2013). The technique involves feedback from human evaluators to guide the model’s training process, with evaluators assessing and comparing the quality of generated outputs. This approach enables generative models to refine their output generation process, aiming for better alignment with human expectations and objectives. Nevertheless, determining what content is “good” or “right” remains a difficult and bias-prone task (Teubner etal., 2023). Transparency The need for explainability arises with the unpredictability of the inherent generative nature of GAI models and the overall functionality of ML models as “black boxes” (Janiesch etal., 2021; Meske etal., 2022). While the impact of GAI may not reach as far as discriminative AI use cases (e.g., decision-making or dynamic pricing), research on explainable generative AI is still in its infancy, and the justification for more transparency is without a doubt (Brasse etal., 2023; Sun etal., 2022). Governments are already discussing the enforcement of AI regulations that include explainable AI to protect the general society and mitigate risks tied to the technology (Hamon etal., 2020). Interpretability (i.e., the human capability to understand the AI system’s processes and decisions) is key, especially for GAI-based systems employed in large-scale information systems that affect large user groups, such as in networked businesses and digital platforms. In these cases, generated content has the potential to impact individuals and society, for instance, when generative AI serves as an advisor based on user questions and provides unsophisticated answers that are difficult to verify. Inaccuracy in generated product recommendations can have varying consequences depending on the situation, ranging from selecting the wrong product to taking the wrong medication. Early studies have shown how the chatbot ChatGPT performed surprisingly well in medical exams, suggesting inherent knowledge similar to medical students (Bhayana etal., 2023; Gilson etal., 2023). However, the seemingly omniscient capabilities may be restricted because, in the case of the medical exams, the GAI model might have been trained on the exam data and can reproduce its answers but is not able to comprehend the contextual state of an individual relevant for medical assessment. Therefore, understanding how the system performs sensemaking and generates its data helps users and businesses to achieve their goals responsibly and effectively, satisfying stakeholders’ needs and expectations (Miller, 2019; Sun etal., 2022). Particularly for autonomous systems in critical business applications that interact with human beings, supervision and explainability of the GAIgenerated content remain vital to ensure reliable, safe, and trustworthy outputs (Brasse etal., 2023; Hamm etal., 2023). Another angle of transparency concerns the debate between open-source and closed-source models. Legal issues revolving around copyright, licenses, and intellectual property make it difficult for individuals and enterprises to deploy GAI-based systems, especially when the large training data of closed-sourced GAI models is procured through Internet scraping (Jin etal., 2023; Smits & Borghuis, 2022). Research initiatives revolving around open-source datasets (e.g., Schuhmann etal., 2022) and open-source models (e.g., Stability.ai, 2023) aim at increasing the transparency on data provenance and highlight, for instance, the data sources as well as the presence of watermarks on images (Schuhmann etal., 2022). 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