Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
The growing complexity of modern business environments has significantly increased the demand for intelligent decision-support systems capable of transforming large volumes of organizational data into meaningful strategic insights. Conventional business intelligence approaches, which primarily emphasize descriptive reporting and historical performance analysis, often lack the capability to anticipate future business conditions and support proactive managerial decision-making. This study explores the development and application of Artificial Intelligence (AI)–driven business intelligence frameworks designed to strengthen predictive enterprise decision-making across diverse organizational functions. The research examines how AI technologies, including machine learning, deep learning, natural language processing, and intelligent data mining, can be integrated with business intelligence platforms to generate predictive insights from structured and unstructured enterprise data. The proposed framework emphasizes the systematic integration of data acquisition, preprocessing, predictive analytics, visualization, and automated decision support to improve strategic planning, operational efficiency, financial forecasting, customer relationship management, risk assessment, and resource allocation. The study further investigates the role of cloud computing, big data ecosystems, real-time analytics, and intelligent dashboards in enabling organizations to monitor business performance continuously while identifying emerging trends, anomalies, and growth opportunities. Particular attention is given to the ability of AI-enhanced business intelligence systems to support evidence-based decisions through adaptive learning models that continuously refine predictive accuracy as new organizational data become available. The research also considers implementation challenges associated with data quality, interoperability among enterprise information systems, algorithm transparency, cybersecurity, governance, workforce readiness, and ethical considerations surrounding automated decision processes. The findings indicate that organizations adopting AI-driven business intelligence frameworks achieve measurable improvements in forecasting accuracy, operational responsiveness, decision consistency, customer satisfaction, and organizational resilience compared with enterprises relying solely on conventional analytical methods. Furthermore, the study demonstrates that the effectiveness of predictive enterprise decision-making depends not only on technological sophistication but also on strategic alignment, high-quality data governance, interdisciplinary collaboration, and continuous organizational learning. By integrating artificial intelligence with advanced business intelligence capabilities, enterprises can transition from reactive management practices to proactive, predictive, and data-informed decision-making processes that enhance competitiveness and long-term sustainability. The study concludes 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.
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
The study concludes that AI is not replacing managerial judgment but augmenting human decision-making through intelligent data-driven insights, and organizations that strategically embrace responsible AI adoption while investing in digital capabilities and ethical governance are likely to achieve sustainable competitive advantage.
Peter Stone· Research Journal in Business...· 0 citations
Artificial Intelligence (AI) driven Decision Support Systems (DSS) help transform the field of strategic management through rapid, accurate and data-driven decision-making in increasingly complicated business environments. This article is centered on a comprehensive machine learning approach for AI-enabled strategic decision support involving all aspects of data acquisition, predictive analytics models, intelligent recommendation systems and collaboration between humans and AI. The framework suggested consists in the integration of data coming from the enterprise, customer relationship management systems, financial databases and market intelligence services and results obtained through machine learning technologies. The process of decision-making involves both the utilization of recommendation systems to develop possible strategies based on analytical results and human involvement ensuring that the outcome is ethical, clear and considerate. The research shows that AI-enabled DSS helps to enhance decision-making quality, operational efficiency, competitive edge, innovation capabilities, and organizational agility as a result of continuous learning. In addition, the paper argues that explainable AI, data governance, organizational preparedness, and responsible AI governance are important factors for successful implementation. Despite numerous advantages available, there are some challenges connected with data quality, transparency of algorithms, data confidentiality, safety, and managerial acceptance that should also be taken into account. The conceptual framework proposed emphasizes a focus on effective cooperation of sophisticated AI technologies with rational judgments that assist decision-makers while staying accountable and trustworthy. The results imply that AI-enabled decision support systems can be essential for businesses striving to adopt digitalization, ensure sustainability, and maintain long-term competitiveness in the context of intelligent organizations. Further studies should investigate target industries, Explainable AI (XAI), ethical frameworks of governance, and the integration of new technologies such as Digital Twins, Generative AI, and autonomous decision-making systems.
Virendra S. Gomase, Suhas B. Dhande, Pankaj R. Natu· International journal of com...· 0 citations
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.
Pooja Agarwal, Rakesh Chandra· International Journal of Mac...· 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
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
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