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The Use of Artificial Intelligence in an Organization
Abstract
The approach of this paper is to discuss the effect of Artificial Intelligence (AI) on the employees of an organization, on the organization itself and on the market situation. With technology being more present than ever, every field necessarily encounters AI as time goes by. Nothing is more important for a company than to stay in touch with new innovations and trends. Not only does such knowledge serve as a base for competitive advantage but it also ensures one’s position in a crowded and competitive market. However, with new innovations and AI in front, do the employees view AI as a threat or as a possibility to enhance their productivity and work environment?
Introduction: The use of artificial intelligence (AI) in dispute resolution through arbitration has become an increasingly relevant topic in the legal world. AI can speed up the process of data collection, data analysis, and provide predictions and recommendations in dispute resolution. However, the use of AI also raises some challenges and risks that need to be overcome.Purposes of the Research: This paper intends to explore the potential of the use of Artificial Intelligence (AI) Technology in dispute resolution through arbitration, as well as the challenges and risks associated with its use. Methods of the Research: The research method used is normative research by analyzing various literature and regulations related to the use of AI in dispute resolution through arbitration. Results of the Research: The results showed that the use of AI in dispute resolution through arbitration can speed up the process and improve the accuracy of data analysis. However, its use also has challenges and risks such as the risk of error and the inability of AI to understand the legal nuances and human factors in the dispute resolution process. To minimize risks and ensure fairness and legal certainty in the dispute resolution process through arbitration, clear regulations and standards are needed in the use of AI. In addition, users and legal practitioners involved in the dispute resolution process through arbitration also need to improve understanding and knowledge of AI technologies.
The study evaluates the role of artificial intelligence in achieving organizational accounting system. The study reviewed conceptual and theoretical studies on artificial intelligent (AI) and drawn conclusion. Secondary data such as internet, book, articles and website are used in obtaining the information for the research. It was discovered that Artificial Intelligent improved firm operation. The study concluded that, the role of artificial intelligence in achieving organizational accounting system in Nigeria positively influences the performance of accounting functions. With the positive impact been made by artificial intelligence, in the coming decades, intelligent systems will take over more and more decision making tasks from humans. The study recommended that management of the organization through the accountants should see it as a necessity to consistently adopt innovative technology in order to maintain accurate record within the organization and the accountants should seek Professional Corporation with international organization that could facilitate their adoption of artificial intelligence in enhancing their record keeping.
Purpose
The purpose of this paper is to examine the nature, causes and consequences of the UK’s productivity problems and whether these may be addressed through the new technologies of artificial intelligence (AI).
Design/methodology/approach
This paper reviews the literature on productivity to explain how it relates to earnings within different theoretical frameworks, advocating a “power over rents” framework as most realistic. It explains the UK’s twin productivity problems and reviews their potential causes, critically assessing the capacity for new technologies of AI to address them. It highlights the enduring importance of distribution and the design of work to improving the UK’s productivity.
Findings
The authors find that the UK’s productivity problems will not be solved by AI technologies due to technical and socio-technical challenges which will require the significant re-design of work. The authors highlight the importance of aggregate demand, which has been inhibited by the shifting distribution of income towards capital and rising inequality of earnings. These issues suggest an important role for trade unions and a renewal of the institutions of employment regulation and collective bargaining. While reversing recent trends raises considerable challenges, the authors observe renewed interest in trade unions from previously hostile thinktanks and international institutions including the IMF and OECD.
Originality/value
This paper advocates adopting a “power over rents” theoretical framework to understanding productivity and the distribution of gains. This provides a clear rationale for the role of trade unions, employment regulation and collective bargaining in improving distributional outcomes, raising firm-level productivity and achieving real productivity growth at an aggregate level.
The potential role of artificial intelligence in improving organisations’ performance and productivity has been promoted regularly and vociferously since the 1960s. Artificial intelligence is today reborn out of big business, similar to the occurrences surrounding big data in the 1990s, and expectations are high regarding AI’s potential role in businesses. This article discusses different aspects of knowledge work that tend to be ignored in the debate about whether or not artificial intelligence systems are a threat to jobs. A great deal of knowledge work concerns highly complex problem solving and must be understood in contextual, social and relational terms. These aspects have no generic nor universal rules and solutions and, thus, cannot be easily replaced by artificial intelligence or programmed into computer systems, nor are they constructed based on models of the rational brain. In this respect, this article draws on philosopher Herbert Dreyfus’ thesis regarding artificial intelligence.
The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system.
>>> Purpose – The service sector is at an inflection point with regard to productivity gains and service industrialization similar to the industrial revolution in manufacturing that started in the 18th century. Robotics in combination with rapidly improving technologies like artificial intelligence (AI), mobile, cloud, big data and biometrics will bring opportunities for a wide range of innovations that have the potential to dramatically change service industries. This conceptual paper explores the potential role service robots will play in the future and advances a research agenda for service researchers.
>>> Design/methodology/approach – This paper uses a conceptual approach that is rooted in the service, robotics, and AI literature.
>>> Findings – The contribution of this article is threefold. First, it provides a definition of service robots, describes their key attributes, contrasts their features and capabilities with those of frontline employees, and provides an understanding for which types of service tasks robots will dominate and where humans will dominate. Second, this article examines consumer perceptions, beliefs and behaviors as related to service robots, and advances the service robot acceptance model (sRAM). Third, it provides an overview of the ethical questions surrounding robot-delivered services at the individual, market and societal level.
>>> Practical implications – This article helps service organizations and their management, service robot innovators, programmers and developers, and policymakers better understand the implications of a ubiquitous deployment of service robots.
>>> Originality/value – This is the first conceptual article that systematically examines key dimensions of robot-delivered frontline service and explores how these will differ in the future.
There is much uncertainty in the business world about what artificial intelligence (AI)
really means for innovation, jobs, productivity and growth – and rightfully so. The
potential for adding value and gaining competitive advantage appears to be
enormous – but it seems to be equaled by the challenges of integrating AI technologies
effectively into the business model and minimizing risk.To help organizations realize the full potential of AI, Accenture undertook a study with Frontier Economics to measure AI’s potential economic impact on 16 industries. The goal was to understand the chief reasons for AI’s potential impact and to identify a set of strategies that companies in any industry can use to benefit from advances in AI technologies.
This article discusses the role of technological myths in the development of artificial intelligence (AI) technologies from 1950s to the early 1970s. It shows how the rise of AI was accompanied by the construction of a powerful cultural myth: The creation of a thinking machine, which would be able to perfectly simulate the cognitive faculties of the human mind. Based on a content analysis of articles on AI published in two magazines, the Scientific American and the New Scientist, which were aimed at a broad readership of scientists, engineers and technologists, three dominant patterns in the construction of the AI myth are identified: (1) the recurrence of analogies and discursive shifts, by which ideas and concepts from other fields were employed to describe the functioning of AI technologies; (2) a rhetorical use of the future, imagining that present shortcomings and limitations will shortly be overcome and (3) the relevance of controversies around the claims of AI, which we argue should be considered as an integral part of the discourse surrounding the AI myth.
This article discusses the role of technological myths in the development of Artificial Intelligence (AI) technologies from 1950s to the early 1970s. It shows how the rise of AI was accompanied by the construction of a powerful cultural myth: the creation of a thinking machine, which would be able to perfectly simulate the cognitive faculties of the human mind. Based on a content analysis of articles on Artificial Intelligence published in two magazines, the Scientific American and the New Scientist, which were aimed at a broad readership of scientists, engineers, and technologists, three dominant patterns in the construction of the AI myth are identified: (1) the recurrence of analogies and discursive shifts, by which ideas and concepts from other fields were employed to describe the functioning of AI technologies; (2) a rhetorical use of the future, imagining that present shortcomings and limitations will shortly be overcome; (3) the relevance of controversies around the claims of AI, which we argue should be considered as an integral part of the discourse surrounding the AI myth.
Purpose
This paper highlights the risks faced by white-collar workers resulting from advances in artificial intelligence (AI).
Design/methodology/approach
This paper explores recent research and expert opinion on the evolution of AI and its encroachment on white-collar jobs.
Findings
This paper reveals susceptibility of white-collar jobs to AI.
Practical implications
This paper guides HR practitioners in advising management on the possible deployment of AI to enhance productivity and the resultant impact in the roles that employees perform.
Social implications
This study draws attention to the risks associated with the deployment of AI and as a consequence the loss of white-collar jobs.
Originality/value
This study raises the issue of how AI could disrupt the workplace by usurping white-collar jobs and creates awareness of the need for people in vulnerable white-collar jobs to re-think their careers and for HR practitioners to manage the change that this disruption will bring.
In construction one of the important factors of the effective development of the construction production is improving the methodology of management, as an economic entity. This became especially possible in view of the rapid development of computer technology and logical-linguistic methods. Active application of information and computing technology allows you to select economically feasible methods of management based on reliably grounded methods of artificial intelligence. The ways of solving the tasks in these studies were developed on the basis of the extension of mathematical and symbolic logic. Given the characteristics of the condition of application of methods of artificial intelligence in the field of organizational solutions for the construction. Stages of formation of organizational and technological solutions using rule-based expert systems, artificial neural networks, genetic algorithms. The proposed method is effective implementation of organizational and technological solutions with the use of its information models and systems of making management decisions on the results of monitoring the construction process. The basic information based on models of knowledge representation (semantic, frames, production rules and regulations, precedents), and concepts on the application of artificial neural networks in construction and information about the methods of extraction of knowledge and formation of knowledge bases. Examines the structure and functions of expert systems and decision support systems solutions.
James Watt invented the steam engine in the 17th century. It outperformed human muscle power by orders of magnitude. Nobody imagined that this single invention would evocate massive consequences: The steam engine increased industrial production, led to societal upheaval, and changed the political landscape for the next 100 years. There had been no historical parallel on the effect of a dedicated technical invention on humankind in such a short timeframe. At the beginning of the 21st century, we might experience a similar dynamic. This time, artificial intelligence spread the word of a new era that makes cognitive capabilities available on a large scale. Machines with highly sophisticated mental competencies will turn upside down the knowledge work in every company department, be it marketing, human resources, research and development, customer service, or the even the board of directors. Moreover, this time, intelligent machines will outperform human brain power. This paper argues that the arrival of artificial intelligence at the enterprise pave the way to a wicked problem: It cannot be resolved by tested methodologies, given procedures, and best practices. Instead, it requires a more sophisticated approach: First, companies must involve all relevant stakeholders at the initial stage of deployment because the impact of artificial intelligence is far-reaching. Second, companies must question their given value system because it closely resonates with the capabilities of artificial intelligence machines. Third, companies must conduct controlled experiments, because “divide and rule” do not work anymore to handle machines that mimic human thinking.
The effect of the spread of Artificial Intelligence (AI) on wages depends on both the form of aggregate production relationships and the elasticity of substitution between human and robotic labor. With a conventional production function involving labor, robots, and ordinary capital, an increase in robotic labor can have either a positive or a negative effect on wages. Alternatively, it is possible to estimate the aggregate production relationship without measuring capital or other fixed factors explicitly, using the procedure developed by Houthakker in the 1950s. Houthakker's method is based on the probability distribution of the productivity of the variable factor. Fitting different distributions to cross-sectional data on U.S. productivity, it is shown that if the elasticity of substitution between human and robotic labor is greater than about 1.9, the burgeoning of AI technologies will cause a decline in aggregate wages, other things equal. For the manufacturing sector, an even smaller human-robot elasticity of substitution is likely to result in declining wages of industrial workers as robots proliferate.
Although scientists have calculated the significant positive welfare effects of Artificial Intelligence (AI), fear mongering continues to hinder AI development. If regulations in this sector stifle our active imagination, we risk wasting the true potential of AIs dynamic efficiencies. Not only would Schumpeter dislike us for spoiling creative destruction, but the AI thinkers of the future would also rightfully see our efforts as the ‘dark age’ of human advancement. This article provides a brief philosophical introduction to artificial intelligence; categorizes artificial intelligence to shed light on what we have and know now and what we might expect from the prospective developments; reflects thoughts of worldwide famous thinkers to broaden our horizons; provides information on the attempts to regulate artificial intelligence from a legal perspective; and discusses how the legal approach needs to be to ensure the balance between artificial intelligence development and human control over them, and to ensure friendly artificial intelligence.
If artificial intelligence (AI) were achievable, what would the consequences be for human society? Perhaps surprisingly, the answer to this question is already at hand. We are achieving rapid and accelerating success in our quest to build AI. That very success—and the slowness with which both the academic community and the general public have come to recognise it—has shown how little we understand our own intelligence, and its role in our lives and culture. Here I attempt to address this problem of understanding, exploiting a variety of scientific evidence, including social simulation. I begin by reviewing current progress in AI, which is profound but underestimated. I suggest this lack of recognition is due to the mistaken belief that intelligence implies agency. I next examine the related question of human uniqueness: why do only we have language and extensive built culture? I use models and data to show that the propensities to use culture, share information and behave altruistically are neither unique to humans nor inexplicable to biology, but rather our uniqueness hinges on the extent of our capacities for communication and memory. Finally, I apply the impact of AI on extending our intelligence to these theories, to predict—and observe—consequences of AI on human societies and individual human lives. I make and support policy recommendations based on these predictions.
In the Industrial Age that humanity has entered long time ago with steam series has caused to primitive mechanization in production. With the development of internet and mobile technologies, electronics, nano technology, advances in medicine, health and digital applications and so on speed up mechatronics studies nowadays. Last World Economic Forum holds an important place on the agenda of Robotics and Artificial Intelligence and the economists like Roubini, Stiglitz also entered in the discussion of robotics and artificial in intelligence impacts on economics and business. Although Stephen Hawking criticized on the risks in this regard, every day we are witnessing tremendous news and articles in business pages, regarding on these topics and obviously corporate life and professionals can no longer resist to these changes. Changing form of the business terms and work forces, the way of doing business by using new technologies will have serious impacts on the daily business life and deriving from these on countries and on world economics. Many items and headlines such as jobless ratio, Philips Curve, performance, management, CRM Analytics, customer relationship management, sales, strategic planning, mass production, Purchasing Power Parity, GDP, inflation, money, Central Banks, Banking System, coaching, training, accounting, taxes etc. regarding to business and economics will face serious dangers, hits, change, exposures as well as opportunities and gains with the improvements in Artificial Intelligence and Robotics. One simple example can explain the degree of these impacts: Should we continue to make provisions for severance pay of the company's staff or should we calculate reserve for depreciation / amortization of robots in the company, which side of the balance sheet will be managed by human resources managers or shall we still name human resources ? This conceptual and hypothetical paper is aiming to address and discusses the future of robots, mechatronics and artificial intelligence in different perspectives.
Creativity research has a long history in psychology, focusing on individual differences in personality, cognitive abilities, and problem-solving styles. Creativity is considered to be a personal characteristic with features that include broad areas of interest and high energy levels. Considerable evidence indicates that employee creativity can fundamentally contribute to organizational innovation, effectiveness, and survival. So, creativity is important to organizations because creative contributions can not only help organizations become more efficient and more responsive to opportunities, but also help organizations adapt to change, grow and compete in the global market. Organizations could develop creativity by selecting individuals that are potentially creative, for instance based on assessment tools like Gough's Creative Personality Scale, or by training workers in cognitive skills like divergent thinking. We consider employee creativity as an ongoing process, not limited in time and space, and we envision a role for ergonomics to foster the creativity of all workers. For creativity to occur in organizations, managers need to support and promote it.
Games have amazing ability to hold people's attention for a long time, build relationship, win recognition and develop creativity. Games can be considered as a sample of motivation and job involvment, so now we are trying to apply these techniques into the labor process. In the following article gamification term is given and history of gamification theory is analyzed. Besides problems of application of gamification method in sector of national economy are examined. Also analysis of the possibilities of implementation this theory to the Russian reality is presented.
DOI: 10.5901/mjss.2015.v6n1s3p77
The Problem
We live in uncertain times, exacerbated by many direct and indirect effects of the global financial crisis. However, there is a lack of understanding about how human resource development (HRD) is responding to the global financial crisis or how HRD scholars and practitioners can best respond to these new realities and thus successfully ensure the future of HRD.
The Solution
To appreciate the future, it is critical to understand the past. Thus, this reflective piece outlines several factors that have caused, and continue to cause, uncertainty in the global economy, including structural changes in labor markets and developments in technology. A literature review revealed several HRD responses of firms to these new realities. These include technology-delivered instruction and social-learning tools. The article concludes by identifying characteristics of effective training and examines some distinctive practices of three firms that have been recognized as the global best in class for their leadership-development efforts.
The Stakeholders
Scholars and practitioners interested in the HRD, technology-delivered instruction, and social-learning fields.
Background
The economy is being lifted by the new concept of robotics, still we cannot be sure of all the possible benefits. And at the same time there are thoughts provoking the negativity of the idea. So, at this early stage, it becomes important to find out the possible benefits/limitations associated with it, so that the positives can be capitalized, established and developed further for the employment and motivation of employees in the healthcare sector, for the overall economical development. And also the negatives be further studied upon and mitigated.
Methods
The research study is an exploratory research, based on secondary data, such as, books on the related topics, websites, public websites of concerned departments for data and statistics, journals, newspapers and magazines, websites of health care providers and different printed materials (brochures, etc).
Results
The study found that the impact of robotics had both, a positive and a negative impact on the employment and motivation of employees in the retail sector. And also, the researchers unveiled that so far, there has been no substantial research done, in this sector, especially in the healthcare sector.
Conclusion
The research study concludes that replacing employees with robots is an inevitable choice for organizations in the service sector, more so in the health care sector because of the challenging and sometimes unhealthy working environments, but at the same time the researchers propose that it should be done in a manner that helps in improving the employment and motivation of employees in this sector.
This paper is focused on aspects of long-term innovativeness of multinational IT companies. Annually, there are several lists of the most innovative IT companies published, and many of IT companies are appearing in the first places for several years in a row. Our research is focused on those factors that enable these companies to maintain their innovativeness for several years. In order to identify the factors, we have compared investments into R&D of the four most innovative multinational IT companies and we conducted a survey about factors of corporate culture in IBM as one of the representatives of innovative multinational IT companies. Additionally, we reviewed business models of these companies in order to find out similarities and attitudes, which might have a positive influence on a long-term generation of innovations. The interview in the Innovation Centre of IBM allowed us to gain deeper insight into its processes. Thanks to our research we were able to identify specific factors of long-term innovativeness of multinational companies, which may be a source for further investigation as innovation management offers a wide range of perspectives since the field is continuously developing.
Purpose
This paper aims to clarify the notion of artificial intelligence (AI), reviewing the present scope of the phenomenon through its main applications. It aims at describing the various applications while assessing the markets, highlighting some of the leading industrial sectors in the field. Therefore, it identifies pioneering companies and the geographical distribution of AI companies.
Design/methodology/approach
The paper builds upon an in-depth investigation of public initiatives focusing mostly on the EU. It is based on desk research, a comprehensive review of the main grey and scientific literature in this field.
Findings
The paper notes that there is no real consensus on any definition for this umbrella term, that the definition does fluctuate over time but highlights some of the main changes and advances that took place over the past 60 years. It stresses that, in spite of the hype, on both the business and consumer sides, the demand appears uncertain. The scope of the announced disruptions is not easy to assess, technological innovation associated with AI may be modest or take some time to be fully deployed. However, some companies and regions are leading already in the field.
Research limitations/implications
The paper, based on desk research, does not consider any expert opinions. Besides, the scientific literature on the phenomenon is still scarce (but not the technical one in the specific research sectors of AI). Most of the data come from consultancies or government publications which may introduce some bias, although the paper gathered various, often conflicting viewpoints.
Originality/value
The paper gives a thorough review of the available literature (consultancies, governments) stressing the limitations of the available research on economic and social aspects. It aims at providing a comprehensive overview of the major trends in the field. It gives a global overview of companies and regions.
The world is currently enduring the fourth industrial revolution causing disruption on various economic and societal pillars. For Burrus (Brand Q Mag 27, [7]), such change is coming too fast and organizations that will leverage Artificial intelligence (AI) will profit the most. One of the sectors that will get disrupted by the introduction of AI will be higher education. From this point this conceptual paper propose a model for the implementation of AI through expert systems (ES) within the AACSB accreditation programs. ES are knowledge-based computer program that achieves human expertise in a limited domain (Res J Recent Sci 3(1):116–121, [14]). We tried to answer two main questions, whether AI can be implemented through ES and how such systems can reshape the AACSB accreditation process. We concluded that in fact ES will reshape such process while ensuring more reliable and efficient results and reducing time, cost and errors.
Purpose
The supposedly radical development of artificial intelligence (AI) has raised questions regarding the moral responsibility of it. In the sphere of business, they are translated into questions about AI and business ethics (BE) and corporate social responsibility (CSR). The purpos of this study is to conceptually reformulate these questions from the point of view of two possible aspect-changes, namely, starting from corporate social irresponsibility (CSI) and starting not from AIs incapability for responsibility but from its ability to imitate human CSR without performing typical human CSI.
Design/methodology/approach
The author draws upon the literature and his previous works on the relationship between AI and human CSI. This comparison aims to remodel the understanding of human CSI and AIs inability to be CSI. The conceptual remodelling is offered by taking a negative view on the relation. If AI can be made not to perform human-like CSI, then AI is at least less CSI than humans. For this task, it is necessary to remodel human and AI CSR, but AI does not have to be CSR. It is sufficient that it can be less CSI than humans to be more CSR.
Findings
The previously suggested remodelling of basic concepts in question leads to the conclusion that it is not impossible for AI to act or operate more CSI then humans simply by not making typical human CSIs. Strictly speaking, AI is not CSR because it cannot be responsible as humans can. If it can perform actions with a significantly lesser amount of CSI in comparison to humans, it is certainly less CSI.
Research limitations/implications
This paper is only a conceptual remodelling and a suggestion of a research hypothesis. As such, it implies particular morality, ethics and the concepts of CSI and AI.
Practical implications
How this remodelling could be done in practice is an issue of future research.
Originality/value
The author delivers the paper on comparison between human and AI CSI which is not much discussed in literature.
Purpose
How to keep employees engaged, rewarded and retained to improve the end-user and customer experience.
Design/methodology/approach
This study provides practical advice on how a business can attract, recruit, retain and develop talented employees to improve customer service, using the author’s experience at Genesys.
Findings
This study explores various research findings including Coleman Parks to establish the challenges organizations face when trying to ensure employees are engaged with their own business.
Originality/value
This is my viewpoint on the situation; I have researched various areas of employee engagement to understand why organizations are facing challenges, and I can provide unique insight as we work with world-leading brands to use technology that will solve these problems in some way at the least.
Purpose
This paper aims to discuss the rationale, theoretical foundation, application, and future of artificial intelligence (AI)-based training.
Design/methodology/approach
A review of relevant research papers, articles and case studies is done to highlight developments in research and practice.
Findings
AI-based training systems are smart, intelligent and expert in handling queries. These systems can curate content, grade, evaluate, and provide feedback to trainee, thus making learning adaptive and contextual.
Practical implications
Application of AI is vital in the field of training, as it helps personalization and customization of training programs to increase the effectiveness of training.
Originality/value
Executives and researchers can save time by reading relevant information on the linkage, and its contribution to AI is discussed and summarized in an easy to read format.
Purpose
This paper aims to review the applications of artificial intelligence (AI) in the hiring process and its practical implications. This paper highlights the strategic shift in recruitment industry caused due to the adoption of AI in the recruitment process.
Design/methodology/approach
This paper is prepared by independent academicians who have synthesized their views by a review of the latest reports, articles, research papers and other relevant literature.
Findings
This paper describes the impact of developments in the field of AI on the hiring process and the recruitment industry. The application of AI for managing the recruitment process is leading to efficiency as well as qualitative gains for both clients and candidates.
Practical implications
This paper offers strategic insights into automation of the recruitment process and presents practical ideas for implementation of AI in the recruitment industry. It also discusses the strategic implications of the usage of AI in the recruitment industry.
Originality/value
This article describes the role of technological advancements in AI and its application for creating value for the recruitment industry as well as the clients. It saves the valuable reading time of practitioners and researchers by highlighting the AI applications in the recruitment industry in a concise and simple format.
In the following , the various methods and technologies are briefly outlined and explained.
Artificial intelligence (AI) has catered for an immense leap in development in business practice. AI is also increasingly addressing administrative, dispositive and planning processes in marketing, sales and management on the way to the holistic algorithmic enterprise.
“Data is the new oil” is a saying that is readily quoted today. Although this sentence still describes the current development well, it ides not get down to the real core of the matter; more suitable would be “artificial intelligence empowers a new economy”. The autonomous automation of ever larger fields of tasks in the business world will trigger fundamental economic and social changes. Based on a future world in which unlimited information is available on unlimited computers, ultimate decisions will be generated in real time and processes will be controlled objectively. These decisions are not liable to any subjectivity, information or delays.
This chapter provides a brief introduction to artificial intelligence – presents a basic concept of AI and describes its relationship with machine learning, data science, data mining and predictive analytics. The chapter also deals with other related issues such as ethics and privacy.
This chapter deals with observations and insight – on employing AI solutions in business. Without finding a problem to solve, business will not gain the desired benefits when employing AI. If they are looking for a solution to detect anomalies, predict an event or outcome, or optimize a procedure or practice, then they have a problem AI can address. The chapter begins with unfolding analytics landscape and describes how to embed AI in business processes. Further, it discusses potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes.
The previous chapter was devoted to the most significant concepts, methods and technologies of artificial intelligence (AI). This gives grounds for the presentation of influence which these systems might have on the contemporary organizations and markets.
A number of recent high-profile studies of robotics and artificial intelligence (or AI) in economics and sociology have predicted that many jobs will soon disappear due to automation, with few new ones replacing them. While techno-optimists and techno-pessimists contest whether a jobless future is a positive development or not, this paper points to the elephant in the room. Despite successive waves of computerization (including advanced machine learning), jobs have not disappeared. And probably won’t in the near future. To explain why, some basic insights from organization studies can make a contribution. I propose the concept of ‘bounded automation’ to demonstrate how organizational forces mould the application of technology in the employment sector. If work does not vanish in the age of AI, then poorly paid jobs will most certainly proliferate, I argue. Finally, a case is made for the scholarly community to engage with wider social justice concerns. This I term public organization studies.
This chapter concentrates on how information and communication technologies can support knowledge work. After analysing the needs of knowledge workers requirements for ICT systems are formulated using newer technologies that can capture, analyse and share experience and collaborate without extra human intervention. The concept of the high performance workplace is introduced and functionalities of advanced ICT systems are explained. The reader also learns about success factors for ICT implementation. The KM Tool presented at the end of the chapter are knowledge taxonomies and maps.
Artificial Intelligence can be implemented as a stand–alone capability but will often be used in conjunction with other technologies, such as Robotic Process Automation (RPA), Cloud Computing and the Internet of Things (IoT). This chapter explains what these associated technologies are and why they are important to anyone implementing AI. It provides examples of where businesses have used a combination of them as part of their overall automation strategy and the benefits they have achieved. In particular, it will look at how the benefits from RPA can be significantly enhanced through the additional deployment of AI. It will also cover the benefits and risks of using AI ‘in the Cloud’.
This handbook provides a comprehensive and unparalleled reference point for studying continuous business transformation. Asserting that change will be the new normal and highlighting the fact that business transformation can never be complete, this important resource is a tool for coping with ongoing change in order to become and stay resilient, the predominant concern of executives across industries. Containing case study material to illustrate issues and solutions, The Palgrave Handbook of Managing Continuous Business Transformation takes an interdisciplinary approach weaving together strategic concepts with real-life experiences, connecting human resource issues with shifts in information technology and linking customers with the businesses from which they buy. Structured into four parts; transformational shifts, achieving customer centricity, dealing with new technology and leading the change, this handbook is crucial reading for academics, scholars and practitioners of business transformation.
In determining the type and measure of punishment to be imposed on the offender, the court is guided by the general purposes of punishment. The court is expected to assess and evaluate each case in this context, based on two types of considerations: data about the offense (in rem) and the about the offender (in personam). When the court evaluates these two types of data, it does so through the prism of the general purposes of punishment. In the modern criminal law there are four accepted general purposes of punishment. The four purposes are retribution, deterrence, rehabilitation, and incapacitation. These purposes are discussed below.
Recent technological advances in big data, machine learning, and robotics, have begun to have a negative influence on existing employment opportunities for human beings. Numerous studies have demonstrated a worrisome decline in low- and medium-income employment resulting from the replacement of human workforce with machines. A seminal study by Frey and Osborne in 2013 predicted that 47% of the 702 examined occupations in the United States faced a high risk of decreased employment rate within the next 10–25 years as a result of computerization. Despite the seemingly dystopian future foreshadowed by these numbers, the wholesale replacement of labor by machines will most likely not become a reality in the foreseeable future for an array of reasons, including the creativity required by many occupations and interventions by governments.
However, despite the barriers, computerization is likely to have a significant effect on the current market. In this paper, we aim to track the relative quantities of jobs that are either susceptible or non-susceptible to computerization in the future, by developing and utilizing an analytical model using Markov chains. Various simulations performed using this model demonstrate the importance of intervention policies, such as improved technical education of the public, in controlling the rate of computerization. Moreover, the simulations identify the probable creation of new jobs that would facilitate new human employment. Although radical changes in technology and economy await humanity, adequate preparation will help to facilitate a smoother transition into the age of computers.
Digitalization has opposing effects on labor markets. Although the overall pie might grow bigger, severe structural changes and therefore challenges for society at large will definitely occur. For this reason, we want to address the following questions: How will digitalization change the division of work? Which jobs are at stake? And will demography be a factor in this regard? In economic textbooks, we normally assume that new technology drives growth and therefore has also a net positive impact on employment. For the past, this was certainly true, as the replacement of the typewriter by personal computers still required a person behind a desk, which now however could offer more and better services. This relationship between technology and the labor market might be about to change in our digitalization era. Already today, some news are written by the computer itself—without human fingers typing. The new feature of this technological change is therefore that not only muscle but also brain work will be replaced by robots—given it is not only technological feasible but also cost-efficient. In addition to the general change by digitalization of work processes, it could be the case that societies have to react differently to this ongoing process given their demographic transition and their education system. Research suggests that the conflict will not only evolve between capital and labor but also between young and old workers, as rationing will disproportionately affect the young. Perplexedly, it might even be the case that rapid aging countries like Japan or Germany will have less problems regarding the labor market.
The need for self-expression influences individuals in their preferences for goods and how they obtain them. One way for individuals to express themselves is through the prosumption of original unique goods. It is explained how the traditional trade-off between the originality and economy of such goods can be addressed through applications of artificial intelligence. However, although applications of artificial intelligence can transcend the originality/economy trade-off, they cannot transcend differences between cultures that value scarcity and cultures that value sharing. Nonetheless, applications of artificial intelligence can expand human self-expression because they can reduce barriers, such as lack of production skills, to prosumers realizing their own original ideas. With reference to technologies’ cultural domestication, it is explained that rather than artificial intelligence being either a miracle or a monster, the potential of artificial intelligence is mediated by multiple considerations.
Recently “skill-based”, “human-centred” or “anthropocentric systems” (APS) approaches have formed the basis for the design of alternative forms of production systems which place a central emphasis on the importance of developing more humane work practices and organisational systems. New working innovations such as autonomous groups, quality circles and networking, not only highlight the inadequacy of hierarchical management structures, they also show the inadequacy of the divisive and exclusive nature of training and education structures for meeting the skill needs of the modern manufacturing industry.
In the first few chapters of the book, we have discussed the theories and concepts of knowledge management. A deeper understanding of the knowledge generating process in an economic or managerial environment will make it easier for the manager to introduce knowledge management within a company. The previous chapters described real life examples of different size, complexity and focus which all had to do with knowledge management in a managerial context. These examples gave some insight into the possibilities of knowledge management. This chapter deals with the corporate “environment” necessary to create the knowledge based company. If a company understands the crucial role which knowledge plays for its future sustainable development, how can it tackle the introduction of knowledge based management. As is argued in this chapter, the mentality of management (the learning attitude) and the support architecture (information and knowledge technology) of the company make or break knowledge management.
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupations probability of computerisation, wages and educational attainment.
From the Publisher:
Organizational Learning and Knowledge Technologies in a Dynamic Environment presents practical and concrete ways for companies to be adaptive and flexible whilst operating effectively and efficiently. The foundational concepts discussed emerge from such fields as neurobiology, cognitive sciences, physics, and organizational theory. These are applied to management and substantiated with examples. New developments in knowledge management, such as connectionist approaches, are outlined and illustrated with real cases.
When properly applied, AI technology can offer important advantages. But many issues must be tackled on the way to a successful implementation.
Purpose
– The purpose of this paper is to advance the view that having employees who are thoroughly motivated and truly engaged is the most powerful competitive weapon an organization can enjoy, and to offer advice on how to achieve this condition.
Findings
– The paper highlights the importance of 14 non‐financial ways of engaging the employee, and offers a ten‐point plan for achieving them.
Practical implications
– The paper shows that engaging talented people needs to be a top organizational priority because they are by definition precious possessions who are particularly likely to find another job if they do not feel that their current one is giving them satisfaction, purpose and sense of self‐worth.
Originality/value
– The paper argues that companies are most likely to be successful when their employees provide large amounts of discretionary effort.
An analysis of the productions and rules in the way they are used in artificial intelligence systems is presented. The proposed new definition for productions refers to a large number of types of productions which may be found in the literature on AI systems. This definition emphasizes in the most general way those production components which are important both for theory and for practice and which for some reasons remained unnoticed by many researchers. These components are implemented in a theoretical formalism which concludes the paper.