Big Bang Based Decision Automation

Performance evaluation (also measurement) provides a mechanism to ensure that organisations thrive in today’s complex and dynamic business environment. Performance evaluation is widely used for not only interpreting present performance but also for examining the future production output. An understanding of the ‘future’ is reliant on ‘prediction’, which should consider potential uncertainties as a result of the change of time or the variations between organisations, sectors or regions. However, the development of the epistemology of prediction has been overlooked within the context of performance evaluation. This paper develops and expands an existing paradigm of prediction by addressing the multi-dimensional observation for predicting an organisation’s production performance under the auspices of evaluation and specifically in the context of a construction organisation that delivers infrastructure projects, which are acknowledged as being dynamic, uncertain and complex. Three advanced econometric models are applied by using the production-related data provided by a construction organisation to test the developed paradigm. The managerial implications of the research are discussed to ensure their relevance to practice. The empirical study presented in this paper provides a significant contribution to improving the practice of performance evaluation, which is essential for construction organisations to ensure positive business outcomes can be achieved.

Objective Determining impact which Artificial Intelligence, Robotization and Automation have and will continue to have on the labour market in the processing industry. Method Literature review, statistical data and data from industry reports. Deduction and comparative analysis. Results In the course of the work, a review of the literature on the subject was carried out and the issue of artificial intelligence, as well as robotization and automation of production was defined, and in addition, it has been demonstrated how important factor of production is labour. It presents examples of the use of artificial intelligence in the modern economy and presents the probability of replacing human work by artificial intelligence systems. Data from research reports and statistical analyses were used in the context of labour market analysis in industries and economies with the highest degree of automation and robotization. Conclusions were presented, which implement the set objective and are the basis for conducting research in the areas indicated by the authors. In the course of work, we defined what the labour is, its productivity and measurement methods. Data from research reports were presented, showing the probability of changes in the labour market in relation to specific occupations, due to the use of artificial intelligence. The relationship between artificial intelligence and process automation and robotization is described. The above-mentioned studies allowed the authors to demonstrate some important relations between the described factors. Keywords: labour productivity, artificial intelligence, robotization and automation JEL Classification: J01, O12, O30

Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior – calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer.

Under the wave of the age of digital intelligence, the teaching of the course "Capital Operation" ushers in the opportunity of change and many challenges. This study takes digital intelligence as the core and explores the teaching reform practice of the course in depth. On the one hand, Course team fully integrate big data, artificial intelligence and other cutting-edge digital technologies to create a rich and diversified teaching resource library and platform, which provides a large amount of accurate and practical teaching materials and cases for the course, and helps students understand the theoretical essence and practical points of capital operation in depth and intuitively. On the other hand, with the help of digitalized teaching tools, the teaching methodology has been innovated to stimulate students' enthusiasm and participation. In addition, Course team emphasize the organic integration of ethical and moral education and professional education in the course teaching, and in the process of teaching professional knowledge and skills of capital operation, Course team guide students to think deeply about the ethical and moral norms and social responsibilities in capital operation activities, and cultivate their professional ethics of adhering to the moral bottom line and following the legal norms in the complex business environment. Practice has proved that Digital Intelligence EmpoCourse teamrment has not only promoted the innovation of the teaching mode and the improvement of the teaching quality of the Capital Operation course, but also facilitated the synergistic development of students' professionalism in capital operation, their ability to apply digital intelligence and their ethical and moral qualities.

This paper explores the use of big data to measure company culture, good and bad, including toxic culture. Culture is a central factor driving employee experiences and contributing to the “great resignation”. Harnessing the key Artificial Intelligence (AI) technology of neural networks using deep learning methodology for NLP provides the capability to extract cultural meanings from a diverse array of organizational information and cultural artefacts ( texts, images, speech and video) available online. Using big data and AI provides a predictive capability surpassing the value of employee survey instruments of the last century providing a rear view of insights. Big data helps break free from the paradigm of only thinking about culture moving at a glacial pace. An innovative methodology and AI technologies help measure and visually plot the organizational culture trajectory within a company cultural landscape. Cultural values, inclusive of toxicity, have the potential for detection across all forms of communications media. A non-invasive approach using a broad range of open data sources overcomes limitations of the traditional survey instruments and approaches for achieving a culture read. The benefits of the approach and the AI technology are the real-time ingestion of ongoing executive and managerial feedback while entirely sidestepping the issues of survey biases and viable samples. The methodology under study for reading a culture moves well beyond traditional text-centric searches, content analyses, dictionaries and text mining, delivering an understanding of the meanings of words, phrases, sentences or even concepts comprising company culture. Embeddings are an ideal neural network breakthrough technology enabling the computation of text as data through creating a meaningful space in which similar word meanings exist in close proximity. Vector algebra in a multidimensional space helps unpack the cultural nuances and biases pent up within the unstructured information flowing through and from organizations, from tweets to text-centric corporate communications, including annual reports. This modelling enables predictions about an organizational future culture based on communication data existing across internal and external digital platforms. The variety of communications represents the twenty-first century culture requiring exploration and discovery. Visualizations of the traces of multiple dimensional cultures make current-state and culture predictions for an organization and competitive organizations in the same or adjacent industries within a company cultural landscape.

The Industrial Revolution 4.0, marked by the accelerated adoption of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data, and other technologies, has significantly impacted various business sectors. One sector that has been fundamentally affected is accounting, which has had to adapt to these changes to remain relevant and efficient in an increasingly complex and dynamic business environment. This study aims to understand and analyze how digital transformation in the Industrial Revolution 4.0 era has affected the field of accounting. The research method used is descriptive-qualitative research. This research reveals that digital transformation in accounting, supported by technologies such as IoT, artificial intelligence, big data, and others, brings operational efficiencies through automation, increases the accuracy of financial data, and drives deep data analysis. Collaboration and communication are also enhanced through a cloud-based platform. However, data protection and privacy are important issues in the digital era. In the Industrial Revolution, 4.0, technologies such as big data analytics, AI, IoT, cloud computing, and blockchain are bringing fundamental changes, enabling accounting to be more proactive, using data analysis for better predictions and customer service, and more intelligent and responsive decision making. Digital transformation continues to change the landscape of the accounting and financial industry, making careful use of the potential of the digital age.

The rapid evolution of artificial intelligence (AI) is revolutionizing knowledge ecosystems, reshaping how knowledge is created, shared, and applied in transformative ways. This study delves into the powerful synergy between human expertise and AI, illustrating how computational intelligence amplifies decision-making and sparks groundbreaking innovation in complex and data-rich business environments. Drawing from a systematic literature review of 101 scholarly articles, we synthesize critical insights and propose a unifying framework that weaves together the socio-technical, ethical, and policy dimensions of AI integration. Our findings highlight the centrality of human-centered design, seamless cross-contextual integration, and equity-driven solutions, while revealing the critical roles of task complexity, cognitive alignment, and organizational readiness in mediating and moderating AI–human collaboration. By bridging fragmented perspectives, this study advances theoretical understanding and illuminates practical pathways for leveraging AI to augment human ingenuity, uphold ethical imperatives, and catalyze innovation in rapidly shifting knowledge landscapes.

This study examined the effect of Artificial Intelligence (AI) on entrepreneurial success of Small and Medium Enterprises (SMEs), Eket, Akwa Ibom State. However, the specific objectives were to examine the effect of AI rarity and AI utilization efficiency on entrepreneurial success of SMEs, Eket, Akwa Ibom State. This study adopted a survey research design and utilized primary data collected from a pre-selected population which included SMEs that had already integrated or were in the process of integrating AI technologies in their business operations, in Eket, Akwa Ibom State. The data collected were analyzed using descriptive statistics, analysis of variance and multiple regression analysis via SPSS 25.0 statistical package. The study’s findings revealed that AI rarity has a significant positive effect (r= 0.063{p=0.008<0.05}) on entrepreneurial success of SMEs in Eket, Akwa Ibom State while AI utilization efficiency has a significant positive effect (r= 0.442{p=0.001<0.05 on entrepreneurial success of SMEs in Eket, Akwa Ibom State. It was thus concluded that AI power exerts a significant effect on entrepreneurial success of SMEs in Eket, Akwa Ibom State at 5% level of significance. The study recommended, amongst others, that the SMEs in Eket should ensure that they take the time to do the research and educate themselves on how to properly use AI technologies to provide services or create value in ways that make them stand out from the competition.

Artificial intelligence and industrial innovation: Evidence from German firm-level data

The purpose of the article is to examine innovative artificial intelligence technologies that allow for the independent creation of artistic images. The research methods are based on a systematic approach to studying the problem of application and implementation of artificial intelligence technologies in artistic practices. The scientific novelty of the obtained results lies in actualizing the problem of using artificial intelligence technologies in the sphere of culture and art, and in establishing the symbiosis of artificial intelligence technologies in artistic practices. The use of machines to create works of art is rapidly gaining popularity, and the transition to practical implementation is underway. Today, it is possible to train artificial intelligence in artistic practices. This applies to simplifying the process of creating samples for the general public and applying it to restoration, renovation, and fundamentally new ways and opportunities for professional artists and designers. Artificial intelligence is used in the arts to create works of art, analyze and interpret art, and interact with viewers. Conclusions. The ways of using artificial intelligence in artistic practice have been analyzed. By analyzing examples of the use of this technology in the visual arts, the basic principles of working with artificial intelligence have been identified. The existing methods of working with images using artificial intelligence and some platforms and programs that allow creating visualizations using artificial intelligence are considered. Some advantages and disadvantages of using artificial intelligence tools in the arts have been highlighted. In particular, the benefits include new opportunities, automation, speed and accuracy, and a wide range of possibilities. Among the disadvantages of using artificial intelligence tools in art are the lack of individuality, uncertainty of results, and the inability of art to interact with the viewer. It has been stated that artificial intelligence and the artist are co-authors in the field of art.

This study aims to systematically analyze the educational issues and challenges arising from the rapid diffusion of artificial intelligence (AI) technologies in secondary education and to derive implications for their future application. To this end, the study synthesizes findings from international and domestic research literature, policy documents, and empirical studies to examine the impacts of AI adoption across multiple dimensions of secondary education, including curricular structure, instructional practices, performance-based assessment, student guidance and counseling, teachers’ professionalism and identity, teachers’ work structure, educational inequality, school facilities and infrastructure, and educational governance. The analysis indicates that AI can serve as an effective cognitive trigger during the exploratory and expansion phases of learning by supporting information access and idea generation. However, when AI intervenes in core stages such as concept formation, value judgment, and meaning construction, it poses a significant risk of weakening students’ cognitive agency and undermining teachers’ educational judgment. In particular, in the domain of performance-based assessment, AI use tends to reinforce outcome-oriented learning, thereby distorting the fundamental purpose of process-oriented evaluation. In student guidance and counseling, the findings further demonstrate that AI technologies cannot replace relational, empathetic, and trust-based educational interventions. Moreover, while AI holds the potential to enhance teachers’ professional capacity by supporting instructional design and learning diagnosis, the analysis reveals a parallel risk: under technology-driven operational models, teachers may be reduced from educational decision-makers to system operators. Based on these findings, this study argues for a paradigm shift in AI integration—from technology-centered implementation to education-centered judgment. It emphasizes the principle that AI should function as a tool for triggering learning processes, while educational judgment and responsibility must remain firmly with teachers. This principle should be institutionally embedded across curriculum design, assessment systems, school operations, and educational governance.

Abstract. Introduction. The new socio-economic reality that emerged during the COVID-19 pandemic crisis has forced modern business to adapt all its commercial processes, including the recruitment process, to “new norms”. Virtual communications are now becoming a daily standard since most companies face the impossibility to implement its recruitment processes in traditional ways successfully. This means that remote recruitment will become a new norm for implementation in the process of personnel management. Therefore, there is a need to find and examine the best digital forms and tools for remote recruitment during COVID-19 and in the post-pandemic period. Purpose. Despite the scientific achievements in the study of innovative and digital technologies of recruitment, there are no answers to the questions “How exactly should the recruitment process be implemented remotely?” and “What innovative recruitment technologies can ensure its effectiveness in the virtual digital environment?” All of this determines the need and relevance of the study. Therefore, the aim of the article is to define conceptual and applied foundations for remote recruitment implementation in virtual digital environment within the context of social distancing which is associated with the COVID-19 pandemic. Results. The article examines some innovative technologies for the remote recruitment process in terms of social distancing. The main tendencies of remote recruitment have been analyzed; the directions of recruitment process transformation in time of social distancing have been identified; recommendations for further transformation of the remote recruitment process have been developed. The article generalizes types of software for remote recruitment and forms a digital ecosystem model for remote recruitment, which includes digital tools for recruitment process automation, candidates attracting platforms, social recruitment technologies, artificial intelligence, and robotics tools. The main challenges and possible answers to digitalization and remote recruitment in the pandemic and post-pandemic periods have been validated. Conclusions. The remote format has significantly transformed existing trends in the recruitment process. These trends will shape new methods of attracting, selecting, and recruiting job candidates. Analysis of the methods and their peculiarities allowed us to conclude that its effective implementation in the digital environment depends on innovative technologies, namely: collaborative recruitment, marketing recruitment, employer branding, transfer (soft) skills, artificial intelligence, and selection. On the other hand, selection should consider, among others, socio-cultural diversity, candidate databases, digital selection, selection of project staff, recruitment based on big data, flexibility, and involvement of generation. To be effective, the recruitment process may use online advertising on job portals, artificial intelligence based on screening algorithms and resume filtering to select promising candidates, online skills testing, communication via e-mail messengers and recruitment chatbots, video interviewing. The following sequence of subprocesses is optimal for remote recruitment: planning and forecasting of workforce challenges, online vacancy announcements placing, usage of digital remote recruitment tools, usage of video interviewing, organization of constant communication with the remote recruitment team, organization constant communication with candidates and virtualization the process of adaptation. In our opinion, the current digital tools for automating remote recruitment include following groups of software: for screening candidates, for automating recruitment, for attracting talents, for video interviews and for operating with job platforms. The main way to ensure effective implementation of remote recruitment in the context of social distancing is to build and maintain a digital ecosystem of remote recruitment, which should include tools for automating recruitment processes, platforms for attracting candidates, digital technologies for social recruitment, artificial intelligence, and robotics.

Yuan blue-and-white porcelain, a treasure of Chinese ceramic art, is renowned worldwide for its unique artistic value and cultural significance. With the continuous advancement of artificial intelligence (AI) technology, the artistic innovation of Yuan blue-and-white porcelain is experiencing new opportunities. AI technology not only improves the efficiency and quality of creation but also breaks through traditional physical limitations, enabling the creation of more diverse and creative artworks. By integrating augmented reality (AR) and virtual reality (VR) technologies, AI provides new ways to display and interact with Yuan blue-and-white porcelain art. In non-physical artistic innovation, AI generates new designs through deep learning algorithms, breaking free from the constraints of traditional handmade processes and creating diverse works. Technologies like generative adversarial networks (GANs) can produce highly realistic Yuan blue-and-white designs, while convolutional neural networks (CNNs) can quickly generate design sketches, enhancing creative efficiency. AI can also optimize designs, improving the artistic quality and visual effects of the works.AR and VR technologies offer immersive experiences and interactivity for the audience. Through virtual exhibition spaces and AR applications, viewers can appreciate Yuan blue-and-white artworks as if they were physically present, enhancing interactivity and visual appeal. VR technology, through virtual exhibitions and other means, enhances the viewing experience and impact of the artworks. The application of AI technology in the non-physical artistic innovation of Yuan blue-and-white porcelain not only enhances creation efficiency and quality but also brings limitless possibilities to artistic creation. The combination of AR and VR technologies further enhances the visual appeal and interactivity of Yuan blue-and-white art. As technology continues to advance, AI will play an increasingly important role in the artistic innovation of Yuan blue-and-white porcelain, creating more splendid artworks.

The article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.

Artificial Intelligence (AI) has emerged as a transformative tool in business operations, particularly in the areas of revenue optimisation, customer relationship management (CRM), and strategic decision-making processes. In revenue optimisation, AI leverages advanced algorithms and machine learning techniques to analyze vast datasets, enabling businesses to predict demand, optimize pricing strategies, and identify profitable opportunities. AI's dynamic pricing models, which adjust in real-time based on market conditions and consumer behavior, significantly enhance revenue generation by ensuring businesses can capitalize on peak demand and minimize losses during low-demand periods. In CRM, AI plays a critical role in personalizing customer interactions by utilizing data from various touchpoints, such as purchase history and online behavior, to provide tailored recommendations and offers. AI-driven chatbots and virtual assistants enhance customer service by automating routine tasks and providing instant support, thereby improving customer satisfaction and loyalty. Additionally, AI enables businesses to forecast customer preferences and segment their customer base effectively, leading to more targeted and impactful marketing efforts. In strategic decision-making, AI supports business leaders by offering data-driven insights that assist in identifying trends, risks, and opportunities. AI tools analyze both internal and external data, providing actionable insights for resource allocation, market expansion, and product development. By reducing human biases and enhancing decision accuracy, AI empowers businesses to make faster, more informed decisions. The integration of AI in revenue optimisation, CRM, and strategic decision-making processes not only streamlines operations but also contributes to business growth and competitive advantage. As AI technology continues to evolve, its impact on these key areas is expected to expand, further driving innovation and efficiency in business practices.