Intelligent Enterprise Research Articles

Intelligent Enterprise Integration: An AI Framework for Dynamic Data Transformation and Process Optimization

Intelligent Enterprise Integration: An AI Framework for Dynamic Data Transformation and Process Optimization

How Do Artificial Intelligence (AI) and Big Data (BD) Technologies Help Social Impact Enterprises Build Legitimacy?

This paper explores the role of artificial intelligence (AI) and big data (BD) technologies in social impact enterprises in China, particularly focusing on their social activities. It demonstrates that these technologies are instrumental in building pragmatic, moral, and cognitive legitimacy by enhancing transparency and efficiency in stakeholder interactions across both commercial and social networks. Additionally, the study highlights the vital role of social impact enterprises in maintaining infrastructure, public services, and social welfare in financially challenged regions amidst local government debt crises. The necessity for enterprises to align with historical and cultural norms, particularly Confucian values and kinship expectations, is also emphasized. This research bridges a critical gap in the literature, offering insights into the integration of technological innovation with social entrepreneurship, and providing a foundation for future studies and policy development.

An Overview of the Intelligence Entreprise in Canada

Dr. Gilmour's presentation highlighted critical gaps in Canada's intelligence enterprise, particularly the disconnect between intelligence analysts and policymakers, and the inadequacy of the current framework in addressing evolving threats like hybrid warfare and non-traditional security issues. He emphasized the need for enhanced cooperation, education, and structural changes within the intelligence community to effectively tackle these challenges. Additionally, Dr. Gilmour stressed the importance of fostering stronger public-private partnerships to leverage the private sector's capabilities in countering modern threats. Received: 08-10-2024 Revised: 08-26-2024

Redefine manufacturing operations for modern production environments with the help of artificial intelligence enterprise information systems

Redefine manufacturing operations for modern production environments with the help of artificial intelligence enterprise information systems

Finance centralization—research on enterprise intelligence

In the context of digital transformation, financial centralization has emerged as a crucial strategy for enhancing the efficiency and effectiveness of corporate financial management. This paper explores the role of Artificial Intelligence (AI) in financial centralization within Chinese enterprises, particularly under the framework of digital corporate financial management. By analyzing multiple case studies, the research demonstrates how AI applications streamline financial processes, mitigate risks, and improve decision-making accuracy. The study focuses on three key AI-driven applications: automated reconciliation, intelligent report generation, and big data-based risk management. Additionally, the research examines the unique challenges and opportunities that private and state-owned enterprises face during AI-driven financial transformation. Through in-depth analysis, the paper offers valuable insights into how AI is reshaping the financial landscape, providing both practical and theoretical contributions. These findings offer valuable implications for financial managers, policymakers, and researchers seeking to leverage AI in the evolving field of intelligent financial management.

Implementation of Artificial Intelligence in Quality Management in SMEs: Benefits and Challenges

The adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMBs) has emerged as a key strategy for improving quality management. This article discusses the main benefits and challenges of implementing AI in this context. A systematic review of the academic literature and relevant business reports was carried out, in order to collect and analyze empirical evidence on the application of AI in the quality management of SMEs, obtaining as results that the main benefits of the implementation of AI in the quality management of SMEs include: Improved data-driven decision-making, process automation and error reduction, early detection of quality issues, customization of products and services, and resource optimization and cost reduction. On the other hand, the main challenges were: Lack of knowledge and skills in AI, high implementation costs, concerns about data security and privacy, resistance to cultural change, and integration of AI with existing systems and processes, concluding that the implementation of AI in SMB quality management offers significant benefits, but it also entails challenges that must be addressed strategically. SMEs that manage to overcome these challenges will be able to take advantage of the competitive advantages offered by AI, improving their efficiency, quality and adaptability in an increasingly dynamic business environment.

A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises

A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises

Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment

This study investigates the effects of digital capabilities, innovation capabilities, and business environmental support on the adoption of Artificial Intelligence (AI) in Small and Medium-sized Enterprises (SMEs). Utilizing dynamic capabilities and resource dependency theories, we provide a comprehensive and integral analysis of the drivers that facilitate AI adoption in SMEs. We conducted an empirical study encompassing 12,108 SMEs, based on survey data of the Flash Eurobarometer database from the European Union. Our analysis employed a combination of classical regression methods and advanced machine learning techniques, including artificial neural networks and tree regression. Our findings highlight the importance of digital capabilities in driving AI adoption, where complementing innovation capabilities exhibit synergistic effects. Contrary to prevailing literature, business environmental support alone demonstrates limited impact, emphasizing its contingent effectiveness within a well-elaborated institutional framework. Furthermore, the synergy between business environmental support and digital and innovation capabilities has a significant impact on AI adoption in SMEs. However, internal capabilities exert a greater influence on AI adoption in SMEs compared to business environmental support. This study contributes to dynamic capabilities theory by elucidating the interplay of digital and innovation capabilities, offering a nuanced understanding of their combined influence on AI adoption. It also enriches resource dependency theory by highlighting the dynamic nature of business environmental support. For practitioners, our results underscore the need for a balanced investment in digital and innovation capabilities. Policymakers should consider these insights when designing support structures for SMEs, emphasizing a comprehensive approach to foster internal capabilities alongside creating an enabling external environment.

Exploration and Construction of Professional Curriculum System in the Field of Intelligent Manufacturing Based on Data Analysis

Intelligent manufacturing is an important means of developing strategic emerging industries and accelerating the formation of a modern industrial system. Currently, many universities are upgrading their related majors or offering new majors such as Intelligent Manufacturing Engineering to provide talent for the field of intelligent manufacturing. Due to the lack of integrated resources between industry and education in the field of intelligent manufacturing, it is difficult for universities to strengthen the correlation between the professional curriculum system and the industry when opening new majors or upgrading majors. With the integration of new generation information technology and manufacturing technology, the country plans to transform traditional enterprises into digital, information-based, and intelligent enterprises, and cultivate innovative, technical, and skilled talents that adapt to the development of intelligent manufacturing. It has become a new development strategy given to vocational colleges by the times. This article first obtains information on professional courses related to intelligent manufacturing and artificial intelligence offered in the region, and then uses Python data analysis technology to conduct statistical analysis on the collected professional courses. The analysis results provide reference and inspiration for universities to offer majors related to intelligent manufacturing.

Financial Synergy for Sustainable Success: Optimizing Management, Investment, AI, and Risk SMEs in Bali

In the context of globalized and environmentally conscious business landscapes, this study investigates the intricate relationships between investment decisions, risk perception, artificial intelligence (AI), and sustainable success in small and medium enterprises (SMEs) in Bali. Employing a quantitative approach, the research explores the mediating role of investment decisions using theoretical frameworks such as the Triple Bottom Line, Pecking Order Theory, Trade-Off Theory, and Prospect Theory. Results from a sample of 150 SMEs reveal significant direct effects and correlations, with investment decisions mediating between AI, risk perception, and sustainability. Managerial implications emphasize strategic AI integration, robust risk management, and a balanced approach to investment decisions for sustainable success. While limitations include sample size constraints, future research directions suggest longitudinal and qualitative studies, exploration of sectoral variations, and assessing the impact of emerging technologies on sustainability decisions. This study provides actionable insights for companies navigating the complexities of sustainable decision-making and underscores the imperative for businesses to foster a culture of adaptability and innovation. By embracing these insights and cultivating a forward-thinking approach, companies can not only optimize their current sustainability strategies but also position themselves strategically to thrive in an ever-evolving business landscape.

Identifying obstacles to evaluating business intelligence in Micro-Small Apparel Enterprises: a case study in Durban, South Africa

The escalating financial burdens faced by the Small Medium and Micro Enterprise (SMME) sector necessitate a perpetual pursuit of enhanced managerial efficacy. For enhanced financial management, client relationship management and inventory control, it is necessary for businesses to utilise various technologies and methodologies. One approach involves the utilisation of business analytical technologies, such as Business Intelligence (BI). Business Intelligence (BI) serves as a valuable tool for enterprises, aiding in the examination and interpretation of data to facilitate informed decision-making processes aimed at enhancing overall business efficacy. The eThekwini region encompasses a significant presence of micro-small enterprises within the apparel manufacturing and retail sector. The primary aim of this research was to identify and analyse the many issues that hinder the evaluation of business intelligence (BI) in micro-small apparel enterprises located in Durban, a city in Kwa-Zulu Natal, South Africa. A cross-sectional quantitative study was undertaken to examine a cohort of 161 small apparel business owners. The participants were chosen through the utilisation of a judgemental sampling technique. The data was gathered through the utilisation of anonymous questionnaires. The data was analysed by utilising multiple linear regression analysis to determine the characteristics that hinder the evaluation of business intelligence (BI) in the small apparel industry. The study employs the Technology-Organisation-Environment (TOE) framework as its conceptual framework. The researcher identified several obstacles that hinder the evaluation of business intelligence in micro-small textile enterprises in South Africa. These factors include perceived relative advantage, cost, organisational data environment, organizational decision-making culture, and external support. However, there are numerous governmental institutions that are intended to provide support for micro-small companies. Therefore, it is recommended that these institutions establish training initiatives aimed at educating apparel SMMEs on BI. The implementation of target-driven training has the potential to significantly contribute to the achievement of vision 2030 for fostering the growth of small, medium, and micro enterprises (SMMEs) to enhance the South African economy.

Ethical considerations in AI use for SMEs and supply chains: Current challenges and future directions

The integration of Artificial Intelligence (AI) in Small and Medium-sized Enterprises (SMEs) and supply chains has revolutionized operational efficiencies and decision-making processes. This review paper aims to explore the ethical implications of AI adoption in these sectors, identifying current challenges and proposing future directions for ethical AI deployment. Through an extensive review of existing literature, the paper examines key ethical concerns such as data privacy, bias in AI algorithms, transparency, and the socio-economic impact on the workforce. The findings indicate that SMEs face unique ethical challenges due to their limited resources and expertise, which exacerbate issues related to AI implementation. Additionally, supply chains grapple with transparency and accountability, necessitating immediate attention to ensure ethical practices. The review concludes that establishing a robust ethical framework is crucial for guiding AI integration in SMEs and supply chains. It recommends the development of standardized ethical guidelines, enhanced stakeholder engagement, and increased investment in AI literacy and infrastructure. Future research should focus on creating adaptable ethical models that evolve alongside technological advancements and industry needs, ensuring that AI contributes to sustainable and equitable growth. This paper contributes to the ongoing discourse on ethical AI, offering actionable insights for policymakers, business leaders, and researchers dedicated to fostering responsible AI practices in SMEs and supply chains. Keywords: Ethical AI, SMEs (Small and Medium-sized Enterprises), Supply Chains, Algorithmic Bias, Data Privacy, Transparency, Accountability, AI Trust, Regulatory Compliance, Stakeholder Engagement, Job Displacement, Explainable AI (XAI), Workforce Reskilling, Sustainable AI Practices, AI Ethics Frameworks.

Artificial Intelligence in Enterprises: How Staff Competencies Requirements of Business Organisations are Evolving through the Integration of Artificial Intelligence

Artificial Intelligence in Enterprises: How Staff Competencies Requirements of Business Organisations are Evolving through the Integration of Artificial Intelligence

Adoption of Artificial Intelligence in Small and Medium-Sized Enterprises in Spain: The Role of Competences and Skills

Adoption of Artificial Intelligence in Small and Medium-Sized Enterprises in Spain: The Role of Competences and Skills

Does artificial intelligence reduce corporate energy consumption? New evidence from China

Artificial intelligence is playing a significant role in addressing the energy crisis. This study selected data from manufacturing companies listed on China's A-share market from 2011 to 2022 and calculated the total energy consumption for the first time. The data include the usage of coal, natural gas, gasoline, diesel and water consumption, electricity usage, and centralized heating. The data were then matched and merged with robot usage data from the International Federation of Robotics to empirically study the impact and mechanism of artificial intelligence on energy consumption levels. Our findings reveal that energy consumption decreases by 0.20 % with a one-unit increase in artificial intelligence applications by a corporation, indicating artificial intelligence can significantly reduce energy consumption. The mechanisms by which artificial intelligence affects energy consumption include technological innovation and digital transformation. Additionally, a heterogeneity analysis revealed that applying artificial intelligence in state-owned enterprises, high-tech companies, and non-heavy-pollution industries can further reduce energy consumption. Our study also provides important practical implications for formulating and optimizing global energy policies to achieve sustainable development goals.

The Innovation of Enterprise Intelligent Transformation to Financial Decision-making Process

This paper explores the transformative impact of enterprise intelligent transformation on the financial decision-making process. Through a comprehensive analysis, it investigates the role of intelligent technologies in reshaping resource allocation, cost control, and long-term planning within modern enterprises. By examining the pathways of innovation in intelligent transformation, it sheds light on the pivotal role of technology in enhancing financial management practices, thereby contributing to strategic decision-making and organizational success.

ПРАВОВЕ РЕГУЛЮВАННЯ ФІНАНСОВОЇ РОЗВІДКИ ПІДПРИЄМСТВ В УМОВАХ ПОСИЛЕННЯ ВПЛИВУ ЧИННИКІВ СУЧАСНОСТІ

The purpose of the article is to study the peculiarities and problematic aspects of the legal regulation of financial intelligence of enterprises in the conditions of increasing influence of modern factors. Based on the results of the conducted research, it was established that at the current stage of the functioning of the national legal system, the organizational and legal mechanisms for regulating financial intelligence at the enterprise level are weak and require revision, improvement and strengthening. It was established that when considering financial intelligence at the enterprise level, the main emphasis is placed on its structural component, such as competitive intelligence. The ambiguity and specificity of the legal basis for the regulation of financial intelligence of enterprises have been revealed, the main difference of which is the formation of legal norms in interdependence with the peculiarities of the country's legal system and its affiliation to the Anglo-Saxon or Romano-Germanic legal system. It has been proven that the financial intelligence system functions successfully at such well-known global companies as Procter & Gamble and Motorola. The main problems of the introduction and implementation of intelligence activities at enterprises were identified, most of which are due to the insufficient number and inefficiency of the management of information, labour and technological resources of the economic entity. The main vectors of improving the legal regulation of financial intelligence of enterprises in the conditions of increasing influence of modern factors are proposed, in particular: (1) the urgency and necessity of implementing the legislative regulation of the economic and legal category "financial intelligence of an enterprise" is substantiated; (2) the necessity of enshrining at the legislative level the basic organizational and legal principles of increasing responsibility for committing acts related to the unauthorized receipt, use and transfer of information at the enterprise has been proven.

Artificial intelligence and labor demand: An empirical analysis of Chinese small and micro enterprises

The widespread application of artificial intelligence (AI) technology has triggered a significant transformation in the economic structure and has brought profound changes to human society. As China promotes the digital transformation of industries, understanding how the investment in AI by small and micro enterprises (SMEs) affects labor demand, which is inextricably linked to “stable employment”, becomes an important question. This paper uses special data from 127 SMEs in 14 provinces from 2016 to 2020 and employs a two-way fixed effects model to study the impact of AI inputs on enterprises’ labor demand. The empirical results show that the impact of AI inputs on the labor demand of SMEs is not significant overall, but shows a significant negative effect in non-state-owned enterprises, private enterprises, and high-tech enterprises. There is a significant difference in the impact of AI inputs on the labor demand of different industries, with only the wholesale and retail industry demonstrating a significant positive impact. From the results of mechanism analysis, the substitution effect and creation effect of AI inputs on labor demand coexist, and in general, these two effects cancel each other out. However, the substitution effect dominates in some types of enterprises and industries. Finally, this paper discusses the government and enterprise coping strategies for the employment impact of AI applications based on empirical evidence and research results. This paper not only theoretically demonstrates that the impact of AI investment on firms' labor demand is uncertain, but also empirically demonstrates that Chinese firms' AI investment does not significantly affect firms' overall labor demand. This facilitates the government and enterprises to formulate strategies that can enhance the level of enterprise intelligence without impacting the labor market.

An Overview on SAP S/4HANA Deployment Options and Transition Paths

SAP S/4HANA is the next generation SAP business suite software. It is the digital core of your organization and is designed to run an Intelligent Enterprise. Many SAP ECC customers are planning for their transition to SAP S/4HANA. SAP ECC customers are in confusion on the approach to move to SAP S/4HANA and choosing a deployment option. There are Three different approaches for moving to SAP S/4HANA.

AI Adoption in Jordanian SMEs: The Influence of Technological and Organizational Orientations

This study examines the factors influencing the adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMEs) in Jordan, a key player in the growing Middle Eastern economy. Rooted in the Technology–Organization–Environment framework, we specifically focus on the role of technological capabilities and organizational dynamics in shaping AI adoption within Jordanian SMEs. A comprehensive survey involving 364 SME owner-managers in Jordan serves as the empirical foundation. Findings reveal the significant impact of employee IT knowledge, IT infrastructure, managerial commitment, training initiatives and well-designed reward systems in shaping SME owners’ or managers’ attitudes to AI. These findings provide valuable insights for SME leaders and stakeholders, guiding them in developing strategies to smoothly integrate AI technologies in line with Jordan’s societal needs. The article concludes by emphasizing the study’s contributions, implications and limitations while suggesting potential directions for future research in this field.