Exploring artificial intelligence as a potential emergent technology for the Enterprise

EXPLORING ARTIFICIAL INTELLIGENCE AS A POTENTIAL EMERGENT TECHNOLOGY FOR THE ENTERPRISE Name of Student Course Name of Professor University Date Table of Contents TOC \o "1-3" \h \z \u Exploring Artificial Intelligence as a Potential Emergent Technology for the Enterprise PAGEREF _Toc166767054 \h 31. Introduction PAGEREF _Toc166767055 \h 31.1. Essay Structure PAGEREF _Toc166767056 \h 32. Chosen Digital Technology, Artificial Intelligence PAGEREF _Toc166767057 \h 42.1. Definitions of Artificial Intelligence PAGEREF _Toc166767058 \h 42.2. Importance of Considering Emergence PAGEREF _Toc166767059 \h 42.3. AI’s Emergence Assessment Attributes PAGEREF _Toc166767060 \h 53. Context and Use Cases PAGEREF _Toc166767061 \h 63.1. Different Use Cases of AI PAGEREF _Toc166767062 \h 73.2. Most Common Use Case PAGEREF _Toc166767063 \h 73.3. Values PAGEREF _Toc166767064 \h 83.4. Issues PAGEREF _Toc166767065 \h 84. A Socio-Technical Approach to the Use Case PAGEREF _Toc166767066 \h 94.1. Critique of Evans’s (2017) Model PAGEREF _Toc166767067 \h 105. A Multi-Model Approach to the Use Case PAGEREF _Toc166767068 \h 125.1. Theoretical Frameworks PAGEREF _Toc166767069 \h 125.3. Comparison and Contrast of Results PAGEREF _Toc166767070 \h 166. Implications for Decision Makers and Policy PAGEREF _Toc166767071 \h 177. Conclusion PAGEREF _Toc166767072 \h 188. Reference List PAGEREF _Toc166767073 \h 20 Exploring Artificial Intelligence as a Potential Emergent Technology for the Enterprise 1. Introduction Artificial intelligence (AI) is the central theme of this paper; it is a form of digital technology set to transform industries and redefine how businesses operate. From intelligent automation to predictive analytics, AI encompasses different categories and has far-reaching implications. Its value is that it can drive economic change in any industry (Reim et al., 2020). Through automating tasks, data analysis, and predicting the future, AI allows efficiency gains, innovation, and competitive advantage. On the one hand, its application fuels ethical, social, and economic questions, making it a topic of significant relevance for leaders. The importance lies in AI’s emergent state, for AI continuously and rapidly evolves and exhibits uncertainty and its potential for transforming the existing technology (Dwivedi et al., 2021). Therefore, proactive engagement is critical to harnessing the opportunities and mitigating the risks. Consequently, this essay deliberates on the multifaceted nature of AI, giving its definitions, use cases, consequences, and challenges, outlining the need for policy formulation by decision-makers. This study will shed light on the multi-layered facets of AI adoption, presenting a detailed picture of its advantages and challenges while proposing a normative approach for the ethical and safe implementation of AI in a digital era of fast changes. 1.1. Essay Structure Defining AI and assessing emergence. The following section defines AI as an evolving science and then applies those norms and standards to examine its attributes. Context and use cases of AI. The article covers the contextual aspects of AI applications in various areas, including impacts, benefits, and challenges, using a tabular presentation. Socio-technical and multi-modal analysis. In this part, analytical techniques such as the Evans model are practiced to implement the socio-technical dynamics structure of AI deployment. Practical implications for decision-makers. In this segment, the actual effect of the analysis on the decision-makers is examined, primarily based on the critical factors for AI adoption and actionable insights for AI change. Conclusion. This essay underlines the principal themes stated and focuses on the main implication of AI on businesses and executives. 2. Chosen Digital Technology, Artificial Intelligence 2.1. Definitions of Artificial Intelligence Artificial intelligence creates computer systems that can execute jobs that usually demand human intelligence. One definition, suggested by John McCarthy in 1956, defines AI as “the science and engineering of making intelligent machines” (Coşar, 2023). Here, the focus is on constructing machines with human-like mental abilities through engineering and scientific means, the core of which is equipping machines with intelligence. Contrary to that, a modern definition states AI as “the ability of a machine to replicate intelligent human behavior,” which emphasizes the result more than the process (Wang, 2019). This definition pinpoints the practical purpose of AI, which is to be used as a tool with the ability to think and behave like humans to do certain things without human involvement. 2.2. Importance of Considering Emergence The advent of Artificial Intelligence (AI) has significant consequences for multiple vital parties. Firstly, policymakers, businesses, and researchers should understand AI's emerging technology to predict possible dangers and disruptions (Gregory et al., 2021). Such awareness helps them modify mechanisms and systems to give AI the right place to take a suitable role in the developing intelligent age. Next, technologies that are potentially coming, like AI, experience fast change, uncertainty, and the ability to transform an industry (Duan et al., 2019). Therefore, everybody must actively participate in this process to capture the opportunities and appropriately deal with the risk. In the case of an AI advancement scenario, stakeholders will stay at the edge of the AI’s innovative status. It will, in turn, promote creativity and resilience in their respective areas (Olawale et al., 2023). Another essential fact to remember is that AI is still an upcoming technology that can contribute to effective decision-making in resource management, laws, and strategic planning. Consequently, humans obtaining substantive knowledge of AI gives them tools to approach all its complexity and fuels the creation of a responsible and sustainable future in the digital age. 2.3. AI’s Emergence Assessment Attributes Rotolo et al. (2015) identify attributes that make a technological development emergent. Assessing AI according to these criteria helps determine how it approaches consciousness. For AI’s Emergence Rapid evolution. AI undergoes a rapid evolution process by constantly improving algorithms and computing power. For example, deep learning algorithms have drastically progressed in performance and efficiency in a short space of time. Uncertainty. AI demonstrates uncertainty because future consequences cannot be predicted as AI grows. The debate about AI-powered self-driving cars is an example of this uncertainty, as the arguments revolve around safety, regulation, and society (Rotolo et al., 2015). Transformative potential. The rising impact of AI on the global workforce becomes increasingly undeniable as disruptions across different industries become increasingly visible (Rotolo et al., 2015). A case in point is AI-powered automation transforming the manufacturing processes, leading to higher efficiency and output. Against AI’s Emergence Maturity. Some people maintain that AI has matured through decades, hinting that it may no longer be an emergent technology, which suggests it is mature. Research by Chui et al. (2018) states that AI has spread widely across sectors, proving it is mature in specific applications. Complexity. The AI complexity features, such as ethical, legal, and social implications, make pure emergence theory problematic. When Benefo et al. (2022) consider the moral implications of AI in decision-making processes, the complexity of integrating AI into societal frameworks is highlighted. 3. Context and Use Cases In this regard, the AI use cases dialogue is taking place in a dynamic setting where sectors and social spheres are in the midst of digital transformation. AI's incredible adaptability allows its deployment within many industries, from healthcare, finance, manufacturing, transportation, and other sectors (Devineni, 2024). This adaptability enables AI to revolutionize processes, increase efficiencies, and stimulate inventions in different fields. Diving deep into AI applications is vital because it helps to understand the numerous aspects of human activity and organizational characteristics. Through AI demonstration of its capabilities and limitations, policymakers can identify chances, address worries, and guide technological growth by the appropriate needs and values. 3.1. Different Use Cases of AI Artificial Intelligence (AI) applications can be seen in many different sectors, and this versatility has brought more and more use cases into focus. In healthcare, AI-based diagnostic systems are the prime movers that make early diagnosis of diseases and diseases easy and offer personalized healthcare plans to patients through advanced data analysis, improving patient outcomes (Jiang et al., 2017). The financial sector implements machine learning algorithms for better fraud detection, risk assessment, and algorithmic trading, ensuring safety and boosting economic activity efficiency (Donepudi, 2019). AI in manufacturing helps to improve the workflow by facilitating predictive maintenance, monitoring quality control, and reducing supply chain-related costs. Thus, increased productivity and cost-saving can be achieved (Arinez et al., 2020). Also, AI plays a significant part in transportation by enabling autonomous vehicles, optimizing logistics routes and traffic management, uplifting safety, and decreasing traffic congestion (Khayyam et al., 2020). These diverse applications highlight AI's transformative role, which drives innovation and reshapes traditional practices to meet the new aspirations of a forward-looking society. 3.2. Most Common Use Case In this field, AI is most widely used for predictive analytics. This case is likened to AI procedures used to analyze historical data, identify patterns, and make predictions (Rahmani et al., 2021). Predictive analytics is in every industry and has been widely implemented in financial services, the medical world, retail, and marketing. In finance, predictive analytics helps determine market trends and better investment moves (Cavalcante et al., 2016). In healthcare, it plays the role of predicting health outcomes and identifying potential health risks. Retailers use predictive analytics to predict customer demand and accurately improve inventory control. Marketing can tell consumers about future behavior, and campaigns are developed based on its strategies. Predictive analytics is the central element of the synergy of analytics in business operations, and it highlights the role that analytics plays in the quality of decision-making and business success across different sectors. It will allow the system to get the necessary knowledge from the data and predict the upcoming trends. It is at the heart of AI solutions that bring in straight decision-making and innovation in various fields. 3.3. Values Increased efficiency. AI simplifies operations, automates processes, and improves productivity. Accuracy. AI algorithms that precisely go through data will result in better forecasts and more correct decision-making. Automation. AI does the routine work, which leaves humans to think and create more Cost savings. AI makes it more accurate and quicker by using its efficiency and eliminating errors. Improved decision-making. Artificial intelligence helps generate data-driven insights and suggestions for optimizing the decision-making process and make them data-driven. Enhanced productivity. AI technology enables tasks to be done in less time and more efficiently than humans can do alone (Van de Poel, 2020). 3.4. Issues Ethical concerns. For instance, data privacy, algorithm bias, and ethics in AI systems belong to the moral issues of AI (Ouchchy et al., 2020). Data privacy. AI systems are inclined towards data thirst, which constrains privacy, security, and the abuse of personal data. Algorithmic bias. Machine Learning programs can amplify or even duplicate the training data’s biases, manifesting themselves in unfair treatment or discrimination. Job displacement. AI-enabled automation can lead to job displacement or transformation of job functions and undermine profitable work, workers’ primary source of income. Societal impact. Implementing AI can have a range of important societal effects, such as growing gulfs in income levels and digital divides. Accountability. Through in-depth data and results from Al, it is possible to account for all the changes, innovations, and activities in the busine...