of cloud-based AI services (SAP Core: AI, SAP: Conversational AI, Oracle AI Services, AWS AI services), suited to the automation needs of organizations and businesses across different market segments. The question of the applicability of large language models (LLMs), generative pre-trained transformers (GPTs), and AI tools in general to large-scale automation for business use has been raised in recent years within the ERP and business management software (BMS) research and development communities [22; 23]. [PERSON], CEO and founder of the AI analytics platform Metanomic, has commented on the integration of LLMs and GPTs such as Google Bard and ChatGPT as enterprise-grade solutions for data analytics and customer service by ERP providers like SAP, Oracle, IBM, Salesforce, Infor, and NetSuite, outlining the importance of considering the trade-off between cost-effectiveness and competitive advantage. [PERSON] notes that the use of pre-trained models (Bard, ChatGPT) fine-tuned on customer data might give BMS and ERP providers a competitive advantage in terms of having cutting-edge AI technology; however, API charges for gaining and maintaining access to such models might prove more expensive in the long-term than the development and training of specialized proprietary models by the BMS and ERP providers [24]. Given that LLMs and GPTs are heavily data-driven technologies, bias in training data sets is a rising concern with respect to their application in critical sectors such as healthcare [25] and law enforcement [26], where data privacy and security are a necessity. The announcement of the introduction of generative AI within Google Workspace offers for businesses has sparked a debate on whether the benefits of having personalized AI tools outweigh the issues with information accuracy, copyright violation, and data security [27; 28; 29]. Despite the aforementioned concerns, research shows that LLMs and GPTs have been applied with some success in several business sectors [22]: finance and banking, healthcare, e-commerce, and digital marketing, with the focus of the applications again being data summarization and analytics as well as automated customer service solutions. OpenAI lists several successful ChatGPT business integration projects on their \"Customer Stories\" webpage [30], including GPT-4 for qualitative data analytics (Viable); GPT-3 for insight extraction based on customer feedback (Yabble); and GPT-4 for knowledge base structuring in wealth management (Morgan Stanley). The use of ChatGPT-4 for [30] image recognition and context generation in blind and low-vision communities (Be My Eyes, Virtual Volunteer project), language preservation (Government of Iceland), and customer support and personalized learning in educational platforms (Khan Academy, Duolingo), shows the transformative potential that such AI technologies can have. However, it is pertinent to note that LLMs and GPTs are not a replacement for human intelligence and critical thinking, but rather tools that can support more streamlined and personalized experiences in business [22], research [31], and educational [32] processes. With new AI tools continuously introduced as automation solutions for organizations and businesses across different market segments, an assessment of the current state of AI technology in management information systems (MIS) can provide valuable insight to researchers and developers. This systematic literature review aims at studying AI technology with respect to its implementation and adoption in MIS and focuses on identifying:  
* Existing opportunities and challenges in AI adoption at the organizational level. * Available tools and platforms for launching AI services. * The reported business value of integrated AI models. * Future research directions. The paper is structured in a popular literature review format. First, a brief theoretical introduction to AI in the context of MIS is presented. Second, relevant preceding literature reviews are considered, and their advantages and limitations are discussed.