AWS and human-AI collaboration: Transforming industries through integrated intelligence

Enterprise IT infrastructures experience a transformation because of rapid cloud computing adoption, especially Amazon Web Services (AWS), which delivers scalability, flexibility, and cost efficiency. AWS users encounter difficulties optimizing their performance because they face problematic situations combining workload variability with complex resource distribution systems and delay issues. The implementation of Artificial Intelligence constructs an evaluation process to boost performance optimization in Amazon Web Services (AWS) cloud systems. AI-powered insights implemented through machine learning algorithms with predictive analytics capabilities and automation functionality improve workload distribution and enhance auto-scaling efficiency while reducing operational expenses. Processor analysis of historical data combined with real-time metrics allows AI algorithms to make proactive decisions about resource administration, detect anomalies, and perform innovative management. The research evaluates performance-altering AI-based approaches that enhance AWS services, including EC2, Lambda, and Sage Maker. Implementing AI-based optimizations generates copious improvements in computational efficiency and cost performance based on experimental data. Thanks to AI, cloud performance management strategies will fundamentally transform since they endorse efficient AWS environments that align with business requirements.

The rapid evolution of Amazon Web Services (AWS) cloud technologies continues to reshape enterprise computing environments across global industries. This technical review examines recent innovations in AWS services that are transforming organizational capabilities and competitive positioning. Beginning with computational performance advancements through AWS Graviton processors and specialized High-Performance Computing offerings, the article explores how these ARM-based architectures deliver enhanced efficiency across diverse workloads. The expanding AI and Machine Learning ecosystem, particularly through AWS Bedrock's foundation model integration and SageMaker's democratized ML tools, enables organizations to implement sophisticated intelligence capabilities without extensive expertise. Serverless computing developments, including Lambda Function URLs and SnapStart features, alongside visual workflow orchestration through Step Functions, have simplified application development while improving performance characteristics. Container orchestration enhancements facilitate consistent hybrid deployments across on-premises and cloud environments. Looking forward, AWS continues strategic investments in quantum computing initiatives, sustainability practices, and multi-cloud compatibility tools that position the platform at the forefront of cloud innovation. These advancements collectively enable organizations to achieve greater operational agility, cost efficiency, and innovation capacity while addressing evolving challenges in security, compliance, and technical workforce development.

Purpose This paper examines how human–AI collaboration boundaries are conceptualized and implemented differently across two key European sectors: healthcare and financial services. Using a narrative literature review methodology and the AI–human collaboration theoretical framework, it analyzes sectoral variations in collaborative dynamics. Design/methodology/approach A narrative literature review approach is employed to synthesize existing knowledge across disciplines. The AI–human collaboration theoretical framework is applied to analyze sector-specific patterns in human-AI interaction based on professional identities, risk models and expertise traditions. Hypothetical case illustrations demonstrate practical applications. Findings The research identifies distinct human–AI collaboration models across sectors: healthcare prioritizes clinical authority and financial services implements tiered authority models based on risk profiles. Sectoral contexts significantly shape collaborative boundaries through professional traditions, regulatory environments and knowledge integration patterns. Originality/value This research contributes to innovation management theory by demonstrating how sector-specific professional identities and expertise traditions shape collaborative boundaries between human and technological agents. It offers a structured comparative framework for analyzing human–AI collaboration and provides actionable insights for designing collaborative systems that improve human capabilities within sectoral contexts.

This study examines the impact of API-based financial services and open banking on market competition, customer trust, and data privacy, highlighting key challenges such as compliance complexities, unauthorized access, and data breaches.The research explores cybersecurity threats, regulatory requirements, and technological advancements influencing data security.Mitigation measures, such as regulatory frameworks and technological safeguards, are analysed to improve security while fostering innovation.Findings show that improving API security, authentication protocols, and regulatory compliance is critical for a secure open banking ecosystem.A balance between innovation and security measures will improve consumer trust and facilitate the sustainable growth of digital financial services.

This study examines the measurement of effectiveness in human-AI collaborative management within hybrid decision-making teams. Using publicly available datasets including chess decision-making data (n=100), CoAuthor collaborative writing interactions (n=63), and Amazon Mechanical Turk team performance data (n=125), we employed mixed-methods analysis to identify key performance indicators and factors contributing to optimal human-AI collaboration. Our findings reveal that team efficacy is significantly influenced by decision-making style, AI transparency, and task allocation strategies. Autocratic decision-making styles negatively impact team effectiveness (OR=1.85), while collaborative approaches show improved performance outcomes (OR=3.48). The research contributes to organizational behavior theory by establishing a framework for measuring human-AI collaborative effectiveness and identifying critical success factors for hybrid decision-making teams. Implications for management practice include the need for adaptive leadership styles, transparency-enhanced AI systems, and structured collaboration protocols in human-AI teams.

Database as a service (DaaS), a new paradigm of software as a service based on cloud computing, is attracting more and more enterprises (data owners) to delegate their database management to a professional third party (database service provider) such as Amazon Web Services and Rackspace. Data owners in DaaS lose control of their sensitive data, which are stored in the delegated database and managed by the untrusted database service provider. Therefore, many encryption‐based approaches including attribute‐based encryption were proposed to implement fine‐grained access control in DaaS scenarios. However, most of the proposed access control enforcement approaches only support one or two of the following privacy guarantees: data privacy, policy privacy and key privacy. In this paper, we first propose a novel concept of DualAcE: a flexible fine‐grained dual access control enforcement mechanism in DaaS by efficiently combining the ciphertext‐policy attribute‐set‐based encryption with database service provider re‐encryption into a DaaS paradigm. The proposed mechanism has implemented dual access control enforcement with multi‐privacy guarantee: data privacy in delegated database, policy privacy in delegated authorization table and key privacy in key distribution process.We describe the security and efficiency analysis through cryptography theory and experimental results. Copyright © 2014 John Wiley & Sons, Ltd.

Enhancing foreign exchange reserve security for central banks using Blockchain, FHE, and AWS

The construction industry, characterized by its intricate network of stakeholders and diverse workforce, grapples with the challenge of managing information effectively. This study delves into this issue, recognizing the universal importance of safeguarding data, particularly amid rising concerns around unauthorized access and breaches. Aiming to harness the potential of blockchain technology to address these challenges, this study used hypothetical biographical and safety data of construction workers securely stored on a Hyperledger Fabric blockchain. Developed within the Amazon Web Services (AWS) cloud platform, this blockchain infrastructure emerged as a robust solution for enhancing data security and privacy. Anchored in the core principles of data security, the model emerges as a potent defender against the vulnerabilities of traditional data management systems. Beyond its immediate implications, this study exemplifies the marriage of blockchain technology and the construction sector, and its potential for reshaping workforce management, especially in high-risk projects and optimizing risk assessment, resource allocation, and safety measures to mitigate work-related injuries. Practical validation through transaction testing using Hyperledger Explorer validates the model’s feasibility and operational effectiveness, thus serving as a blueprint for the industry’s data management. Ultimately, this research not only showcases the promise of blockchain technology in addressing construction data security challenges but also underscores its practical applicability through comprehensive testing, thus heralding a new era of data management that harmonizes security and efficiency for stakeholders’ benefit.

The increased penetration of renewable resources and retirement of traditional generation units are posing new challenges to power system operations and planning to meet the ever-growing demand for more computing resources to run large-scale, computationally intensive simulations. As the traditional on-premises infrastructure loses pace to the constantly increasing computing needs, cloud computing has become an attractive addition for increasing average computing capacity, and alternative for meeting peak demands. ISO New England (ISO-NE) pioneered the use of cloud computing technology for large-scale power system simulations, believing it to be the only viable solution that is capable of keeping pace with the growing challenge in the foreseeable future. This paper shares the practical design and implementation of an elastic, on-demand, application-independent cloud platform that ISO-NE deployed in production at the end of 2017. As a secure, cost-effective and scalable platform for addressing the intensive computing demand in planning studies, this platform made using Amazon Web service as easy as using on-premises computers by completely automating the access to cloud based resources. This frees ISO engineers to focus on power system analysis, and thus significantly increased their productivity. To accommodate simulation jobs with varying urgency requirements, this platform allows the users to make the tradeoff between cost and completion time. The platform also further strengthened its support for cyber security and data privacy.

Yield mapping visualizes yield rate per geological distribution. It is frequently used as a baseline metric to measure yield efficiency in precision farming. A major challenge in mapping yield for specialty crops is how to collect accurate yield data without incurring substantial overhead to a farming operation. We design a yield efficiency analysis system that uses a cloud-based computing platform to acquire and analyze yield data. By reusing labor data collected by a cloud-based labor monitoring system that we developed earlier, our system calculates yield data from labor data, and computes yield map in real time and without the overhead for data acquisition. A distinctive feature of our approach is the introduction of a customizable yield distribution function that quantifies the probability of geographic distribution of fruits weighted at a Labor Monitoring Device. Practitioners may define yield distribution functions based on operational characteristics of an orchard, enabling our system adaptive for a variety of orchards with different harvesting operations and canopy architecture. Using a multi-tenancy software architecture, our system can support multiple orchards concurrently with improved scalability and data privacy. Our system has been deployed and tested on Amazon Web Services (AWS).

Cybersecurity training activities require specialized environments, typically called cyber ranges, to make it possible for trainees to acquire not only security knowledge, but also practical security skills. However, the setup of these training environments is a tedious task, which hinders the wider use of cyber ranges for security training. In its turn, this has a negative impact on the development of the cybersecurity workforce that is exceedingly necessary in our network-centric society. In this paper we introduce our approach of using the Amazon Web Services (AWS) Elastic Compute Cloud (EC2) public cloud for cyber range deployment, thus making it possible to conduct cybersecurity training activities at scale and at a relatively low cost. Our system was implemented by extending the functionality of the cyber range instantiation system CyRIS that is available as open source on GitHub. We evaluated our implementation from several perspectives, demonstrating that public cloud deployment can provide similar functionality and performance compared to local server or private cloud deployment, while avoiding the high purchase and management costs associated to those.

This review synthesizes current research on human-AI collaboration, particularly within educational and decision-making environments, to evaluate when and how partnerships between humans and artificial intelligence outperform individuals alone. While AI technologies—such as machine learning, natural language processing, and recommender systems—have demonstrated significant potential to enhance personalized learning and collaborative educational outcomes, evidence indicates a nuanced relationship between human-AI interactions and task effectiveness. Meta-analytic findings reveal that human-AI collaborations often underperform compared to the best individual agent, especially in decision-making tasks, whereas content creation tasks benefit from such synergy. Key factors influencing successful collaboration include the relative strengths of humans and AI, task design, emotional and social engagement, and the balance between automation and human control. Additionally, ethical considerations surrounding transparency, data privacy, and stakeholder involvement remain critical in the deployment of AI systems in education. This review underscores the importance of targeted human-centerd design and active end-user participation to harness AI’s potential effectively, advocating for future research that integrates cognitive science insights to optimize human-AI teamwork and enhance learning and decision-making outcomes.

The rapid progress in artificial intelligence (AI) and the emergence of large language models (LLMs), like GPT-4, create a unique opportunity to transform nursing care planning. In this editorial, we explore the potential applications of AI in the nursing process, with a focus on patient data assessment and interpretation, communication with patients and families, identifying gaps in care plans, and ongoing professional development. We also examine the ethical concerns and challenges associated with AI integration in healthcare, such as data privacy and security, fairness and bias, accountability and responsibility, and the delicate balance between human-AI collaboration. To implement LLMs responsibly and effectively in nursing care planning, we recommend prioritizing robust data security measures, transparent and unbiased algorithms, clear accountability guidelines, and human-AI collaboration. By addressing these issues, we can improve nursing care planning and ensure the best possible care for patients.

Identifying decisions in optometry: A validation study of the decision identification and classification taxonomy for use in medicine (DICTUM) in optometric consultations

This paper analyses the methodological and technical aspects of health technology assessment (HTA) as a tool for evaluating health technologies and procedures, with special reference to diagnostic imaging; describes the main experiences with HTA at the international and national level; outlines the most important HTA projects in Italy, and analyses the effects of HTA on health care strategies and policies. The work was carried out in three phases. In the first phase, the authors analysed the principles, methods and instruments of HTA; in the second, they evaluated the current status of HTA in different countries; and in the third, they defined the impact of HTA on the decision-making process in health care. Since the 1970s, technological innovation has been accompanied by the development of methods for the multidisciplinary assessment of the technical, scientific, economic, ethical and social aspects inherent in the use of new technologies. The method is implemented at an international level by a network of public and private bodies that carry out HTA in support of health care policies. Because the application of HTA is still in its early stages in Italy, it is necessary to promote its development by drawing on consolidated international experiences.