Artificial Intelligence Management Research Articles

How perceived lack of benevolence harms trust of artificial intelligence management.

As organizations continue to supplement and replace human management with artificial intelligence (AI), it is essential that we understand the factors that influence employees' trust in AI management. Across one preregistered field study, where we survey 400 delivery riders in Mainland China, and three preregistered experiments (total N = 2,350), we find that AI management is perceived as less benevolent than human management. Given that benevolence is an important antecedent of trust in leaders, this perception has a negative effect on trust in AI management, even when controlling for perceived ability and integrity. Employees prefer human management to AI management in high empathy demand contexts, where individuals seek management that can empathize and experience the emotions that they are feeling, as opposed to low empathy demand contexts. These findings deepen our understanding of trust and provide important theoretical and practical insights on the implementation and adoption of AI management. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A typology of AI-based tasks for the HR function

Purpose This paper aims to elucidate the keys transformations of human resources (HR) tasks amid the age of artificial intelligence (AI). Design/methodology/approach This paper synthesizes recent theoretical and empirical research on the topic of AI and human resource management to establish a typology of AI-based HR tasks. Findings HR jobs will revolve around three types of tasks in the age of AI: mechanical, thinking and feeling. Originality/value AI radically changes HR function and it becomes essential for organizations to clearly define the purpose of using AI, its role and the context of its use in tasks. Strategic value of the HR function will lie in its future reorientation toward feeling tasks. HR managers need to possess the knowledge, skills and abilities to adapt to these tasks and ensure the responsible use of AI.

Study on Class Management Change and Teacher Orientation under the Vision of Artificial Intelligence

With the rise of artificial intelligence (AI), class management is experiencing unprecedented changes, but it is also facing certain risks and tests. It is of great practical significance to clarify the class management changes and teacher orientation under the vision of AI. Since the application of AI in the field of education, class management has realized the burden reduction and efficiency increase under the ability of technology, and has changed from the traditional mode of standardization, one-sided and unitization to a personalized, systematic and diversified intelligent mode, which provides great convenience for the management of class teachers. However, at the same time, the rise of AI has also caused a series of dilemmas such as the disclosure of students’ privacy and the challenge of the main value, which has put forward higher requirements for teachers’ literacy, positioning and management. Based on this, this paper puts forward the orientation of teachers in class management under the vision of AI. Teachers should improve their intelligent application literacy and boost the level of class management. Teachers should reasonably reflect on intelligent technology and grasp the applicable management situation. Teachers should fill the gaps in intelligent management and pay attention to humanistic emotional assistance to promote the positive integration of AI and class management.

The Evolution of Customer Service: Identifying the Impact of Artificial Intelligence on Employment and Management in Call Centres

The transformative role of Artificial Intelligence (AI) is reshaping customer service within call centres. As AI technologies advance, there is a growing trend of automation replacing human agents in these service-oriented environments. The adoption of AI in call centers raises concerns about job displacement for human agents. The economic repercussions of this shift need to be carefully addressed to mitigate potential societal challenges. The economic impact of AI customer care and management is a dynamic interplay of cost efficiency, enhanced productivity, and ethical considerations. Striking a balance between technological advancement and societal well-being is imperative for a sustainable future of the industry.

Integration and transformation: The impact and applications of artificial intelligence in the financial sector

This paper explores the intersection of artificial intelligence (AI) and the financial sector, showcasing their transformative synergy. The integration of AI into finance has led to pioneering advancements like robo-advisors and AI-driven risk assessment methods. These innovations reshape investment strategies and risk management, ushering in a new era of financial operations. The study's focal question examines how AI recalibrates investment management, risk assessment, and fraud prevention in finance. The paper comprises sections on AI's impact on investment management, risk assessment, and fraud detection, detailing how robo-advisors provide personalized portfolio recommendations, AI aids risk identification and management, and transaction surveillance benefits from AI-powered fraud detection. Ethical, regulatory, and accountability considerations are discussed, reflecting AI's transformative influence on traditional financial paradigms. The application of AI in transaction detection and its role in enhancing portfolio recommendations, risk management, and automated trading are examined. While AI holds potential, its limitations such as data quality, model risks, and ethical concerns must be addressed. Regulatory oversight is crucial to ensure responsible AI implementation, fostering a balance between technological progress and financial stability. This paper underscores the intricate relationship between AI and finance, portraying AI's capacity to reshape the financial landscape and drive innovation

What competencies will be needed to manage Artificial Intelligence in the workplace? (An AI perspective)

Key digested messageThis article explores essential managerial competencies in the context of integrating artificial intelligence (AI) into the modern workplace. Key skills include technical literacy for informed decision-making, ethical consideration to address biases, change management for seamless AI adoption, and effective communication to bridge technical and non-technical stakeholders. These competencies position managers as pivotal contributors to successful AI management, organisational resilience, and innovation.

Managing risk and resilience in autonomous and intelligent systems: Exploring safety in the development, deployment, and use of artificial intelligence in healthcare.

Autonomous and intelligent systems (AIS) are being developed and deployed across a wide range of sectors and encompass a variety of technologies designed to engage in different forms of independent reasoning and self-directed behavior. These technologies may bring considerable benefits to society but also pose a range of risk management challenges, particularly when deployed in safety-critical sectors where complex interactions between human, social, and technical processes underpin safety and resilience. Healthcare is one safety-critical sector at the forefront of efforts to develop and deploy intelligent technologies, such as through artificial intelligence (AI) systems intended to automate key aspects of healthcare tasks such as reading medical images to identify signs of pathology. This article develops a qualitative analysis of the sociotechnical sources of risk and resilience associated with the development, deployment, and use of AI in healthcare, drawing on 40 in-depth interviews with participants involved in the development, management, and regulation of AI. Qualitative template analysis is used to examine sociotechnical sources of risk and resilience, drawing on and elaborating Macrae's (2022, Risk Analysis, 42(9), 1999-2025) SOTEC framework that integrates structural, organizational, technological, epistemic, and cultural sources of risk in AIS. This analysis explores an array of sociotechnical sources of risk associated with the development, deployment, and use of AI in healthcare and identifies an array of sociotechnical patterns of resilience that may counter those risks. In doing so, the SOTEC framework is elaborated and translated to define key sources of both risk and resilience in AIS.

Toward human-centered AI management: Methodological challenges and future directions

As algorithms powered by Artificial Intelligence (AI) are increasingly involved in the management of organizations, it becomes imperative to conduct human-centered AI management research and understand people's feelings and behaviors when machines gain power over humans. The two mainstream methods – vignette studies and case studies – reveal important but inconsistent insights. Here we discuss the respective limitations of vignette studies (affective forecasting errors, biased media coverage, and question substitution) and case studies (social desirability biases and lack of random assignment and control conditions), which may lead them to overrate negative and positive reactions to AI management, respectively. We further discuss the advantages of a third method for mitigating these limitations: field experiments on crowdsourced marketplaces. A proof-of-concept study on Amazon Mechanical Turk (Mturk; as a world-leading crowdsourcing platform) showed unique human reactions to AI management, which were not perfectly aligned with those in vignette or case studies. Participants (N = 504) did not differ significantly under AI versus human management, in terms of performance, intrinsic motivation, fairness perception, and commitment. We suggest that crowdsourced marketplaces can go beyond human research subject pools and become models of AI-managed workplaces, facilitating timely behavioral research and robust predictions on human-centered work designs and organizations.

Integration of AI in Distributed Energy Resource Management for Enhanced Load Balancing and Grid Stability

The landscape of power systems is undergoing a transformative shift with the burgeoning inclusion of Distributed Energy Resources (DERs), which, while beneficial in enhancing the sustainability of electricity supply, introduces complexity in grid management. This paper presents a comprehensive framework leveraging Artificial Intelligence (AI) to orchestrate DER operations, thus achieving optimized load balancing and grid stability. A multi-agent system that utilizes machine learning algorithms is proposed, capable of predictive analytics and real-time decision-making. The architecture is underpinned by a robust data layer that assimilates inputs from a myriad of sensors and smart meters, facilitating the dynamic management of DERs. Through the simulation of various scenarios, the system demonstrates significant improvements in load distribution, peak shaving, and voltage regulation. The framework also showcases resilience against fluctuations and anomalies, attributing to the self-learning capability of AI models that continuously refine control strategies. The adaptability of the system is evaluated in the context of grid demand-response initiatives and the integration of intermittent renewable energy sources. Overall, the results indicate a substantial advancement in the operational efficiency of power grids, highlighting the synergy between AI and energy resource management.

Artificial intelligence in management and prognosis of melanoma: A literature review

This literature review aims to explore the applications of artificial intelligence (AI) in the management and prognosis of melanoma, a highly aggressive form of skin cancer. Melanoma is a major public health concern worldwide, and early detection and accurate prognosis are crucial for effective treatment. AI techniques, such as machine learning and deep learning algorithms, have shown promising results in improving the accuracy of melanoma diagnosis, risk assessment, and prognostic prediction. This review consolidates and analyzes existing studies to provide an overview of the current state of AI in melanoma management, highlighting its potential benefits, limitations, and future directions.

Introduction: Artificial Intelligence in Management

Artificial Intelligence (AI) has become a popular topic of debate, both within and beyond academia. According to some commentators, we are on the cusp of major social and economic change. The future of work is now to become the present. This introduction aims to lay out some of the emerging and pressing issues relating to AI in management. It is not an attempt at prediction or doomsaying but instead outlines what research questions and methods can equip us to make sense of these changes.

When computer science is not enough: universities knowledge specializations behind artificial intelligence startups in Italy

This paper investigates the role of local knowledge specializations from universities in the artificial intelligence (AI) startup creation process. The empirical analysis is grounded in the Knowledge Spillovers Theory of Entrepreneurship. It provides evidence of the interplay between the presence of university knowledge specializations and the creation of AI startups located in Italian NUTS3 regions. More precisely, we analyze the mix of different types of university knowledge specializations able to foster the creation of AI startups, demonstrating that competencies in technological domains, such as computer science, need to be complemented with domains of applications for the emergence of AI technology. The findings indicate that the creation of AI startups is closely tied to the local knowledge present in the region. The article also proposes an empirical approach to identify and classify innovative startups operating in AI domains. Our work offers important implications for regional innovation policies and the management of AI in entrepreneurship.

Emerging Applications for Computer Vision and Artificial Intelligence in Management of the Cardiovascular Patient.

Artificial intelligence and telemedicine promise to reshape patient care to an unprecedented extent, leading to a safer and more sustainable work environment and improved patient care. In this article, we summarize how these emerging technologies can be used in the care of cardiovascular patients in such ways as fall detection and prevention, virtual nursing, remote case support, automation of instrument counts in the operating room, and efficiency optimization in the cardiovascular suite.

ПРОБЛЕМЫ И ПЕРСПЕКТИВЫ ИСПОЛЬЗОВАНИЯ ТЕХНОЛОГИЙ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В МЕНЕДЖМЕНТЕ: ЗАПРОС НА ИИ-СЕРВИС

The article considers the modern practice and experience of using the latest achievements in the field of artificial intelligence by foreign and domestic companies. The goal is to identify the key problem and formulate the fundamental prospects for the use of artificial intelligence in management, including to form a request for the creation of an AI management service as a hardware and software complex that increases the efficiency of solving management problems. The methodological basis of the study was: dialectical, systemic, complex, situational approaches; methods of comparative, expert analysis. The following main results of the study can be distinguished: current trends and trends in the use of artificial intelligence are considered and systematized; substantiates the importance and necessity of using artificial intelligence technologies in management; the main promising directions for the use of artificial intelligence technologies in modern companies are formulated.

Transformative Data Management Technique: Redefining Artificial Intelligence (AI)

This abstract provides an overview of the evolving landscape of data management, driven by the transformative influence of artificialintelligence (AI). This exploration delves into how AI technologies are reshaping every facet of data management, from collection toanalysis, offering unprecedented efficiencies and insights. In the age of massive data proliferation, effective data management has becomea cornerstone of organizational success. AI algorithms are revolutionizing data integration by automating the harmonization of diversedata sources, transcending the limitations of structured and unstructured data. Through advanced techniques such as natural languageprocessing, AI enables comprehensive data extraction from text, images, and audio, broadening the scope of information captured. Insummary, the amalgamation of AI and data management yields a paradigm shift. From harnessing unstructured data to elevating securitymeasures, AI redefines the data landscape. Through automation, predictive capabilities, and adaptive learning, AI-driven datamanagement propels organizations towards more efficient and informed strategies. This abstract encapsulates the profoundtransformation of data management facilitated by AI, urging organizations to grasp the vast potential of this synergy for informed,strategic decision-making in a data-centric world.Keywords: Data Management, AI Algorithms, Big Data and AI, Data Collection & Quality

Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture

Artificial intelligence (AI) research and market have grown rapidly in the last few years, and this trend is expected to continue with many potential advancements and innovations in this field. One of the emerging AI research directions is Distributed Artificial Intelligence (DAI). It has been motivated by technological advances in communication, networking, and hardware, together with the nature of data being generated from connected, distributed, and diverse objects. DAI is expected to create a fertile environment for innovative, advanced, robust, and scalable approaches for AI supporting the vision of smart societies. In this paper, we explore state of the art on DAI and identify the opportunities and challenges of provisioning distributed AI as a service (DAIaaS). We provide a taxonomy and a comprehensive review covering the literature from 2016 to 2022. It comprises various aspects of DAI, including AI workflow, distribution paradigms, supporting infrastructure, management techniques, and applications. Based on the gained insights from the conducted review, we propose Imtidad, a framework for provisioning DAIaaS over the cloud, fog, and edge layers. We refine this framework and propose the Imtidad software Reference Architecture (RA) for designing and deploying DAI services. In addition, we extended the framework and developed a future networking infrastructure transformation framework, as it is one of the main enablers for DAI. This framework and RA can be used as guidance facilitating the transition to the future DAI, where DAI is decoupled from the design and development of smart applications. This paper, including the proposed framework, RA, taxonomy, and detailed review, is expected to have an extensive impact on DAI research and accelerate innovations in this area.

Artificial intelligence applied to potential assessment and talent identification in an organisational context

Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.

Culture intelligent workflow, structure, and steps.

Technologies abstract intelligence and provide predictor and precision insight in workflows that manage disorders, similar to cardiology and hematological disease. Positive perceptions of Artificial Intelligence (AI) that support Machine Learning (ML) and Deep Learning (DL) manage transformations with a safe system that improves wellbeing. In sections, workflow introduces an eXamination (X = AI) as an end-to-end structure to culture workstreams in a step-by-step design to manage populace health in a governed system. To better healthcare outcomes, communities and personnel benefit from an explanation and an interpretive that elucidates workflow for citizens or practitioners to comprehend personalized platforms. Therefore, the author undertook structure and practice reviews and appraised perspectives that impact the management of AI in public health and medicine. Figures for the management of AI workflow illustrate and inform on the model, structure, culture, assurance, process steps, values, and governance required for abstract insights in public health and medicine. The papers' end-to-end structure with explanans in a work culture interprets the step-by-step designs that manage the success of AI. Personalized care graphics offer an explanandum in the management of biological analytic value. Healthcare leadership collaboratives plan population health with an upstream, workplace and workstream format. Secure workflow and safety wellbeing system requirements prove that genomics and AI improve medicine. Therefore, the paper discusses group understanding of current practice, ethics, policy, and legality. "Culture, intelligent workflow, structure, and steps" improve wellbeing with personalized care and align a percept for national opportunities, regional control, and local needs. Personalized practice cultures support analytic systems to describe, predict, precision, and prescript medicine in population health management eXaminations.

Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM)

PurposeThe Checklist for Artificial Intelligence in Medical Imaging (CLAIM) is a recently released guideline designed for the optimal reporting methodology of artificial intelligence (AI) studies. Gliomas are the most common form of primary malignant brain tumour and numerous outcomes derived from AI algorithms such as grading, survival, treatment-related effects and molecular status have been reported. The aim of the study is to evaluate the AI reporting methodology for outcomes relating to gliomas in magnetic resonance imaging (MRI) using the CLAIM criteria.MethodsA literature search was performed on three databases pertaining to AI augmentation of glioma MRI, published between the start of 2018 and the end of 2021ResultsA total of 4308 articles were identified and 138 articles remained after screening. These articles were categorised into four main AI tasks: grading (n= 44), predicting molecular status (n= 50), predicting survival (n= 25) and distinguishing true tumour progression from treatment-related effects (n= 10). The average CLAIM score was 20/42 (range: 10–31). Studies most consistently reported the scientific background and clinical role of their AI approach. Areas of improvement were identified in the reporting of data collection, data management, ground truth and validation of AI performance.ConclusionAI may be a means of producing high-accuracy results for certain tasks in glioma MRI; however, there remain issues with reporting quality. AI reporting guidelines may aid in a more reproducible and standardised approach to reporting and will aid in clinical integration.

AIMDP: An Artificial Intelligence Modern Data Platform. Use case for Spanish national health service data silo

The huge amount of data being handled today in any environment, such as energy, economics or healthcare, makes data management systems key to extracting information, analysing and creating more efficient daily processes in these environments. However, the inability of current systems to take advantage of the data generated can waste good opportunities for analysing and extracting information from the data. Modern data platforms (MDP) appear suitable for supporting management systems and are able to perform future prospective analyses. This paper presents a data platform called Artificial Intelligence Modern Data Platform (AIMDP), based on Big Data, artificial intelligence for management, and efficient data handling. The different components of AIMDP intervene in the data acquisition phase and implement algorithms capable of analysing massive data collected from heterogeneous sources. In addition, the entire platform is geared towards data management and exploitation with a layer of security and data governance that allows the integrity and privacy of the databases to be maintained. The proposed platform is designed to be used by users who are not experts in data science. To this end, it implements a user-oriented workflow that has effectively been introduced in a use case of two Spanish hospitals to extract knowledge from their historical data, which had been siloed and had never been explored by any hospital researchers or doctors.