Artificial Intelligence and Data Analytics for Business Intelligence Systems

Dear Colleagues,

1. Special Issue Description and Introduction

Artificial Intelligence (AI) and data analytics are transforming business intelligence (BI) systems by enabling organizations to extract actionable insights from large, complex, and heterogeneous datasets. The integration of machine and deep learning, natural language processing, predictive analytics, big data technologies, and generative AI offers new opportunities to enhance decision-making, optimize business processes, improve organizational performance, and support innovation across diverse industries.

Despite these advances, several challenges remain, including data quality and integration, model interpretability, analytical reliability, privacy and security, ethical AI adoption, and the effective integration of intelligent technologies into existing business intelligence infrastructures. Addressing these challenges requires rigorous theoretical and empirical research, innovative computational methods, and practical solutions that bridge the gap between technological developments and business needs.

This Special Issue aims to provide an interdisciplinary platform for researchers and practitioners to present original research, methodological advances, and practical applications at the intersection of AI, data analytics, and business intelligence systems. Particular attention will be given to innovative approaches that improve analytical accuracy, decision support, operational efficiency, strategic planning, and organizational competitiveness. Contributions addressing emerging technologies, real-world implementations, responsible AI, and future directions in intelligent business analytics are especially welcome.

2. Aim of the Special Issue

This Special Issue primarily aims to advance research and practice in the integration of artificial intelligence and data analytics into business intelligence systems. We seeks to explore innovative methodologies, computational frameworks, analytical models, and application-oriented solutions that enable organizations to transform data into reliable, meaningful, and actionable business insights.

The Special Issue encourages interdisciplinary contributions that combine computer science, information systems, data science, business analytics, management, and decision sciences. It will also examine the opportunities and challenges associated with deploying intelligent BI systems in real-world organizational environments.

3. Objectives

The specific objectives of this Special Issue are as follows:

• Explore emerging AI techniques and data analytics methodologies for developing advanced business intelligence systems.
• Investigate predictive, prescriptive, and descriptive analytics approaches for supporting data-driven business decisions.
• Examine applications of machine learning, deep learning, generative AI, and large language models in business intelligence.
• Develop and evaluate intelligent decision-support systems for strategic, tactical, and operational decision-making.
• Investigate big data architectures, data engineering, data integration, and real-time analytics for intelligent BI environments.
• Address challenges related to data quality, explainability, fairness, privacy, security, trustworthiness, and responsible AI.
• Present empirical studies, experimental evaluations, case studies, and practical implementations demonstrating the effectiveness of AI-enabled BI solutions.
• Identify emerging research directions, methodological limitations, and opportunities for future innovation in intelligent business analytics.

4. Scope and Suggested Topics

The following suggested areas of interest are not intended to be exhaustive. Contributions addressing related themes at the intersection of AI, data analytics, and business intelligence are also welcome.

• Theme 1: Artificial Intelligence for Business Intelligence
  • AI-driven business intelligence architectures and frameworks.
  • Machine and deep learning for business decision support.
  • Generative AI and large language models in BI systems.
  • Natural language interfaces and conversational business intelligence.
  • Intelligent dashboards, automated reporting, and augmented analytics.
  • Knowledge graphs and semantic technologies for business intelligence.
  • AI-based decision automation and recommendation systems.
  • Hybrid AI approaches combining knowledge-based and data-driven methods.
• Theme 2: Data Analytics and Predictive Intelligence
  • Predictive and prescriptive business analytics.
  • Advanced statistical learning and forecasting methods.
  • Time-series analysis and demand forecasting.
  • Customer behavior analytics and customer segmentation.
  • Sentiment analysis and social media intelligence.
  • Business process analytics and process mining.
  • Anomaly detection, fraud analytics, and risk prediction.
  • Causal inference and data-driven decision optimization.
• Theme 3: Big Data and Intelligent Data Management
  • Big data analytics architectures for enterprise BI.
  • Data integration, interoperability, and data governance.
  • Data quality assessment and improvement.
  • Real-time and streaming analytics.
  • Cloud-based and edge-enabled business intelligence.
  • Data lakes, data warehouses, and lakehouse architectures.
  • Distributed analytics and scalable computing frameworks.
  • Synthetic data generation and data augmentation for business applications.
• Theme 4: Intelligent Decision-Support Systems
  • AI-enabled strategic and operational decision-making.
  • Multi-criteria decision analysis and intelligent optimization.
  • Prescriptive analytics for resource allocation.
  • Decision intelligence and human–AI collaboration.
  • Uncertainty-aware and risk-sensitive decision support.
  • Reinforcement learning for business optimization.
  • Explainable decision-support systems.
  • AI-assisted scenario analysis and strategic planning.
• Theme 5: Business Applications and Industry-Specific Solutions
  • AI-driven financial analytics and business forecasting.
  • Intelligent supply chain and logistics management.
  • Retail analytics and personalized customer experiences.
  • Marketing intelligence and customer relationship management.
  • Human resource analytics and workforce planning.
  • Healthcare management analytics and operational intelligence.
  • Manufacturing intelligence and predictive maintenance.
  • Smart cities, sustainability analytics, and environmental intelligence.
  • E-commerce analytics and digital business transformation.
  • Small and medium-sized enterprise intelligence systems.
• Theme 6: Trustworthy, Explainable, and Responsible AI
  • Explainable AI (XAI) for business intelligence.
  • Fairness, accountability, and transparency in analytical models.
  • Privacy-preserving data analytics.
  • Federated learning for distributed business environments.
  • Cybersecurity and adversarial robustness of BI systems.
  • Ethical considerations in AI-supported business decisions.
  • Governance, compliance, and responsible AI adoption.
  • Human oversight and trust in automated decision-making.
• Theme 7: Evaluation, Implementation, and Emerging Directions
  • Benchmark datasets and evaluation frameworks for intelligent BI.
  • Comparative evaluation of AI and analytics methods.
  • Empirical investigations of AI adoption in organizations.
  • Case studies and industrial implementations.
  • Performance, scalability, and computational efficiency.
  • Digital twins and simulation-based business intelligence.
  • Multimodal analytics for complex business data.
  • Agentic AI and autonomous business analytics workflows.
  • Integration of AI into enterprise information systems.
  • Research challenges and future directions in AI-enabled BI.

5. Types of Contributions Invited

The Special Issue welcomes original and scientifically rigorous contributions, including the following:

• Original research articles: Novel theoretical contributions, algorithms, models, architectures, and empirical investigations.
• Methodological papers: New analytical techniques, computational frameworks, optimization approaches, and evaluation methodologies.
• Application-oriented studies: Practical implementations supported by appropriate experimental or empirical evidence.
• Case studies: Well-documented investigations demonstrating the application and evaluation of intelligent BI systems in real-world settings.
• Review articles: Systematic literature reviews, evidence-based surveys, and critical reviews that identify research gaps and future directions.
• All submissions should clearly articulate their contribution to the field, demonstrate methodological rigor, and provide appropriate validation or supporting evidence.

6. Call for Research Contributions

Researchers, academics, data scientists, information systems specialists, and industry practitioners are invited to submit original manuscripts addressing recent advances in artificial intelligence, data analytics, and business intelligence systems.

The Special Issue seeks contributions that advance the state of the art through novel methodologies, rigorous empirical investigations, innovative applications, and critical evaluations of existing approaches. Submissions should demonstrate clear research objectives, appropriate methodological design, reliable evaluation, and meaningful implications for research or practice.

Interdisciplinary research combining AI techniques with business analytics, information systems, organizational decision-making, and digital transformation is particularly encouraged. Studies addressing practical challenges, reproducibility, responsible AI, and the translation of advanced analytical methods into effective business solutions are also welcome.

Manuscripts must be original, must not be under consideration for publication elsewhere, and must comply with the journal's publication ethics, authorship, and submission requirements.

Potential contributions should address one or more of the suggested topics while making a clear and substantial contribution to the Special Issue's overall theme.

Prof. Dr. Saad Yaseen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

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