Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

In response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios.

The adoption of Artificial Intelligence (AI) in the meat industry signifies a transformative shift toward intelligent, secure, and sustainable food production systems. This review paper explores how AI is enhancing operations across the entire meat supply chain—including processing, quality assessment, spoilage identification, regulatory compliance, and logistics optimization. Conventional methods, often limited by human bias, workforce shortages, and inefficiencies, are being replaced by AI‐powered solutions such as machine learning, computer vision, hyperspectral imaging, smart sensors, and edge computing, enabling real‐time, non‐invasive, and data‐centric decision‐making. Beyond process automation, AI facilitates predictive safety controls, smart packaging technologies, and customized consumer engagement. Applications like robotic meat processing, cloud‐based traceability platforms, and smartphone‐integrated spoilage sensors highlight AI's contribution to precision meat processing. In addition, AI promotes clean‐label formulation, operational energy savings, and automated hygiene management via intelligent Cleaning‐in‐Place (CIP) systems. Global implementations—ranging from carcass grading systems in Australia and blockchain traceability in Europe to biosensor deployment in Asian markets—demonstrate the adaptability and impact of AI‐based solutions. Although barriers such as high implementation costs, lack of algorithmic transparency, and regulatory conservatism remain, the integration of AI with the Internet of Things (IoT), blockchain technology, and explainable AI (XAI) offers a promising pathway. This technological convergence is set to enhance food safety, boost consumer confidence, expand export potential, and redefine industrial competitiveness in the evolving post‐pandemic marketplace.

Enterprise Resource Planning (ERP) systems play a pivotal role in streamlining business operations and improving decision-making in high-stakes industries such as healthcare, finance, and manufacturing. With the rise of Artificial Intelligence (AI), ERP solutions have undergone a paradigm shift, offering enhanced capabilities for real-time data analysis, predictive analytics, and process automation. This paper explores AI's transformative impact on ERP, with a specific focus on strategies for seamless Software-as-a-Service (SaaS) implementation in industries with critical operational demands. The integration of AI into ERP systems not only optimizes resource utilization but also mitigates risks associated with manual data handling and fragmented workflows. Key challenges in implementing SaaS-based AI-driven ERP solutions include data security, interoperability, scalability, and organizational resistance to change. This study presents a comprehensive framework to address these challenges, emphasizing AI-enabled data migration, adaptive learning algorithms, and robust cybersecurity measures tailored for high-stakes environments. Additionally, it highlights the importance of stakeholder engagement, training programs, and iterative implementation strategies to ensure smooth adoption and maximize ROI. Case studies from healthcare and manufacturing sectors illustrate successful AI-SaaS ERP adoption, showcasing significant improvements in supply chain optimization, financial forecasting, and regulatory compliance. The role of predictive analytics in anticipating operational bottlenecks and machine learning in automating repetitive tasks is emphasized, demonstrating tangible outcomes such as cost reduction, enhanced decision-making, and operational efficiency. This paper concludes by outlining future trends, including the integration of generative AI for custom ERP module development, AI-driven self-healing systems for real-time troubleshooting, and the use of natural language processing (NLP) for intuitive user interfaces. These advancements are poised to redefine ERP systems, empowering enterprises to navigate complex challenges in high-stakes industries with greater agility and precision.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

The logistics and facility management industries are currently experiencing a paradigm shift driven by the rapid advancement of Artificial Intelligence (AI) and blockchain technology. These innovative technologies are poised to revolutionize various aspects of logistics operations, particularly in the context of sea cargo handling, where the demands for efficiency, security, and transparency are increasingly critical. As global trade continues to expand and the complexity of supply chains grows, the need for more advanced and reliable systems becomes ever more pressing. This research paper delves into the transformative potential of integrating AI and blockchain technologies within logistics and facility management, with a focused lens on sea cargo handling services. Sea cargo handling, a vital component of international trade, has traditionally relied on manual processes and centralized systems that often struggle with inefficiencies, delays, and security vulnerabilities. The integration of AI and blockchain offers a robust solution to these challenges by enhancing operational efficiency through automation and predictive analytics, while simultaneously ensuring the security and transparency of transactions through the decentralized and immutable nature of blockchain. AI's ability to process vast amounts of data in real-time allows for improved decision-making, predictive maintenance, and the optimization of resource allocation. Blockchain, on the other hand, provides a secure, tamper-proof ledger that ensures the authenticity and integrity of cargo movements, from origin to destination, reducing the risk of fraud and enhancing compliance with international regulations. This paper presents a comprehensive exploration of the integration of AI and blockchain in the logistics sector, specifically within the realm of sea cargo handling. The study begins with an extensive literature review that examines the current state of AI and blockchain technologies in logistics and facility management, highlighting both the potential benefits and the existing challenges. The review covers various applications of AI, such as machine learning algorithms for demand forecasting and route optimization, and explores how blockchain can be used to create transparent and secure supply chains. It also addresses the synergy between these technologies, proposing a combined approach that leverages the strengths of both AI and blockchain to create a more resilient and efficient logistics framework. Following the literature review, the paper outlines the methodologies employed to integrate AI and blockchain into sea cargo handling operations. This includes the development of AI models for optimizing cargo handling processes, predicting port congestion, and automating customs clearance procedures. The methodologies also cover the implementation of blockchain for tracking the provenance of goods, verifying transactions, and ensuring that all cargo movements are securely recorded and accessible to relevant stakeholders. The paper details the steps involved in deploying these technologies, from initial assessment and planning to the actual implementation and integration with existing systems.To provide a practical perspective, the paper includes a detailed case study of a major port that has successfully implemented AI and blockchain technologies in its sea cargo handling operations. This case study illustrates the tangible benefits of this integration, such as significant improvements in operational efficiency, enhanced security measures, and cost reductions. It also highlights the challenges encountered during the implementation process, such as the need for extensive training and the complexities of integrating new technologies with legacy systems. The case study serves as a valuable example for other ports and logistics companies considering similar technological upgrades, offering insights into the best practices and potential pitfalls.This research paper underscores the transformative impact of AI and blockchain on logistics and facility management, particularly in sea cargo handling services. By integrating these technologies, logistics operations can achieve higher levels of efficiency, security, and transparency, ultimately leading to more reliable and cost-effective supply chains. However, the paper also cautions that the successful implementation of these technologies requires careful planning, a clear understanding of the specific operational context, and a willingness to invest in the necessary infrastructure and training. As the logistics industry continues to evolve, the integration of AI and blockchain will likely become a standard practice, paving the way for a more advanced and secure global trade network.

The integration of artificial intelligence (AI), big data analytics, and blockchain technologies within the digital economy presents transformative opportunities for promoting low-carbon urban development. However, a systematic understanding of how these digital innovations influence urban carbon mitigation remains limited. This study addresses this gap by proposing two research questions (RQs): (1) What are the key success factors for artificial intelligence, big data, and blockchain in urban carbon emission reduction? (2) How do these technologies interact and support the transition to low-carbon cities? To answer these questions, the study employs a hybrid methodological framework combining the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques. The data were collected through structured expert questionnaires, enabling the identification and hierarchical analysis of twelve critical success factors (CSFs). Grounded in sustainability transitions theory and institutional theory, the CSFs are categorized into three dimensions: (1) digital infrastructure and technological applications; (2) digital transformation of industry and economy; (3) sustainable urban governance. The results reveal that e-commerce and sustainable logistics, the adoption of the circular economy, and cross-sector collaboration are the most influential drivers of digital-enabled decarbonization, while foundational elements such as smart energy systems and digital infrastructure act as key enablers. The DEMATEL-ISM approach facilitates a system-level understanding of the causal relationships and strategic priorities among the CSFs, offering actionable insights for urban planners, policymakers, and stakeholders committed to sustainable digital transformation and carbon neutrality.

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry.Graphic abstractFrom the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

In the realm of Artificial Intelligence (AI) integration and project management efficiency (PME), a comprehensive research study has been conducted, primarily focusing on various industries in Pakistan. The intricate interplay between AI integration, team proficiency in AI, organizational support for AI technologies, and PME forms the crux of this investigation. The theoretical underpinning of this research has been rooted in the Resource-Based View (RBV) theory. Data for this study have been collected through a structured questionnaire survey, targeting a diverse group comprising project managers, IT managers, senior executives, and other key personnel engaged in AI-driven decision support systems. The research has revealed significant positive correlations between the integration of AI, team proficiency in AI, organizational support for these technologies, and PME. These findings highlight the crucial role these elements play in enhancing project outcomes. This study, by uncovering these relationships, offers valuable insights for organizations aiming to optimize their project management practices, especially in emerging economies like Pakistan. It contributes to the existing body of knowledge by providing a nuanced understanding of how AI integration can be leveraged to enhance project management efficiency. Furthermore, the study discusses broader implications for policy and suggests directions for future research, emphasizing the strategic importance of nurturing AI competencies and fostering organizational support for AI technologies to realize enhanced project management outcomes.

The rapid evolution of Artificial Intelligence (AI) has profoundly influenced various sectors, with education emerging as a pivotal area of transformation. The integration of AI into educational systems is redefining teaching methodologies, learning experiences, and administrative efficiencies. However, this intersection of AI and education faces significant challenges, including disparities in access, ethical concerns, and the lack of standardized frameworks for implementation. To address these challenges, this paper proposes a comprehensive AI-powered educational framework designed to personalize learning experiences and scale educational delivery efficiently. The framework incorporates a multi-layered architecture consisting of intelligent tutoring systems, adaptive learning platforms, and automated assessment tools. These components are designed to leverage AI algorithms such as natural language processing, predictive analytics, and machine learning to analyze student data, identify learning gaps, and deliver customized content. The proposed solution was evaluated through case studies and pilot implementations, demonstrating improved learner engagement, enhanced knowledge retention, and optimized resource utilization. Key findings include a 25% improvement in learning outcomes in personalized environments and increased teacher productivity by automating repetitive tasks. This research contributes to the field by offering a scalable and practical model for integrating AI into educational systems. It highlights ethical considerations, emphasizes the importance of inclusivity, and underscores the need for interdisciplinary collaboration. Finally, the paper presents actionable recommendations, including policy guidelines, strategies for addressing equity challenges, and a roadmap for future research. These recommendations aim to guide educators, technologists, and policymakers in harnessing the full potential of AI to create more equitable and effective learning ecosystems.

Digitalization reduces unnecessary workload by accelerating and diversifying business processes and enables it to focus on more useful areas. In this context, the transformation of accounting in the digital age aims to ensure efficiency in accounting transactions with real-time accounting information system integration. In terms of real-time accounting, it is important to benefit from the power of new technologies by connecting all current technologies to every stage of financial accounting processes and to establish a solid integrated system. The contribution of every new technology to the process is inevitable within the scope of cloud-based accounting information systems. In order to find an answer to the question of how artificial intelligence and blockchain technologies will affect cloud-based accounting systems, a detailed literature review has been conducted and discussed conceptually. In this context, the study focuses on the advantages of cloud-based accounting systems unlike traditional accounting systems, the effectiveness of blockchain and artificial intelligence technologies in accounting processes, and the synergy of blockchain and artificial intelligence. Today, accounting basic functions have been significantly integrated into artificial intelligence technology. Decentralized artificial intelligence emerging as a combination of artificial intelligence and blockchain allows the processing of reliable, digitally signed, and secure shared data that is stored on a decentralized and distributed blockchain without trusted third parties or intermediaries. The basic understanding of this decentralized, reliable, and secrecy system is based on the reliability and credibility of information. Central data storage can be highly sensitive in terms of security and privacy when it contains personal and private data about users, operations, and financial information. Artificial intelligence applications can expose the capacity and scaling issues of the centralized infrastructure that needs to process, transform, and store big data sets. Blockchain-based decentralized storage infrastructure will simplify cryptographically secure data storage across participatory networks. Thus, technology integration will offer benefits such as enhanced data security, collective decisions making, decentralized intelligence, and high efficiency. Multi-user accounting processes involving stakeholders such as business management, regulators, financial institutions, or government are inherently inefficient by reason for the multilateral authorization of business transactions. The integration of artificial intelligence and blockchain will enable automatic and rapid verification of data-asset-value transfers between different stakeholders. Thus, it is clear that the stakeholders involved in the process (financial advisors, auditors, public and fiscal authorities, shareholders, creditors) will also provide practical solutions to all their needs.KeywordsAccounting information systemCloud accountingArtificial intelligenceBlockchain

PurposeThis paper will concentrate on examining how the integration of Artificial Intelligence (AI) and Blockchain technologies will enhance resilience, transparency, and intelligence in the modern supply chains structures. It also talks about the awareness, preparedness and strategic implementation of these technologies within the organizations with emphasis being on sustainable and responsive supply chain performance. Design/methodology/approachThis quantitative research design was chosen with the assistance of the structured questionnaire survey that was distributed to 335 professionals working in manufacturing, logistics, IT, and retail. The instrument was based on five constructs, including Awareness and Perception, AI Integration and Supply Chain Intelligence, Blockchain Implementation and Transparency, Resilience and Risk Management, Future Readiness and Strategic Adoption. The analysis of data was done using the descriptive statistics and the reliability analysis was made based on the Cronbachs Alpha (alpha = 0.921 in general) which served to prove there was excellent internal consistency of the scale. FindingsThe findings showed that there was a high degree of awareness and positive attitude to the use of AI and blockchain. The respondents concurred that AI enhances the accuracy of forecasting, decision-making, and operational efficiency, whereas blockchain guarantees data transparency, data security, and trust among stakeholders. Moreover, it was found that the intertwining of the technologies was viewed as a decisive force of supply chain resiliency and long-term strategic competitive edge. Nevertheless, financial requirements of investment and scarcity of digital infrastructure were claimed as a challenge to the full implementation. Originality/valueThe paper is one of the new contributions to the existing literature on the digital transformation and intelligent supply chain management, as it empirically confirms the beneficial impact of introducing AI-Blockchain integration as the core of operations resilience. It provides a realistic guide to managers and policy makers who might want to use the emerging technologies to gain sustainable competitiveness in an ever volatile global market.

Integrating artificial intelligence (AI) with blockchain technology presents a transformative approach to enhancing security in financial services. This fusion leverages the strengths of both AI and blockchain to mitigate various security risks, including fraud, data breaches, and identity theft, thereby bolstering trust and confidence in financial transactions. This abstract explores the synergies between AI and blockchain, highlighting their combined capabilities, applications, and potential benefits for the financial services industry. AI algorithms, including machine learning and natural language processing, empower financial institutions to analyze vast amounts of data in real-time, identifying patterns, anomalies, and suspicious activities indicative of fraudulent behavior. By integrating AI-powered fraud detection systems with blockchain-based transactional networks, organizations can enhance security and transparency throughout the entire financial ecosystem. Blockchain's immutable ledger ensures the integrity and traceability of transactions, while AI algorithms provide advanced analytics and predictive insights to detect and prevent fraudulent activities effectively. Furthermore, AI-driven identity verification and authentication systems enhance security in digital transactions by accurately verifying user identities and detecting unauthorized access attempts. By integrating AI-based biometric authentication with blockchain-based identity management solutions, financial institutions can streamline customer onboarding processes, enhance security, and protect sensitive information from unauthorized access. Moreover, AI-powered smart contracts automate and enforce the execution of contractual agreements, reducing the risk of fraud, errors, and disputes in financial transactions. By combining AI-driven smart contract platforms with blockchain technology, organizations can facilitate secure, transparent, and tamper-proof transactions, eliminating intermediaries and reducing transaction costs. The integration of AI with blockchain also offers opportunities for regulatory compliance and risk management in financial services. AI-powered regulatory compliance solutions analyze vast amounts of regulatory data, identify compliance risks, and ensure adherence to regulatory requirements. By integrating these solutions with blockchain-based regulatory reporting systems, financial institutions can enhance transparency, auditability, and regulatory oversight, fostering trust and compliance in the financial ecosystem. In conclusion, the integration of AI with blockchain technology holds immense potential for enhancing security in financial services. By harnessing the combined capabilities of AI and blockchain, organizations can detect and prevent fraudulent activities, enhance identity verification and authentication, streamline transaction processes, and ensure regulatory compliance. This abstract underscores the transformative impact of integrating AI with blockchain on security in financial services, paving the way for a more secure, efficient, and trusted financial ecosystem.. Keywords: Al, Blockchain, Enhanced, Financial, Services Security.