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The Use of Artificial Intelligence in Business: Innovations, Applications and...
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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 July 2026 | Viewed by 4267

Special Issue Editors

Dr. Hesham Allam

E-Mail Website
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
Dr. Hossam Ali-Hassan

E-Mail Website
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
Dr. Firoz Khan

E-Mail Website
Guest Editor
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

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI 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

  • 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 1959 KB  
Article
LLM-Augmented Algorithmic Management: A Governance-Oriented Architecture for Explainable Organizational Decision Systems
by Nikolay Hinov and Maria Ivanova
AI 2026, 7(3), 102; https://doi.org/10.3390/ai7030102 - 10 Mar 2026
Viewed by 1165
Abstract
Algorithmic management systems increasingly coordinate work, allocate resources, and support decisions in corporate, public sector, and research environments. Yet many such systems remain opaque: they optimize and score effectively but struggle to communicate rationales that are contextual, auditable, and defensible under emerging governance [...] Read more.
Algorithmic management systems increasingly coordinate work, allocate resources, and support decisions in corporate, public sector, and research environments. Yet many such systems remain opaque: they optimize and score effectively but struggle to communicate rationales that are contextual, auditable, and defensible under emerging governance expectations. Large language models (LLMs) can help bridge this gap by translating quantitative signals into human-readable explanations and enabling interactive clarification. However, LLM integration also introduces new risks—hallucinated rationales, bias amplification, prompt-based security failures, and automation dependence—that must be governed rather than merely engineered. This article proposes a governance-oriented architecture for LLM-augmented algorithmic management. The model combines the following elements: an algorithmic decision core; an LLM-based cognitive interface for explanation and dialogue, and a verification and governance layer that enforces policy constraints, provenance, audit trails, and human-in-command oversight. The framework is developed through targeted conceptual synthesis and normative alignment with key governance instruments (e.g., the EU AI Act, GDPR, and ISO/IEC 42001). It is illustrated through cross-domain scenarios and complemented by a demonstrative synthetic-trace simulation that highlights transparency–latency trade-offs under verification controls. Using the demonstrative simulation (n = 120 decision events), the framework illustrates a mean baseline latency of 100.3 ms and a mean LLM-augmented latency of 115.8 ms (≈15.5% increase), a mean explanation validity proxy of 85.6%, and a simulated constraint-satisfaction rate of 94.2% (113/120 events), with failed cases routed to review. These values are presented as design-level indicators of operational plausibility and governance trade-offs, not empirical performance benchmarks or state-of-the-art comparisons. The paper contributes a conceptual and governance-oriented architectural blueprint for integrating generative AI into organisational decision systems without sacrificing accountability, compliance, or operational reliability. Full article
(This article belongs to the Special Issue The Use of Artificial Intelligence in Business: Innovations, Applications and Impacts)
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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 - 22 Dec 2025
Viewed by 1449
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
(This article belongs to the Special Issue The Use of Artificial Intelligence in Business: Innovations, Applications and Impacts)
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