The Use of Artificial Intelligence in Business: Innovations, Applications and Impacts

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1163

Special Issue Editors


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Guest Editor
Center for Information and Communication Sciences, College of Communication, Information, Media, Ball State University, Muncie, IN, USA
Interests: artificial intelligence; decision-making; customer engagement; machine learning, generative AI; predictive analytics

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Guest Editor
Business Administration-International Studies and Business Administration Program, Glendon Campus, York University, Toronto, ON, Canada
Interests: business analytics; data literacy; artificial intelligence; organizational performance; AI-driven tools; social media technologies; decision-making; business settings
Center of Information and Communication Sciences, Ball State University, Muncie, IN, USA
Interests: security; IoT; AI; networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am writing to propose a Special Issue titled "The Use of Artificial Intelligence in Business: Innovations, Applications and Impacts". This Issue will explore the powerful and rapidly growing influence of AI technologies on how businesses operate, make decisions, engage with customers, and plan for the future. As AI continues to evolve, it is becoming an essential tool across industries—reshaping everything from supply chains to marketing strategies. We aim to bring together a collection of high-quality articles that will highlight both the opportunities and the challenges AI presents in the business world. Topics may include, but are not limited to, AI-powered business intelligence and predictive analytics for market trends, the automation of operations and logistics, machine learning for customer behavior insights, AI in financial forecasting and planning, and the ethical questions that arise when AI is integrated into corporate environments. We are also particularly interested in how generative AI is changing the way companies create content, design products, and communicate with consumers. By gathering diverse perspectives and practical case studies, this Special Issue will offer meaningful insights for researchers, professionals, and decision-makers navigating the fast-moving intersection of AI and business innovation.

Dr. Hesham Allam
Dr. Hossam Ali-Hassan
Dr. Firoz Khan
Guest Editors

Manuscript Submission Information

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Keywords

  • transformation
  • smart business solutions
  • predictive innovation
  • AI-driven strategy
  • machine learning insights
  • automated intelligence
  • generative AI applications
  • future of work
  • ethical AI in business
  • digital decision-making
  • intelligent enterprise

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Published Papers (1 paper)

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Research

28 pages, 3264 KB  
Article
A Unified Fuzzy–Explainable AI Framework (FAS-XAI) for Customer Service Value Prediction and Strategic Decision-Making
by Gabriel Marín Díaz
AI 2026, 7(1), 3; https://doi.org/10.3390/ai7010003 (registering DOI) - 22 Dec 2025
Abstract
Real-world decision-making often involves uncertainty, incomplete data, and the need to evaluate alternatives based on both quantitative and qualitative criteria. To address these challenges, this study presents FAS-XAI, a unified methodological framework that integrates fuzzy clustering and explainable artificial intelligence (XAI). FAS-XAI supports [...] Read more.
Real-world decision-making often involves uncertainty, incomplete data, and the need to evaluate alternatives based on both quantitative and qualitative criteria. To address these challenges, this study presents FAS-XAI, a unified methodological framework that integrates fuzzy clustering and explainable artificial intelligence (XAI). FAS-XAI supports interpretable, data-driven decision-making by combining three key components: fuzzy clustering to uncover latent behavioral profiles under ambiguity, supervised prediction models to estimate decision outcomes, and expert-guided interpretation to contextualize results and enhance transparency. The framework ensures both global and local interpretability through SHAP, LIME, and ELI5, placing human reasoning and transparency at the center of intelligent decision systems. To demonstrate its applicability, FAS-XAI is applied to a real-world B2B customer service dataset from a global ERP software distributor. Customer engagement is modeled using the RFID approach (Recency, Frequency, Importance, Duration), with Fuzzy C-Means employed to identify overlapping customer profiles and XGBoost models predicting attrition risk with explainable outputs. This case study illustrates the coherence, interpretability, and operational value of the FAS-XAI methodology in managing customer relationships and supporting strategic decision-making. Finally, the study reflects additional applications across education, physics, and industry, positioning FAS-XAI as a general-purpose, human-centered framework for transparent, explainable, and adaptive decision-making across domains. Full article
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