Digital Transformation Journey of HR

2022 ◽  
pp. 94-115
Author(s):  
Tuğba Karaboğa ◽  
Hasan Aykut Karaboğa ◽  
Dogan Basar ◽  
Songul Zehir
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Human Resources ◽  
Digital Transformation ◽  
Digital Culture ◽  
Digital Workplace ◽  
Digital Platforms ◽  
Digital Management ◽  
Employee Experience

Big data and artificial intelligence (AI) technologies have changed how we live, how we work, and how we organize businesses. Thus, it is no surprise that it is also changing how we manage human resources (HR). For HR leaders, digital transformation is a very hot topic, having the potential to create high value for businesses. First, HR can transform all functions, processes, and systems by leveraging digital platforms and applications. Second, HR can lead business digitalization, enabling a compelling employee experience where a digital culture, a digital workplace, and digital management are welcomed. To provide a more pragmatic perspective, this chapter discusses digitalization of HR with big data and artificial intelligence (AI) technologies and identifies key digital HR strategies and roles needed to sustain the digital transformation. Also, this chapter presents the advantages of digital HR and the basic pitfalls HR faces in the digital transformation of HR.

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

Augment Human Resource Proximity Plumbing Cybernation

2021 ◽  
pp. 131-140
Author(s):  
Anant Deogaonkar ◽  
Sampada Nanoty ◽  
Archana Shrivastava ◽  
Geetika Jain
Keyword(s):  
Artificial Intelligence ◽  
Human Resources ◽  
Human Resource ◽  
Work Culture ◽  
Technological Transformation ◽  
The People ◽  
Human Energy ◽  
Technological Interventions ◽  
Business Acumen ◽  
Employee Experience

The expeditious proliferation of artificial intelligence in the mainstream has rejigged the simplest processes of the various sectors in the most efficient way. With the advent of the era of cybernation, the work culture has been curbed with the timely developments and upgradation of the technology. Cybernation has propelled the growth of every respective sector of the vast corporate diaspora with time. The main aim of the cybernation being that of smoothening the complex, bulk tasks which exploit mass human energy, has seen much success in its purpose so far. But certain domains of the corporate diaspora still await the technological transformation of their respective processes. One such prominent domain and the real fuel of the corporate diaspora, the human resource has yet to expand its purview to imbibe and imbue cybernation in its certain processes. Human resource domain being the custodian of the corporate, wherein it is for the people and by the people though with the niche of Industry 4.0 beholds more space to expand the angle of understanding the term resource for the human, than human as an element of resource in itself. Multifarious human resource processes can be enhanced further with apt utility of digitization in order to optimize the user interface and user experience, boosting the overall employee experience amidst the corporate. Several certain customary functions of the human resources entail the adaptation of automation in more nuanced way to evolve parallel with the digitalization. Moreover, the millennial era further looks up to a transformed human resource with higher echelons of functions to be performed, digitally evolved jobs, an automated work environment, work culture well acquainted with the artificial intelligence. The effect of cybernation on the business acumen of futuristic human resource leaders, working in the rapid concurrent era of disruptions, without losing the human touch, will carve the future human resource structure. Therefore, the intent of this chapter is to study the detailed implications of automation, digitalization, and cybernation in the domain of human resources and to study and examine the dynamically changing HR functions with technological interventions and disruptions by proposing a literature review.

scholarly journals Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?

2020 ◽  
Vol 6 (4) ◽  
pp. 187 ◽  
Author(s):  
Tan Yigitcanlar ◽  
Nayomi Kankanamge ◽  
Massimo Regona ◽  
Andres Ruiz Maldonado ◽  
Bridget Rowan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Urban Planning ◽  
Methodological Approach ◽  
Digital Transformation ◽  
Social Media Analytics ◽  
Urban Context ◽  
Banking And Finance ◽  
Urban Planning And Development ◽  
Artificial Creativity

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.

scholarly journals Development of the Logistics 4.0 Concept in the Digital Economy

2020 ◽  
pp. 85-96
Author(s):  
N. Trushkina ◽  
◽  
H. Dzwigol ◽  
O. Serhieieva ◽  
Yu. Shkrygun ◽  
...  
Keyword(s):  
Big Data ◽  
Customer Service ◽  
Industry 4.0 ◽  
Business Processes ◽  
Digital Technologies ◽  
Digital Economy ◽  
Digital Transformation ◽  
Digital Platforms ◽  
Delivery Times ◽  
Sustainable Logistics

The transition to a digital economy is becoming a key driver of GDP growth. This is due not only to the effect obtained from the automation of existing processes, but also from the introduction of new, breakthrough business models and technologies, including digital platforms, digital ecosystems, in-depth analytics of big data, Industry 4.0, Logistics 4.0. At the same time, digital transformation is seen as a radical change in the complex of business processes, from product development to customer service, as well as the introduction of modern digital technologies in the organization of business processes in enterprises. The purpose of the article is to analysis the features and trends of organizing logistics activities in the context of digital transformation of business processes; research of the main prerequisites for the formation of the Logistics 4.0 concept; determination of priority directions for its further development in the context of Industry 4.0. Based on the generalization of scientific approaches, the definition of the concept of "Logistics 4.0" has been clarified, which means the modern paradigm of managing logistic (material, financial, information, transport) flows and organizing a complex of logistics activities (purchase and delivery of material resources, warehousing, production, stock formation, recycling of industrial waste, customer service, transportation and sale of finished products) using breakthrough digital technologies and information systems. The priority areas of organizing the logistics activities of enterprises using digital technologies include the following: multichannel logistics; logistics marketplaces; rethinking the use of packaging; mass personalization; Silver Economy (new services for older clients and new opportunities for older workers); sustainable logistics; sharing economy; multi-supply; customer experience; smart containerization; big data analytics; augmented and virtual reality; cloud service applications and APIs; Internet of Things; robotics and automation; new generation wireless communication; blockchain; Artificial Intelligence; unmanned aerial vehicles or "drones"; 3D printing; unmanned vehicles; quantum computing; supergrid logistics; space logistics; the use of digital platforms that unite customers and transport and logistics companies (the parties can enter into digital contracts, exchange transport booking requests and electronic documents, control the delivery of goods in real time). All this can help to reduce costs by optimizing procurement; decrease in personnel costs and decrease in labour costs as a result of automation; reduction of errors in logistics; optimization of the supply process; efficient warehouse management; forecasting shipments; creation of optimal routes; operational planning of loads and control of delivery times; ensuring product delivery on time, improving customer loyalty; optimal interaction with customers on the "last mile".

scholarly journals Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

2019 ◽  
Vol 8 (1) ◽  
pp. 382
Author(s):  
Rahul Badwaik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Setting ◽  
Digital Transformation ◽  
Current Status ◽  
Healthcare Industry ◽  
Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

scholarly journals DIGITAL BANKING REFLECTION AND ZAKAT ACCOUNTABILITY SHARIA BANKING IN INDONESIA

2019 ◽  
Vol 17 (2) ◽  
pp. 264-279
Author(s):  
Pertiwi Utami
Keyword(s):  
Artificial Intelligence ◽  
Human Resources ◽  
The Other ◽  
Islamic Banking ◽  
Islamic Banks ◽  
Islamic Bank ◽  
Technological Advances ◽  
Digital Management ◽  
Potential Sources

ABSTRACT Digital banking and the accountability of good zakat can increase interest in paying zakat on the benchmarking of sharia. On the other hand, technological advances such as the use of artificial intelligence make the role of human resources shifted. Even though human resources (labor) are one of the potential sources of zakat revenue. Zakat literacy and interest in paying zakat are also low in Islamic banking. The researcher did not find zakat data in statistical reports on Islamic banks nationally. It seems that it was only found in the presentation of reports about the sources and uses of private Islamic bank zakat funds. This can lead to the perception that Islamic banks do not optimally manage zakat. Through literature studies, researchers provide a solution to how zakat management can be done to increase interest in paying zakat but can still maintain the use of human resources (work). The conclusion obtained is that interest in paying zakat on Islamic banks cannot be maximally realized if it is not supported by internal efforts. Efforts that can be made are transparency of zakat reports, increased literacy, acceleration and optimization of digital management of Islamic bank zakat.

Methodology for cognition of digital society

2020 ◽  
Vol 3 (2) ◽  
pp. 17-26
Author(s):  
N. N. Meshcheryakova
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Research Methods ◽  
Large Volume ◽  
Digital Transformation ◽  
Digital Society ◽  
Modern Information ◽  
Short Time

Digital sociology is a computational social science that uses modern information systems and technologies, has already formed. But the conflict with traditional sociology and its research methods has not yet been resolved. This conflict can be overcome if we remember that there is a common goal – the knowledge of the phenomena and processes of social life, which is primary in relation to the methods to be agreed upon. Digital transformation of sociology is essential, since 1) traditional sociological methods do not solve the problem of providing voluminous, reliable empirical data qualitatively and in a short time; 2) the transition from contact research methods to unobtrusive ones is in demand. The adaptation of four modern information technologies-cloud computing, big data, the Internet of things and artificial intelligence – for the purposes of sociology provides a qualitative transition in the methodology of knowledge of the digital society. Cloud computing provide researchers with tools, big data – research materials, Internet of things technology aimed at collecting indicators (receiving signals) in large volume, in real time, as direct, not indirect evidence of human behavior. The development of “artificial intelligence” technology expands the possibility of receiving processed signals of the quality of the social system without building a preliminary hypothesis, in a short time and on a large volume of processed data. Digital transformation of sociology does not mean abandoning the use of traditional methods of sociological analysis, but it involves expanding the competence of a sociologist, which requires a revision of University curricula. At the same time, combining the functions of an expert on the subject (sociologist) and data analyst in one specialist is assessed as unpromising, it is proposed to combine their professional competencies in working on unified research projects.

An Empirical Analysis of Artificial Intelligence, Big Data and Analytics Applications in Exploration and Production Operations

2021 ◽  
Author(s):  
Armstrong Lee Agbaji
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Empirical Analysis ◽  
Oil And Gas ◽  
Oil Industry ◽  
Digital Transformation ◽  
Deep Dive ◽  
Exploration And Production ◽  
Oil And Gas Operations

Abstract Oil and Gas operations are now being "datafied." Datafication in the oil industry refers to systematically extracting data from the various oilfield activities that are naturally occurring. Successful digital transformation hinges critically on an organization's ability to extract value from data. Extracting and analyzing data is getting harder as the volume, variety, and velocity of data continues to increase. Analytics can help us make better decisions, only if we can trust the integrity of the data going into the system. As digital technology continues to play a pivotal role in the oil industry, the role of reliable data and analytics has never been more consequential. This paper is an empirical analysis of how Artificial Intelligence (AI), big data and analytics has redefined oil and gas operations. It takes a deep dive into various AI and analytics technologies reshaping the industry, specifically as it relates to exploration and production operations, as well as other sectors of the industry. Several illustrative examples of transformative technologies reshaping the oil and gas value chain along with their innovative applications in real-time decision making are highlighted. It also describes the significant challenges that AI presents in the oil industry including algorithmic bias, cybersecurity, and trust. With digital transformation poised to re-invent the oil & gas industry, the paper also discusses energy transition, and makes some bold predictions about the oil industry of the future and the role of AI in that future. Big data lays the foundation for the broad adoption and application of artificial intelligence. Analytics and AI are going to be very powerful tools for making predictions with a precision that was previously impossible. Analysis of some of the AI and analytics tools studied shows that there is a huge gap between the people who use the data and the metadata. AI is as good as the ecosystem that supports it. Trusting AI and feeling confident with its decisions starts with trustworthy data. The data needs to be clean, accurate, devoid of bias, and protected. As the relationship between man and machine continues to evolve, and organizations continue to rely on data analytics to provide decision support services, it is imperative that we safeguard against making important technical and management decisions based on invalid or biased data and algorithm. The variegated outcomes observed from some of the AI and analytics tools studied in this research shows that, when it comes to adopting AI and analytics, the worm remains buried in the apple.

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