Titlebook: Embedding Artificial Intelligence into ERP Software ; A Conceptual View on Siar Sarferaz Book 2024 The Editor(s) (if applicable) and The Au

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https://doi.org/10.1007/978-3-7091-6339-9aries according to the various vendors. In order to address this issue, we abstract from the various ERP products and propose a reference architecture. Thus, we can systematically incorporate our suggested artificial intelligence concepts into the ERP reference architecture and guarantee their valid

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Virtual-Channel Flow Control and Buffering, identified two types of technology provisioning in the context of ERP systems. There are artificial intelligence technologies that are embedded into the database system and application server of ERP systems. For scalability reasons, artificial intelligence infrastructure (e.g., GPU computing) is al

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Jeffrey J. Walker,Edward N. Brody,Larry Goldes is the added value resulting from this elaboration. Artificial intelligence must be deeply integrated into business processes and user interfaces of the underlying ERP systems in order to gain benefit for users. Typical ERP users are business experts with nearly no knowledge about data science or

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Achlesh Daverey,Kannan Pakshirajangence, new artifacts and processes are introduced that must be considered from the life cycle management perspective. For example, artificial intelligence models must be trained, deployed, activated, and monitored. Those aspects are not covered by classic life cycle management tools and concepts. ER

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Colonized Selves, Decolonizing Ontologies,legal requirements. Thus, processing of artificial intelligence must be compliant with data protection and data privacy legislations. For example, the training job must only consider personal data for which consent is given. Compliance must be ensured for all aspects of General Data Protection Regul

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Microbes for Legume Improvementintelligence scenarios. However, customers or partners might require adopting this artificial intelligence-related content to their specific needs based on extensibility. This might be also necessary for use cases ERP customers or partners develop by themselves. Thus, technical solution is required,

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Microbes for Legume Improvementpower of artificial intelligence models decreases due to changes in the data environment. Determining this time point and triggering retraining is the objective of model degradation. However, this is a challenging task as in addition to statistical techniques, feedback of the users is required and a