AI-Driven Business Analytics Revolution
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 96
Special Issue Editor
Special Issue Information
Dear Colleagues,
We are pleased to invite you to submit your latest research to this Special Issue of Algorithms, “AI-Driven Business Analytics Revolution”. This Special Issue aims to explore the transformative potential of artificial intelligence (AI) techniques in reshaping business analytics, decision-making processes, and organizational performance across industries.
Recent advancements in machine learning, deep learning, natural language processing, and generative AI have enabled organizations to derive actionable insights from large-scale, complex, and heterogeneous data. As we enter a new era of data-centric decision making, this Special Issue seeks high-quality submissions that propose novel algorithms, theoretical frameworks, and empirical analyses at the intersection of AI and business analytics.
We welcome contributions that address foundational algorithmic developments, scalable AI pipelines for real-time analytics, and innovative AI applications that optimize business operations, enhance predictive capabilities, or enable autonomous decision systems. Interdisciplinary approaches that combine methods from computer science, operations research, economics, and data science are particularly encouraged.
Topics of interest include, but are not limited to, the following:
- AI algorithms for predictive, prescriptive, and adaptive analytics;
- Deep learning architectures for financial forecasting, customer modeling, or supply chain optimization;
- Generative AI applications for business intelligence and knowledge management;
- Multi-agent reinforcement learning in dynamic business environments;
- Causal inference and explainability in business decision systems;
- AI-enhanced simulation and digital twins for strategic planning;
- Federated learning and privacy-preserving AI in enterprise analytics;
- Real-time anomaly detection and risk management with AI;
- Ethical and governance challenges in deploying AI for business analytics;
- Hybrid human–AI collaboration models for decision augmentation.
We encourage both theoretical and applied contributions, including algorithm design, complexity analysis, experimental benchmarking, and real-world deployment case studies. Submissions should demonstrate scientific rigor, methodological clarity, and relevance to the ongoing transformation of business analytics through AI.
Dr. Maikel Leon
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 100 words) can be sent to the Editorial Office for announcement on this website.
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI-driven analytics
- business intelligence
- machine learning algorithms
- deep learning for business
- generative AI applications
- predictive analytics
- prescriptive decision-making
- reinforcement learning
- explainable AI
- digital transformation
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