2019· International Journal of Artificial Intelligence & Digital Transformation· 0 citations
TL;DR
This study proposes a Multimodal AI Framework for Decision Intelligence Systems that integrates diverse data sources to enhance prediction accuracy, contextual understanding, and operational efficiency and demonstrates that multimodal AI significantly outperforms traditional unimodal systems.
Abstract
Decision Intelligence (DI) combines Artificial Intelligence (AI), Machine Learning (ML), analytics, and domain expertise to improve organizational decision-making. However, the growing volume of multimodal data, including text, images, sensor data, and numerical information, presents significant challenges for conventional decision support systems. This study proposes a Multimodal AI Framework for Decision Intelligence Systems that integrates diverse data sources to enhance prediction accuracy, contextual understanding, and operational efficiency. The proposed architecture consists of four layers: data ingestion, multimodal processing, fusion intelligence, and decision orchestration. It employs Natural Language Processing (NLP), Computer Vision (CV), time-series analytics, and transformer-based fusion techniques to generate predictive insights, automated recommendations, and explainable decisions. The framework is applicable across healthcare, finance, manufacturing, retail, and intelligent governance. Performance evaluation demonstrates that multimodal AI significantly outperforms traditional unimodal systems by improving prediction accuracy, reducing decision latency, and enhancing contextual awareness. The proposed framework supports faster, more reliable, and explainable decision-making, providing a scalable solution for next-generation enterprise decision intelligence and data-driven strategic planning.
Artificial Intelligence (AI) has become a transformative technology for enhancing Intelligent Decision Support Systems (IDSS) by enabling accurate, adaptive, and data-driven decision-making across diverse computer science applications. This review examines the fundamental concepts of AI, its major techniques, including machine learning, deep learning, expert systems, fuzzy logic, reinforcement learning, explainable AI, and generative AI, and their roles in modern decision support systems. It further discusses emerging trends such as human-centered AI, edge AI, federated learning, digital twins, hybrid AI models, and responsible AI that are reshaping intelligent decision-making. The review also highlights the applications of AI-enabled IDSS in cybersecurity, software engineering, cloud computing, the Internet of Things, healthcare informatics, robotics, big data analytics, smart manufacturing, and education technologies. In addition, key challenges related to data quality, explainability, scalability, privacy, ethics, computational complexity, and user trust are critically discussed, followed by future research directions emphasizing foundation models, neuro-symbolic AI, Green AI, Quantum AI, AI governance, and autonomous decision intelligence. Overall, the review provides a comprehensive overview of recent advancements and identifies promising opportunities for developing trustworthy and efficient AI-driven intelligent decision support systems.
P. S· Journal of Intelligent Decis...· 0 citations
The proposed framework includes four stages: knowledge acquisition, data preprocessing, hybrid model integration, and predictive decision support, which improves prediction accuracy, reliability, transparency, and decision-making of next-generation intelligent systems.
Karen Lewis, Steven Young· International Journal of App...· 0 citations
This paper proposes the Foundation Model-Based Predictive Analytics Framework for Multi-Domain Decision Intelligence (FMPA-MDI), an integrated architecture that combines heterogeneous data acquisition, multimodal preprocessing, semantic representation learning, transformer-based predictive reasoning, retrieval-augmented learning, knowledge graph integration, explainable AI (XAI), and intelligent decision optimization.
Seshagiri N· International Journal of Mac...· 0 citations
This paper analyzes hybrid AI systems that combine symbolic approaches (rule-based reasoning, interpretability) with sub-symbolic methods (machine learning, neural networks) to improve flexibility and robustness and suggests future directions, including explainable AI and scalable distributed architectures.
Riyaz Mohammed, Pooja Agarwal· International Journal of Art...· 0 citations
Business Intelligence systems powered by Artificial Intelligence (AI) have demonstrated to be an innovative approach towards transforming organizational intelligence into valuable information for strategic decisions, maximum efficiency of operations, and constant innovations within organizations. Conventional Business Intelligence systems heavily utilize descriptive analytics based on historical reporting methods and dashboard-based visualizations, and as such, they feature limited predictive and prescriptive analytics capabilities. In this scenario, the current paper aims at providing the full conceptual framework of AI-based Business Intelligence, encompassing the various elements of the system, namely, data management, AI data analytics, automated insight generation, intelligent decision making, and innovation within the organization.This framework is based on machine learning, deep learning, natural language processing, predictive analytics, and anomaly detection technologies that help process structured and unstructured data from various sources including enterprise systems, customer databases, IoT devices, market information systems, and social media platforms. AI-based analytics can help in identifying trends that manifest themselves in business processes, performance problems, risks, and opportunities as well as in providing recommendations for decision making in real time.The framework promotes and boosts innovations through enhanced strategic planning, a focus on customers’ needs in product development, optimizations of operations including processes, and adaptability of business models. The findings indicate that business intelligence based on AI technology can help improve the quality of decisions in addition to fostering higher operational efficiency, agility of organizations, and securing compliance with the demands of modern competition. Nevertheless, one should keep in mind that challenges related to data quality, systems integration, data security, explainability, governance, and adequate readiness of organizations might hinder the implementation of this framework.The results highlight the need for AI to perform the role of an intelligent decision-making support tool which enhances human expertise without replacing it. Further studies should investigate the use of AI across various industries, use of Explainable Artificial Intelligence (XAI), Generative AI, AI governance frameworks and the validation of ideas proposed through machine learning experiments and organizational case studies. The suggested project gives a framework for companies in their attempts to achieve intelligent, flexible and innovation-oriented digital transformation using the AI-driven systems of Business Intelligence.
Virendra Gomase, Suhas B. Dhande, P. Natu et al.· Journal of Intelligent Decis...· 0 citations
Recent advancements in Artificial Intelligence (AI), machine learning, predictive analytics, and intelligent automation have transformed enterprise strategic planning. Traditional planning methods often relied on historical data, manual forecasting, and business intuition, resulting in delayed decisions and limited responsiveness. AI-driven Enterprise Intelligence Systems (EIS) address these challenges by integrating intelligent analytics, predictive forecasting, and real-time decision support into strategic planning processes. This research presents an AI-based Enterprise Intelligence framework that combines data integration, intelligent processing, predictive modeling, and strategic decision support. The system utilizes structured and unstructured data from ERP, CRM, supply chain, financial, and external market intelligence sources. Machine learning techniques, including regression models, neural networks, and ensemble algorithms, are employed to generate accurate business insights and forecasts. Performance evaluation demonstrates significant improvements, including a 35% increase in forecasting accuracy, a 42% reduction in decision-making time, a 38% improvement in operational efficiency, and a 40% enhancement in strategic planning effectiveness. The findings indicate that AI-driven Enterprise Intelligence Systems enable organizations to make informed decisions, optimize resources, respond rapidly to market changes, and achieve sustainable growth. The study concludes that AI-powered enterprise intelligence is a key enabler of future-ready strategic planning, competitive advantage, and digital transformation.
O. Dahl, K. Nygaard· International Journal of Art...· 0 citations
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