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From Algorithms to Applications: AI in Management and Engineering

What began in the mid-20th century as expert systems and symbolic reasoning has evolved into advanced machine learning (ML), deep learning (DL) and intelligent agent technologies that have the potential to revolutionize decision-making and operational behaviors across industries. To technically inclined readers, understanding AI is not merely an inventory of algorithms but a conceptual appreciation that positions every AI type within its broader technological and organizational setting. The twin use of AI in management and engineering illustrates convergence and divergence in the uses of technologies. In the case of management, AI facilitates predictive analytics, automation of processes and intelligence augmentation to improve organizational productivity. In the case of engineering, it enables design automation, predictive maintenance and smart system integration, transforming the pillars of industry and infrastructure. This chapter places AI as the practical enabler and theoretical instrument, and it draws attention to a taxonomy of AI types and multiple functions AI has in achieving managerial and engineering creativity.

1.1. Introduction

Artificial intelligence (AI) application in engineering is facilitating enormous revolutions in various areas. AI assists in design and optimization by analyzing enormous datasets to predict failure and optimize system performance, particularly in civil engineering (Cubric 2020; Adeyeye and Akanbi 2024). For construction management, AI assists in controlling schedules, monitoring costs and quality, and observing safety, with the use of platforms such as the engineering machine learning automation platform (EMAP) (Choi et al. 2021; Parekh and Mitchell 2024; Xu and Guo 2025). AI is a key factor in product life cycle management in Industry 4.0 and smart manufacturing, maximizing design, manufacturing and service stages (Wang et al. 2021). In materials and manufacturing studies, AI and machine learning (ML) complement traditional modeling techniques, especially where mechanisms are complex or high in computational costs (Lin and Lu 2020). Aerospace engineering is boosted by AI and blockchain technology for supply chain management and fault identification (Abdulrahman et al. 2023).

AI-based applications in business management are transforming efficiency and decision-making. Intelligent decision support systems improve project management using data-driven models for fostering insight and acceleration (Wang 2023). AI-based risk management goes beyond statistical methods for minimizing errors and maximizing accuracy throughout project stages (Choi et al. 2021). Human resource management and international business management are also assisted by AI through automation and performance improvement (Madanchian and Taherdoost 2025). Apart from this, Kansei Engineering uses AI to infuse emotional and consumer-centric designing elements into the products, hence making them attractive (Nagamachi and Lokman 2016).

Problems of data quality, algorithm bias and explainability requirements for AI systems are serious concerns that need to be addressed to enable successful deployment (Naser 2021; Whang et al. 2023). With businesses still in pursuit of the potential of AI, there is likely to be greater focus on creating solid frameworks that allow for the effective and responsible application of AI technologies across industries.

The aim of this chapter is to provide an integral taxonomy-driven technical explanation of AI technology in the context of management and engineering. Rather than citing isolated case studies or merely mandated applications, the chapter conceptually maps AI categories - such as knowledge-based systems, machine learning, natural language processing, computer vision, and intelligent agents - to their roles in organizational management and in shaping engineering imagination. The goal is to give technical readers a uniform framework for understanding the variety of AI applications, the conceptual underpinnings of different approaches and the comparative ways in which these technologies enhance decision-making, process optimization and system design.

1.2. Foundations of artificial intelligence

The quest for defining intelligence, and particularly intelligence in the case of artificial systems, is at the forefront. Legg et al. (Legg and Hutter 2007) note that one of the problems inside AI is that there is no clear definition of intelligence. They suggest a mathematical formalization that aligns with machine intelligence and require a definition to be broad for various types of intelligence, such as artificial systems. This kind of formalization serves as a starting point for studying how AI is defined and evaluated. Context is one of the main concepts in AI, specifically how systems understand and react to information.

Sowa (1997) records the history of context in AI, where Peirce's work started it off. This study concludes that despite the colossal dispute regarding context, there is no single definition that can be used across all cases. Insertion of context in AI systems is required in a bid to enhance their interpretation and interaction capabilities. Marchesotti et al. (2004) also explain the importance of context in ambient-intelligence systems. They introduce structured approaches to contextual knowledge definition, which become critical while developing scalable and adaptable AI systems. This multi-layered modeling of context allows AI to efficiently handle heterogeneous data, thereby improving its decision-making capability. Wang (2019) critically examines the issues of how to define AI and recommends that a good working definition should meet ordinary usage, have well- defined boundaries and be suitable for rewarding research. He recommends that intelligence may be defined as "adaptation with too little knowledge and resources", a focus that underscores the adaptive character of intelligence in humans and machines. This definition aligns with the wider context of AI capabilities, namely under General-Purpose AI Systems (GPAIS).

This definition is in line with the overall discussion of AI capabilities, particularly under General-Purpose AI Systems (GPAIS). Triguero et al. (2024) explained GPAIS as systems that may perform a wide variety of tasks without being programmed on an individual case-by-case basis. They also present a taxonomy that distinguishes types of GPAIS according to their nature and limitations and also defines what is considered AI. Collective intelligence (CI) is the other cornerstone of AI. Szuba (2001) poses the question of CI being used to enhance AI systems, so its definition and measures of quality would be formalized. This piece raises questions about the trade-off between individual and collective intelligence, and that AI could possibly get improved by the implementation of both those advantages. The AI-enabling technological sub-systems also constitute a critical element in the definition of AI. Torkamani et al. (2017) refer to evidence-based treatments in modern medicine to demonstrate how AI technology facilitates the performance of high-definition assessments of human health. Table 1.1 summarizes the main capabilities of AI, specifying their purpose, methodology, area of research and examples.

Analyzing AI technologies requires us remembering their strengths and weaknesses. Big data, pattern identification, predictive analytics and decision-making are best handled by ML algorithms. ML algorithms usually require humongous amounts of data and are prone to being biased unless properly managed (Bannister and Connolly 2020).

Table 1.1. A framework for understanding core capabilities of AI

Capability Definition Key methods Current research focus Applications References Perception Ability to interpret and understand sensory data from the environment. Sensor fusion, NLP, computer vision Multimodal perception, cognitive computing models, world models Autonomous vehicles, voice assistants, image recognition Chen et al. (2019) and Fung et al. (2025) Reasoning Drawing logical conclusions from available information. Abductive learning, logic programming, symbolic reasoning Attention mechanisms, symbolic correction of learned facts Medical diagnosis systems, intelligent tutoring systems Dai et al. (2019) and Chen et al. (2020) Decision-making Selecting the best action based on perception and reasoning. Decision algorithms, hybrid human-AI models Mitigating cognitive biases, transparent AI systems, collaborative decision-making Business analytics, automated trading Shrestha et al. (2019), Shin (2021) and Rastogi et al. (2022) Learning Improving system performance through experience and data. Machine learning, reinforcement learning, neural networks Integration with reasoning and decision-making, adaptation to new situations Robotics, recommendation systems, game AI Gupta et al. (2022) and Putta et al. (2024)

Conversely, NLP tools are strong in automating communication and processing human language but can be...