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This chapters asks: What do the Artificial Intelligence (AI) strategies of the EU, the United States under Donald Trump and China look like? It conducts a critical policy discourse analysis from a Radical Humanist Perspective. It analyses what kind of ideologies we can find in the AI strategies of the European Union, the United States under Donald Trump and China.

The analysis shows that AI and robotics are situated in a digital technology race that is indicative of an international political-economic race for the accumulation of political-economic power.

The contemporary business environment has been revolutionized by artificial intelligence (AI), which has undoubtedly impacted the operational and competitive strategies of small and medium-sized enterprises (SMEs). In this write-up, we delve into AI’s influence on SMEs by delving into the advantages, obstacles, and prospects associated with adopting AI technology. Based on an exhaustive analysis of current literature, this essay contends that SMEs can optimize their functions, enhance decision-making processes, and deliver superior customer experiences with the adoption of AI. However, SMEs face several challenges in embracing AI technology, including the high cost of implementation, lack of technical expertise, and concerns about data confidentiality and security. This chapter also illuminates prospective opportunities that SMEs can leverage through AI, such as establishing a foothold in new markets and competing effectively with larger enterprises. Overall, this chapter concludes that SMEs must carefully evaluate the costs and benefits of adopting AI, and develop a strategic plan for implementation that aligns with their business goals and resources.

Artificial intelligence (AI) has already changed the financial industry by increasing the accessibility and inclusiveness of financial services. While acknowledging the challenges posed by AI, this chapter provides insights into the positive impact of AI in promoting financial inclusion. AI has greatly enhanced credit scoring and risk assessment through the use of non-traditional data sources, enabling individuals with limited credit histories and low incomes to access loans and financial products. In addition, the implementation of AI-powered customer identification and verification systems has enhanced security measures while reducing the risk of fraudulent activity. However, the digital divide still remains a challenge to achieve wide financial inclusion. Limited access to technology and digital skills keeps some people from fully benefiting from AI-powered financial services. Access to loans through AI systems may seem convenient, but it also raises concerns about excess borrowing and the resulting unsustainable debt levels. In the age of digital finance, privacy and data security are still key issues. The chapter concludes by highlighting that more research is needed to address these challenges. By fully understanding the potential of AI, as well as its limitations, the power of technology can be harnessed to create more inclusive economic opportunities for everyone, especially those living in poorer areas.

The financial sector’s growing interest in leveraging artificial intelligence (AI) for forecasting has been noted in recent years. In this chapter, we delve into the application of AI in financial forecasting within Indonesia’s stock market. Our primary focus is to assess how AI’s prediction potential can impact investors and financial regulators in this context. Our review spans existing literature on AI and financial forecasting, recent developments in the Indonesian stock market, and ethical and regulatory concerns that surround AI in finance. Our analysis indicates that AI can enhance forecast accuracy in Indonesia’s stock exchange; however, we must also consider limitations and challenges.

Disruptive technologies are accelerating global growth. Artificial intelligence (AI) has the potential to transform the idea of delivering value to end users. On the other hand, the growth of Industry 5.0 has given rise to the concept of humanizing technology, and AI is a promising technology with the potential to contribute to business success. Nevertheless, the idea of value creation in the field of AI is novel, so it is necessary to define the meaning of value by understanding the context of AI applicability in different environments and industries. In this chapter, the author uses the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) procedure to conduct an SLR that provides interesting insights into the focus, industries, and methodologies and approaches used in existing research. Following the initial literature review on the state of the art of AI and value creation, the author also offers a reflection on the strategic implications of AI in the field of marketing, postulating a macrovalue creation framework that addresses the existence of implications on three different levels: emerging markets, Sustainable Development Goals, and adoption issues. Therefore, this chapter examines the value creation perspectives of AI to understand the current research focus and future directions.

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

Purpose – The purpose of this chapter is to provide a comprehensive overview of the substance use disparities among American Indian/Alaska Native (AI/AN) youth, the contributing factors to these disparities, proven and promising approaches through strengths-based methods, barriers to implementation of prevention and treatment efforts, and future recommendations for effective programs and research.

Approach – We have conducted a thorough literature review of relevant research studies, as well as a review of government, tribal, and community-based curricula and resources. This review of programs is not exhaustive but provides several examples of best practices in the field and suggestions for future directions.

Social implications – We strongly advocate that to accurately explore the true etiology of substance abuse and to respond to the concerns that AI/AN have prioritized, it is necessary to utilize a strengths-based approach and draw upon traditional AI/AN perspectives and values, and active community participation in the process. More specifically, prevention and treatment programs should use methods that incorporate elders or intergenerational approaches; foster individual and family skills-building; promote traditional healing methods to recognize and treat historical, cultural, and intergenerational and personal trauma; focus on early intervention; and tailor efforts to each Native nation or community.

Value – Ultimately, to reduce substance abuse disparities in AI/AN youth, we must find better ways to merge traditional Native practices with western behavioral health to ensure cultural competency, as well as to develop mechanisms to effect system- and policy-level changes that reduce barriers to care and promote the well-being of AI/AN youth, families, and communities.