Strategic Pivot of Artificial Intelligence

Inhaltsverzeichnis

1. Frontmatter

2. 1. Why Artificial Intelligence Requires Economics, Governance, Management, and Human Resources

   Marco Pironti

   Abstract

   Whether or not artificial intelligence (AI) is a disruptive economic, social, and environmental phenomenon rather than a mere advanced technology depends on our perspectives. Such perspectives include economics, governance, management, and human resources. These can offer a wide nuance of such technology discussing its effects on society. AI is a peculiar technology that does not replace human beings but empowers their capabilities. This is because AI pervasively changes how people, businesses, and institutions live and relate to each other, changing their ecosystems, economies, equilibria, and value-creation mechanisms over time. In this sense, AI should be approached as a complex and dynamic system that involves economics, governance, management, and human resources. As the need for new talents and/or skills is highly requested, the value of AI ensures social and eco-systemically sustainable development, even including neurodiversity people.

3. 2. Innovation and AIs: New Forms of Evolutions

   Lea Iaia

   Abstract

   The huge volume of heterogeneous data generated in the last decade has forced an acceleration of technological progress, due to the need to increase computational processing capacity and develop new artificial intelligence techniques to respond to the information needs of organizations.

   Advancements in processing and computational power have enabled artificial intelligence (AI) to reach performance levels that were previously unimaginable, elevating AI to a key strategic technology for organizations. In dynamic and unpredictable environments, characterized by competitive global markets, rapidly advancing technologies, and shifting political landscapes, AI has demonstrated its capacity to support the decision-making processes of increasingly complex organizations. Leading technology companies such as Google, Amazon, Netflix, Airbnb, Facebook, and Apple have integrated AI into their strategies, leveraging vast amounts of data to gain valuable insights. This has enabled them to enhance productivity, innovate their offerings, and expand into new markets with highly personalized products and services for their customers. This initial evidence highlights AI’s significant potential to drive organizational innovation.

   However, what are the future prospects—both opportunities and challenges—of its impact on innovation processes? Additionally, which types of organizations are best equipped to support and advance AI-driven innovation?

4. 3. Entrepreneurship and its Sustainable Declinations Leveraged by AIs

   Veronica Scuotto

   Abstract

   The modern society has been rapidly embraced in the use of AI, inducing ethical challenges and social sustainability. To understand such transformation, the present chapter offers an overview of the concept of entrepreneurship, mostly focusing on social entrepreneurship. In turn, it emphasizes the role of context in analyzing the new scenario and presents new types of entrepreneurs such as smart social entrepreneurs and human entrepreneurs which employ new education approaches and ethical challenges.

5. 4. AI Revolution Road: A KM Perspective

   Cultivating the Future: AI-Driven Knowledge Management in Society 5.0 Pasquale Sasso, Cillo Valentina, Manlio Del Giudice

   Abstract

   This chapter explores the transformative potential of Artificial Intelligence (AI) and knowledge management (KM) within the framework of Society 5.0, a human-centered paradigm. Unlike Industry 4.0, which emphasizes automation and optimization of industrial processes, Society 5.0 integrates advanced technologies to address global challenges such as climate change, social inequality, and an aging population. The chapter uses the Data, Information, Knowledge, Wisdom (DIKW) model to illustrate how AI-driven solutions can transform traditional sectors like agriculture, turning data into actionable knowledge that supports strategic and long-term decision-making. Through case studies in agriculture, the chapter shows how AI and KM enhance productivity, sustainability, and collaboration. The chapter also discusses the ethical challenges associated with AI adoption, particularly regarding job displacement, data privacy, and algorithmic bias, and emphasizes the importance of human oversight and ethical frameworks to ensure the equitable distribution of AI benefits. Ultimately, this work argues that the successful integration of AI and KM within the context of Society 5.0 requires a balance between technological innovation and human-centered values. By prioritizing ethical considerations and focusing on the collaborative use of AI, businesses can leverage these technologies to foster sustainable development and social well-being, addressing both economic and ethical challenges in the digital age.

6. 5. Managing Innovative Circular Ecosystems: The Role of Artificial Intelligence

   Robert Crammond, Francesco Caputo

   Abstract

   The concept of the Circular Economy (CE), in confronting global environmental and socioeconomic challenges, has continued to attract attention of researchers and practitioners, as well as result in practical initiatives and interventions for desired Innovative Circular Ecosystems (ICEs). Significantly, amidst ongoing (I)CE discussions, society witnesses the proliferated integration of Artificial Intelligence (AI) within organizations. AI integration by business assists their understanding of key attributes and assets, as well as in sorting and analysing ongoing operations towards sustainable practice. However, managerial-level challenges exist, including the transformative requirements concerning resource, planning, and education, and the overarching cultural knock-on effects of AI which occupy time and decision-making. In response, this chapter discusses the managerial perspective of integrating AI within organizations towards ICEs. Subsequently, presented is a novel ‘cyclical model’, which embraces and embeds AI technology through phases and stages of recognition, AI integration, and sustainable development via sequential creative and innovative activity led by managers. Adoption of this model may result in renewed organizational structure and strategy, through AI-assisted analysis, product and service redesign, and reasoned re-positioning within respective markets. Nevertheless, integration of such conceptualizations in practice, along with understanding social, cultural, and ethical impacts of the intersection of AI and ICEs within organizational and sector contexts, merits further research investigation.

7. 6. Innovating Business Model in and for AI

   Theo Tzanidis, Alan Murray

   Abstract

   The chapter “AI and the Reimagination of Business Models: Transforming Growth from Startup to Large Enterprise” deals with how artificial intelligence (AI) can revolutionize business development at all stages of the business. It covers how AI reimagines essential elements of the business model like value proposition, customer experience, and efficiency, to enable hyper-personalization, optimized operations, and new revenue models. Looking at the adoption path, this chapter brings to light the adoption of AI in startups for agility, in SMEs for scale, and in large enterprises for complexity. It contains practical examples (like Netflix’s recommendations, Dyson’s smart appliances, HSBC’s fraud-prevention systems) to illustrate AI’s practical potential. It also tackles organizational inertia, ethical issues, and regulation, challenging businesses to act strategically and responsibly. With AI integrating with blockchain and IoT technologies, the future holds more possibilities for companies to disrupt, innovate, and succeed than ever before.

8. 7. Entrepreneurial Neurodiversity and AIs

   Lucrezia Casulli, Suzanne Mawson

   Abstract

   This chapter discusses the practical applications of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD) through an innovative iPad-based serious game. It begins by specifying that the focus is on machine learning as it applies to health diagnosis. The case study presented involves a game designed to detect ASD in children with greater accuracy and earlier in the child’s life than traditional diagnostic methods.

   In the chapter, we begin by contrasting two perspectives on ASD: the medical view, which sees it as a disability, and the sociological perspective, which advocates for neurodiversity. Next, we emphasize the importance of early diagnosis for ASD, reporting on how it can significantly improve developmental trajectories and life outcomes for affected children.

   Professor Jonathan Delafield-Butt, who developed the game, shares insights on how movement patterns in children can indicate autism and how the technology has evolved to utilize touch-sensitive screens for data collection. Professor Delafield-Butt argues that AI does not replace the expertise of health professionals but serves as a complementary tool, expediting diagnosis and enhancing decision-making.

   The chapter concludes by detailing the various forms of value generated by this AI- powered tool, including economic benefits for healthcare systems and enhanced wellbeing for families and children undergoing diagnosis. By providing clearer, quicker insights into a child’s condition, the game not only alleviates anxiety for parents but also facilitates timely intervention for children with ASD. Ultimately, the article illustrates how AI can transform medical diagnostics in a socially inclusive manner, benefiting both practitioners and patients.

9. Backmatter