Navigating the Fourth Industrial Revolution: Empowering Socio-Technical Organizations With Data-Driven Business Intelligence Systems

Navigating the Fourth Industrial Revolution: Empowering Socio-Technical Organizations With Data-Driven Business Intelligence Systems

David Valle-Cruz, J. Patricia Muñoz-Chávez, Rigoberto García-Contreras
Copyright: © 2024 |Pages: 27
DOI: 10.4018/979-8-3693-1210-0.ch001
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Abstract

Artificial intelligence (AI) has become an integral component in organizational processes, generating several business models that have revolutionized the way companies operate. These advancements have the potential to enable organizations to respond with more agility, achieve greater productivity, and provide superior stakeholders experiences. To provide a more proactive and pragmatic perspective, this chapter explores the current state of data-driven business intelligence systems within the realm of socio-technical organizations. The findings offer an early glimpse into how AI has become a key catalyst for innovation and success in socio-technical organizations, offering benefits ranging from operational agility to improved experience. Furthermore, for the future, understanding and effectively implementing data-driven business intelligence systems is crucial to staying competitive in today's business environment.
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Milestones On The Data-Driven Business Intelligence Systems

A constant in modern organizations is change (Kamel, 2020). Creative destruction and disruptive innovation are amplified by the emerging technologies of the 21st century (Clarke, 2019). In recent decades, the rapid advancement of the socio-digital era and the consequent increase in the application of Artificial Intelligence (AI) as a new operational foundation in various areas of organizations, including those related to products, services, and processes, has transformed the way companies operate and compete. AI is now comprehensively utilized in various aspects of company operations, such as decision-making, process automation, product or service customization, supply chain optimization, customer data analysis, and customer service, among other areas (Iansiti & Lakhani, 2020).

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