Business and Generative Artificial Intelligence: Literature Review

Abstract

Objective: In the current landscape—characterized by rapid transformations and increasing global competition—the advancements achieved by OpenAI, Google, and Meta in the field of Generative Artificial Intelligence (GAI) stand out due to their remarkable capacity to autonomously create original content independent of human intervention. These technologies are capable of generating text, images, music, stories, and other similar outputs by processing vast volumes of data collected from the Internet. GAI models are typically categorized based on the type of content they produce—such as language, image, or video. These models not only generate responses but also interpret, learn, and adapt to new contexts. Additionally, multimodal models have emerged that integrate and process information from both textual and visual inputs. Thus, various sectors of the economy have begun to implement Generative Artificial Intelligence (GAI) tools. Among the most prominent are ChatGPT, I-JEPA, Stability AI, MidJourney, DALL·E 3, and Painter; these are GAI platforms capable of generating text and text-to-image content with a scriptwriter-like approach, storing visual data, and producing new images from stored content, as well as creating digital artworks based on textual descriptions or photographs. Other more specialized models, such as PaLM2, Claude, Copilot, Gemini, Llama-2, Bing Chatbot, and Prometheus, are used primarily for application development and more precise, secure information retrieval. Companies are leveraging these technologies to detect anomalies and simulate operational scenarios, allowing them to redefine key performance indicators (KPIs), explore multiple solution pathways, enhance decision-making processes, and respond swiftly and flexibly to market dynamics. Moreover, GAI is employed to optimize pricing strategies, forecast sales, calculate optimal inventory levels, and automate order processing. From a business perspective, evidence indicates that the adoption of Generative Artificial Intelligence (GAI) is influenced by various factors that shape organizational decision-making. According to Gupta (2024), three fundamental stages can be identified: a) The pre-perception and perception stage (or primary appraisal), during which the belief that GAI may be beneficial is shaped by factors such as social pressure, prior experience in the business and technological domains, system quality, availability of training and support, and the perceived ease of interaction with the technology; b) The evaluation stage (or secondary appraisal), in which entrepreneurs assess the costs and benefits associated with implementing GAI, evaluating its potential to solve specific business challenges and its ease of use; and c) The outcome stage, where the intentions to adopt change—both on the part of the business owners and their customers—play a critical role. It is at this point that the entrepreneur decides whether to adopt GAI or continue relying on traditional methods. The accelerated growth of technology has driven companies of various sectors, sizes, and types to implement—or consider implementing—Generative Artificial Intelligence (GAI). Therefore, the research question posed was: Why are companies adopting GAI in their businesses? In line with this, the objective of this study was to describe the reasons behind the adoption of GAI by companies.

Materials and methods: A systematic literature review was conducted following the PRISMA methodology, utilizing the databases Scopus, ScienceDirect, EBSCOhost, and SciELO for the period 2020–2024. Inclusion criteria comprised peer-reviewed articles published in English, Spanish, or Portuguese that followed the IMRaD structure and contained a DOI. Documents published in other languages, reviews, editorials, and duplicates were excluded. A total of 51 articles met the inclusion criteria.

Results: The 51 selected articles were analyzed. Of these, 50.98% (26 articles) were sourced from the EBSCOhost database, 27.45% (14 articles) from Scopus, 13.73% (7 articles) from SciELO, and 7.84% (4 articles) from ScienceDirect. In terms of temporal distribution, 20 articles (39.22%) were published in 2023, while 31 articles (60.78%) were published in 2024. The findings revealed that GAI is being adopted across multisector companies due to its ability to increase operational efficiency, foster creativity and innovation, and streamline business operations. GAI enables the autonomous production of high-quality content—including emails, scientific articles, products, and services—thus generating competitive advantages and enabling the emergence of new business models.

Conclusion: Numerous researchers concur that Generative Artificial Intelligence (GAI) has become an indispensable tool for companies across all sectors and sizes, primarily due to its capacity to enhance operational efficiency, foster creativity, and drive innovation. GAI facilitates the automation of processes such as email drafting, article generation, coding, and the development of innovative products and services, thereby strengthening companies’ competitive advantages and even enabling the emergence of entirely new business models. Its widespread adoption is largely attributed to the speed and utility of its outputs, which have earned the trust of users and have led to an average projected revenue of USD 449 per user through the implementation of AI-powered chatbots. Beyond enhancing creative productivity, GAI contributes to the development of new skills, fostering the formation of multidimensional professionals. It improves critical thinking and decision-making, facilitates data acquisition and analysis, strengthens planning capabilities, and enables the establishment of clear goals. GAI simplifies complex tasks, generates forecasts across diverse scenarios, and personalizes the customer experience by delivering preference-based recommendations. Furthermore, it optimizes real-time pricing strategies, enhances user retention, and collectively contributes to elevating business competitiveness. These findings are expected to serve as a solid foundation for researchers, professionals, and entrepreneurs interested in exploring the benefits of implementing GAI as a tool to enhance productivity, promote innovation, and improve business efficiency—outcomes that may prove critical in guiding strategic decision-making. Nevertheless, this study encountered several limitations. A substantial portion of the reviewed scientific literature was concentrated in the medical and educational industries. Additionally, the vast majority of the selected articles were published in 2023 and 2024, revealing a notable scarcity of research focused specifically on the corporate sector.