Multimodal AI-Driven Decision Support Framework for Strategic Management Optimization

With this recent acceleration in relation to data generation process from a variety of sources, mainly textual, visual, auditory and from sensors domain, there is an increasing need for effective decision-making framework in the strategic management environment. We, therefore, propose in this research to create a multimodal machine learning-based AI AS decision support system (DSS) using machine learning (ML), natural language processing and computer vision, and a knowledge graph as an assistant to inform or support an organization's decision making. The framework generalizes the heterogeneous data to usable data by using the novel deep learning architectures (e.g. transformer for text, convolutional neural network for image and even multimodal fusion, etc.) to generate assemblies of heterogeneous data. In the current situation, OpenAI's annotated Language Model or its GPT 3, for example, and TensorFlow or PyTorch from Google, are being used to offer the conventional checks, for example, for scalability, accuracy and model versatility. As one reason for referring to this one must understand, that on the one hand the situation information may be of biblical complexity, but decisions in the name of business development are made executive at the other hand, so that an at least considered closing stone is raised for a sustainable business development.