Artificial Intelligence (AI) as a potential emergent technology for the enterprise

ARTIFICIAL INTELLIGENCE (AI) AS A POTENTIAL EMERGENT TECHNOLOGY FOR THE ENTERPRISE The Name of the Class (Course) Professor (Tutor) The Name of the School (University) The City and State where it is located The Date Contents TOC \o "1-3" \h \z \u Introduction PAGEREF _Toc164884523 \h 2Is it the Enterprise's Newest Emergent Technology? PAGEREF _Toc164884524 \h 3Context and Use Cases: AI Reshaping the Enterprise Landscape PAGEREF _Toc164884525 \h 5Most Common Use Case PAGEREF _Toc164884526 \h 8A Socio-Technical Lens on AI-powered Data Analytics PAGEREF _Toc164884527 \h 8A Bundle Of Methods To Create The Use Case PAGEREF _Toc164884528 \h 10Values Through a Multi-Modal Lens: PAGEREF _Toc164884529 \h 11Issues Through a Multi-Modal Lens: PAGEREF _Toc164884530 \h 12Comparing and Contrasting the Results: PAGEREF _Toc164884531 \h 13Implications For Decision Makers PAGEREF _Toc164884532 \h 141. Prioritize Transparency and Explainability PAGEREF _Toc164884533 \h 142. Invest in User Skills and Training: PAGEREF _Toc164884534 \h 143. Mitigate Data Privacy Concerns and Algorithmic Bias: PAGEREF _Toc164884535 \h 144. Develop a Responsible AI Framework: PAGEREF _Toc164884536 \h 155. Address Job Displacement and Upskilling Initiatives PAGEREF _Toc164884537 \h 15Conclusion PAGEREF _Toc164884538 \h 15 Introduction At the same time, artificial intelligence (AI) is the most often mentioned trend in today's digital transformation. Under the term "Artificial Intelligence," there are many technologies when machines have equipped human-like cognitive functions—the ability to learn, solve quite a bunch of problems, and make a decision on their own. These capabilities have enabled the simulation of human intelligence, and revolution has taken over industries, with most businesses increasingly realizing the transformative potential that AI can bring. Therefore, the discussion on AI is justified for several reasons. First, AI offers the only opportunity to streamline operations and enhance enterprise efficiency. Automating repetitive tasks and analyzing large data would leave AI for human resources to carry out high-level strategic planning. Secondly, it unlocks the potential for generating valuable insights. Its ability to find patterns and trends in data allows businesses to gain deeper insights into their customers, markets, and operations. Lastly, artificial intelligence fosters innovation by ensuring the development of new products and services that are important for businesses' much-needed thrust in a competitive environment. Of course, the transformation is not without the need for an extensive understanding of its possibilities and its limitations. The essay plunges into the world of AI, digging into the essence of AI and its qualifications as emergent technology within an enterprise framework. Each of the following sub-sections will help guide understanding of the place AI is likely to have in businesses. First, we define AI, starting by distinguishing the different perspectives, including an operational definition of the term for the essay. Lastly, we come to emergent technologies, focusing on AI within this concept and basing our argument on Rotole, Heeks, and Martin's (2015) outline. After that, the paper will focus on the practical sides of AI in business, dwelling upon particular use cases, issues, and values for businesses. From there, we will seek to understand the socio-technical approach in the most common use case even more holistically, and then, with such analysis, we will introduce a multi-modal approach to afford a wider understanding of the identified values and issues. Last but not least, this paper will discuss the implications of the findings on responding to the dynamism AI brings to enterprise decision-makers (Benbya et al., 2020). Is it the Enterprise's Newest Emergent Technology? Artificial Intelligence (AI) has become one of the most popular terms used today, and its definitions are rather wide. The widest definition of AI is machines' ability to have abilities judged as intelligent, like learning, solving problems, and making a choice. This definition is quite general. A more focused definition of one key aspect of AI could be "machine learning," referring to the kind of algorithms that improve their ability to perform a particular task without necessarily being explicitly programmed to do so. The essay will broadly embrace a spectrum of AI, including diverse technology that aims at mirroring human-like intelligence in machines. Thus, the business must understand whether AI is a developing technology it must embrace. All these characteristics make emergent technologies new, complex, dynamic, uncertain, and incoherent (Rotole, Heeks, & Martin, 2015). A few possible opportunities and challenges AI presents to the enterprise are outlined below. Indeed, there are powerful arguments for the status of emergent technology that AI holds. The first AI demonstrates novelty to a very high degree. It is a very big departure from the usual computing methods and refers to a situation where machines can do tasks previously considered to belong exclusively to human beings. For example, deep learning algorithms can take up pattern relationships in complex datasets at an accuracy level higher than what human capabilities can achieve in scopes (Stahl, 2021). This novelty opens doors to entirely new applications inside businesses, disrupting established processes and creating innovative solutions. Secondly, AI is, without a doubt, very complex—most often, including the use of very sophisticated networks of algorithms and datasets that work harmoniously for a purpose. A self-driving car, for example, depends on sophisticated interaction among computer vision, machine learning, and sensor fusion to process real-time driving in an environment. Its sophisticated nature brings both opportunities and challenges in return. On the one hand, there's great further potential in automation and optimization, but at the same time, risks occur because the AI systems are too complex and occasionally bring surprises, requiring the development and maintenance of very specialized expertise (Sun et al., 2020). Third, the dynamism of AI is breathtaking. It is very dynamic in that the field is always changing because rapid changes are taking place in algorithms, processing power, and data availability. New frontiers keep coming up, such as neuro-inspired computing and explainable AI. All this dynamism creates a very fluid environment for the business sector that constantly needs adaptation and learning to stay updated with technological innovation (Qasim, El Refae, and Eletter, 2021). However, fully classifying AI as an emerging technology takes a lot of work. And in the strictest sense, it might be argued against, but that is novel. But what is AI today? It has its novelties, and some aspects go back to early research on artificial neural networks and symbolic logic (ieeexplore.ieee.org). Similarly, automation and machines have been introduced to carry out different tasks. Further from this, the long-term impacts are uncertain. Risks of highly advanced AI systems that are being considered, from job displacement to concerns about autonomous weapons, are currently highly debatable. Added to this, in certain cases, there is an inherent 'black box' characteristic of AI algorithms—a characteristic that makes it difficult to understand their internal functioning, and hence, it forces questions on 'explainability' and possible 'bias.' So, this would necessarily call for due consideration and ethical frameworks in developing and deploying AI solutions within businesses (Vijayakumar, 2023). Last but not least, AI also wants a degree of incoherence. While ethical and social implications are still under debate, a considerable mass of research and regulation is growing that tries to address these issues somehow. Some specialized domains, like explainable AI, are under standardization efforts to clarify their scope and remove any possible risk. As a result, AI demonstrates several key attributes of emerging technologies: novelty, complexity, and dynamism. All of these combined opportunities to change the enterprise landscape are huge. However, it should be taken care of considering the risks of uncertainty and, to some extent, even incoherence in development practices (Qasim et al., 2021). Businesses must recognize these challenges and be prepared to make the decisions required to fully harness the potential gains of AI in optimizing operations, unlocking new opportunities, and contributing to a better future led constructively by responsible technological advancement. Context and Use Cases: AI Reshaping the Enterprise Landscape The transformational scope of AI covers many areas, ranging from selling processes to marketing and data analysis within an organization. It's time to consider key application examples attentively, highlighting their pros and cons. AI in data analytics is one of the key areas of using AI in business. The volume of data that modern business activities produce is enormous, and traditional analysis can no longer handle it. AI can be the solution in such situations. Machine learning artificial intelligence can recognize patterns, trends, and irregularities in complicated data sets, helping enterprises obtain more profound information about client behavior, market tendencies, and performance. (Howard, 2019). In particular, AI-driven customer relationship management (CRM) systems can explore customer interactions and the foreseeable needs of the people, which leads to personalized marketing campaigns and quality customer service. (Huang et al., 2020). AI in data analysis services is helping drive this progress with its data mapping techniques, which give useful wisdom for decision making, with the right resource allocation, and thus, high profitability. This big-picture issue is the controversy of data privacy and algorithm bias to be addressed (Munoko, Brown-Liburd, and Vasarhelyi, 2020). Throughout the data and the data collection and processing parts, businesses must ensure that their actions comply with the data protection regulations and actively counteract any pre-thought bias in the datasets, which could lead to biased outcomes. (Eubanks, 2018) Moreover, another topical context includes automation. Tiring and some menial tasks may be automated with the appliance of artificial intelligence. For example, robotic process automation (RPA) platforms can automate repetitive tasks such as manual order data entry, invoice processing, and email responses, enabling people to focus on more significant decision-making activities. AI-gifted automation means improving efficiency, reducing human errors, and giving an employee the freedom of burden. According to studies, repetitive task automation has been shown to make up 80% of most industrial sectors. However, jobs lost and shocks in the workforce stemmed from the potential consequences of this robotization. Organizations should be responsible for developing retraining programs and new job opportunities while implementing automation to limit its negative impacts on the workforce. AI also brings about significant transformation in customer experience (CX) capabilities. AI-driven chatbots are accessible through the clock, respond to basic questions, and sometimes are gifted with the ability to deal with issues. Furthermore, AI can also personalize product recommendations, and similarly, the marketing campaign can be turned based on unique individual preferences (heinonline.org, nd). AI is one of the factors for CX optimization because it has customer satisfaction, brand loyalty, and sales as its outputs. The research done by Accenture (2020) indicates that AI-using p...