Adaptive Multimodal Learning in Smart Enterprises: Improving Retention and Cross-Modal Generalization for Sustainable AI Systems

Abstract

The rapid advancement of multi-modal artificial intelligence (AI) has resulted in business intelligence and organizational decision-making has resulted in the creation of models that can process and integrate a variety of data, e.g., images, text, audio. These models however have been associated to be challenged in cases of continuous learning where low capacity to remember the previous knowledge and adjust to new tasks which limits their effectiveness in AI-driven decision support and adaptive business analytics systems. The framework in this paper is an Adaptive Multimodal Learning (AML) framework that is intended to improve retention and cross-modal generalization in continuous AI systems while supporting dynamic business intelligence and data-driven organizational learning. The suggested solution presents an active hybrid hyper-adaptation process which integrates memory-efficient replay and task-specific modulation layers to overcome catastrophic forgetting. Benchmark multimodal datasets of image-text learning and audio-visual learning were evaluated experimentally. From a managerial perspective, AML contributes to strategic adaptability and continuous improvement in enterprise-level AI applications. Findings indicate that the suggested framework yields a 9.6% increase in mean accuracy and a 31% decrease in the forgetting rate over traditional continual learning baselines like Elastic Weight Consolidation (EWC), Experience Replay (ER) and Progressive Neural Networks (PNN). Moreover, experiments in cross-modal transfer show that there is a significant enhancement in the generalization between the unseen combinations of modality, which validates an adaptive learning ability of the framework. In general, the paper offers a viable and scalable remedy to long-term retention of knowledge and enhancement of cross-modal reasoning in continuous multimodal AI systems, with potential applications in business intelligence, organizational decision-making, and digital transformation initiatives.