2024· International Journal of Machine Learning and Predictive Analytics· 0 citations
TL;DR
This study presents a comprehensive framework that includes data acquisition, preprocessing, feature engineering, predictive modeling, evaluation, and decision support, concluding that predictive analytics is a key enabler of intelligent enterprises and future data-driven innovation.
Abstract
AI-powered predictive analytics has emerged as a critical tool for modern organizations, enabling data-driven decision-making and strategic planning through the analysis of large-scale structured and unstructured data. By integrating machine learning, deep learning, natural language processing, and optimization techniques, predictive analytics frameworks can identify patterns, forecast future outcomes, and reduce organizational risks. This study presents a comprehensive framework that includes data acquisition, preprocessing, feature engineering, predictive modeling, evaluation, and decision support. The framework emphasizes data quality, computational efficiency, algorithm selection, and model interpretability. Applications across finance, healthcare, manufacturing, retail, and supply chain management demonstrate the effectiveness of AI in improving forecasting accuracy and operational performance. Comparative analysis shows that AI-based models outperform traditional statistical methods in accuracy, adaptability, scalability, and decision support. The study also highlights the role of explainable AI in enhancing transparency and trust, concluding that predictive analytics is a key enabler of intelligent enterprises and future data-driven innovation.
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
It is concluded that AI-driven business intelligence frameworks represent a transformative approach to enterprise management by enabling organizations to anticipate future challenges, optimize strategic decisions, improve resource utilization, and create resilient business ecosystems capable of adapting effectively to rapidly evolving economic and technological environments.
Shi-Hu Gan· International journal of com...· 0 citations
In today's competitive business environment, organizations face the constant challenge of optimizing operational efficiency while managing limited resources. Traditional resource allocation methods often fall short in addressing the complexity and dynamic nature of modern operations. This paper explores the transformative role of Artificial Intelligence (AI) and Predictive Analytics in revolutionizing resource allocation processes. AI techniques, including machine learning and optimization algorithms, enable businesses to make data-driven decisions, while predictive analytics provides insights into future demand and resource needs. By integrating AI and predictive analytics, organizations can enhance decision-making accuracy, reduce costs, and improve overall operational efficiency. Through case studies and industry examples, this paper demonstrates the potential of these technologies to optimize resource allocation in various sectors, including manufacturing, healthcare, and logistics. The paper also discusses the challenges, ethical considerations, and future trends in leveraging AI and predictive analytics for operational optimization.
E. F. Novseen· International Journal of App...· 0 citations
The rapid adoption of Artificial Intelligence (AI) and business analytics is transforming organizational decision-making by enabling managers to utilize large volumes of data for timely and informed business decisions. This study examines the role of AI-driven business analytics in improving managerial decision-making and organizational performance. The study proposes an integrated framework in which AI-driven business analytics capability influences organizational performance through enhanced managerial decision-making effectiveness. The framework considers the ability of AI-enabled analytics to provide predictive insights, identify business patterns, support risk assessment, and improve the quality and speed of managerial decisions. A quantitative research approach is proposed, using a structured questionnaire to collect data from managers and executives working in organizations that utilize AI and business analytics. The collected data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the proposed relationships and mediation effects. The study is expected to demonstrate that effective utilization of AI-driven analytics can strengthen managerial decision-making and contribute to improved organizational outcomes. The study contributes to the emerging literature on AI-enabled management by linking analytical capabilities, managerial decision-making, and organizational performance within a unified framework. The findings are expected to provide practical guidance for organizations seeking to develop data-driven and AI-enabled decision-making capabilities.
Lakshmi Vasanthi Jampani· World Journal of Advanced Re...· 0 citations
AI-Based Decision Intelligence (DI) Platforms are transforming digital business operations by combining artificial intelligence, machine learning, predictive analytics, natural language processing, and business analytics to support intelligent decision-making. Unlike traditional Business Intelligence systems that mainly provide historical insights, DI platforms deliver predictive and prescriptive recommendations that improve decision accuracy, operational efficiency, business agility, and customer satisfaction. The proposed framework integrates data collection, intelligent analytics, predictive modeling, decision optimization, and continuous learning within a scalable cloud-based ecosystem. The study demonstrates that AI-driven decision intelligence significantly enhances organizational performance and supports successful digital transformation across various industries. Furthermore, emerging technologies such as Explainable AI, federated learning, autonomous decision systems, and ethical AI governance will further strengthen enterprise decision ecosystems. Overall, AI-Based Decision Intelligence Platforms serve as a critical enabler for sustainable growth, competitive advantage, and intelligent enterprise transformation in the digital era.
Vladimir Glushkov, V. Glushkov· International Journal of Art...· 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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
