Table of Contents
Artificial Intelligence and Labor
Byrum (2018) elaborates that the world is profoundly advanced in more than two times in the period of the lifetimes of the entire population globally. Due to rapid innovation, no period in the history of the universe has experienced a significant change in technological advancement as the current times. Industrial revolution during the 18th century triggered new mechanization methods and devices powered by steam that resulted in the liberation of slaves from tasks that they were undertaking in various industries and manufacturing companies. Moreover, the second revolution in the 19th century resulted in techniques for mass production that triggered the mass production of products on an unprecedented scale. The innovation of computer systems led to the digital age transformation during the twentieth century. Furthermore, the current twenty-first century has seen the development of smart technological methods that define numerous organizational operations in the world.
According to Frank et al. (2019), smart-based technology is an artificial advancement that lacks various elements that reduce the performances of people. In the current generation, people have various emotions that often cloud their judgments. Machines and robots do not have the aforementioned limitations. Such limitations often limit the operationalization of individuals in various firms. As a result, technology has been perceived to have a significant impact on various tasks that were initially performed by people.
Artificial intelligence and technological advancement have been able to conquer numerous tasks in the past decades. Atkinson (2018) highlights that one can identify that within the next decades, smart technology will be the driving force of numerous activities, which are beyond the limits of the people. It is evident that smart technology will gradually outperform human labor and tasks performed by people (Perisic, 2018). However, human beings are still instrumental in handling complex issues and tasks.
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Byrum (2018) argues that within the next 20 years, artificial intelligence (AI) will be the key instrument in numerous tasks that cannot be performed by people. For instance, Bill Gates suggested that AI has been able to achieve advanced levels of overall intelligence. Computers can understand various information similar to human beings. In the business world, individuals will be required to identify methods of retaining the control of artificial intelligence systems in addition to autonomous technology (Korinek, 2019). Moreover, individuals should decide the level of independence-based on the allowance of the development of artificial intelligence systems.
Griffin (2018) asserts that machines will be fully independent once they can understand and learn in the manner that people do. Such systems will not depend on data scientists and programmers. In these connections, several questions arise on the matter (Byrum, 2018). They are inclusive of; how will artificial intelligence and people get along in business operationalization? Additionally, can people improvise on the chances of the development of a coexistence that is harmonious between them and the systems?
Various firms that include Google developed an initiative that is meant to research on the co-existence of people and artificial intelligence systems. The mission of the project is to undertake research that is imperative for the future of business operations. Byrum (2018) posits that it is a philosophy that was developed at Google that is centered on people. Machine-based learning algorithms solve issues while considering the behaviors and needs of people in general. The method identified above is based on the phenomenon that basic solutions to an issue should not include the assistance of machines.
Notwithstanding, managers can make use of AI technology that could surpass the capabilities of the roles of people in organizations. In most industrial firms and systems, the element of people is the most complex to plan and work with. Individuals working in such environments have lapses in their memories and often guess how to conduct their activities. Byrum (2018) posits that they get tired, and in numerous instances, make mistakes. Furthermore, artificial intelligence is fundamental for the elimination of human-based factors in decision-making that involves statistics and probability elements. Such systems incorporate science and essential data to make important and essential decisions within an organization. Artificial Intelligence gathers data that is available from various business procedures and optimizes them to issue the best probable outcome in an advanced decision-making process.
The future of AI systems will enable machines to learn and further adapt to various tasks without the intervention of custom-based coding and programming for every task. General artificial intelligence technology can use the environment within which it is operational to train its intelligence and open vast opportunities that include optimization of both minor and major tasks in a company (Byrum, 2018). In this connection, harnessing artificial intelligence in an organization will be basic and simple as using a website. Nonetheless, sophisticated learning of AI technology is still in a developmental stage.
The implementation of artificial intelligence in a company in the current times is a critically involving strategy that requires solutions that are custom developed to provide solutions to specific issues (Byrum, 2018). The technology incorporates algorithms for machine learning. To develop specific tools for artificial intelligence, executive managers should assemble a particular team within their organization that can identify various elements required to address particular business issues. Such a move will involve gathering information and developing an algorithm that is required to make rational judgments because of statistics.
With the incorporation of the above elements, AI can conduct numerous trial-error based experiments to focus on the most appropriate cause of action to be undertaken in a particular business operation. With current processing power systems, AI technology can undertake tasks performed by people in a short time. Building an artificial intelligence project includes experts for the particular business procedure, programmers and mathematicians that can work in cohesion to develop the elements that are needed. Byrum (2018) asserts that such talent is often outsourced by organizations.
The workforces in organizations know the subject-matter of the business processes of the firm. Nonetheless, they might not have the talent required in the development of algorithms that incorporate machine learning. As a result, hiring specialists to undertake such roles is imperative in the firm. Moreover, open innovation can allow managers to develop AI instruments with the incorporation of crowdsourcing. Being patient is crucial to the success of the development of AI since the process takes time to deliver. Moreover, it requires substantial time and resource management to effectively divide and share tasks before using crowdsourcing solutions (Byrum, 2018). Open innovation is not an easy fix for businesses since AI is still in the development stage.
Change Expectations for Industries
Expectations for artificial intelligence on organizations are high in numerous industries globally (Ransbothham et al., 2017). Within the telecommunications, media and technology industry, an average 70% of players expect significant effects from the incorporation of AI. The statistic is an increase of 52 percent from players that have recorded significant effects from the incorporation of AI.
Such bullishness is evident regardless of the geography or size of the company. In other companies, respondents in the research by Ransbothham et al. (2017) respondents have high expectations for the incorporation of AI in business processes. Moreover, 15% of the overall respondents in the research reported a huge effect on their current operations that include the incorporation of AI. Also, most of the firms foresee significant impacts on manufacturing and operations, information technology, procurements, and customer-based activities. Business outsourcing organizations and providers are a key example of the potential of artificial intelligence services
Expectations and Hopes for Artificial Intelligence
It is a combination of hope and fear that drives the futuristic and novelty perceptions of AI integration that is popularized in various media outlets. Numerous organizations are incorporating this technology to advance their business operations. Competition is spurring organizations towards the incorporation of AI, and there is urgency in most industries to include the technology so that they are not left behind. More than one-third of respondents in a survey conducted by Berlucchi et al. (2016) believe that artificial intelligence will allow technology organizations to disrupt the operations of their industry. Furthermore, 44% of the respondents in the survey suggest that the implementation of AI will result in their enterprises being vulnerable to raiders that use technology. Moreover, up to 46% of the general respondents believe that start-ups will shake the markets on an upward scale.
Notwithstanding, most of the respondents in the research by Berlucchi et al. (2016) expect artificial intelligence to benefit their crucial metrics of performance within five years that includes revenue, operationalization, decision making, and quality control. The most significant impact of AI is in user experience. The use of AI causes efficient and productive interactions in customer service. Moreover, the most significant rewards are in the products and services organizations issue based on data analysis gathered from clients of the company.
Machine Learning
Methods for machine learning are applied to the available data for various business procedures to enable the computation of prediction; AI had built several algorithms for machine learning such as decision trees and neural-based networks (Maruti, 2019). The aforementioned elements are incorporated in libraries. Learning enables a particular agent to improve on its performance gradually. The process of learning is based on the perceptions of the agent on the world. Research for machine learning distinguishes between unsupervised and supervised methods for learning (Koehler, 2018). For the case of unsupervised-based learning, an AI agent can learn essential patterns from a particular input in the absence of feedback. Moreover, in supervised-based learning, AI agents can learn from specific examples of pairs of both input and output.
The results of the process of learning are evaluated based on test information and data on where a particular agent outputs a prediction based on the input it has received. Calvano, Calzolari, Pastorello & Denicolò (2019) posit that algorithms vary in output and input representations, the models, and the process of learning. Various researchers have developed measures that include recall, precision, and accuracy to describe the qualities of various algorithms (Koehler, 2018). Also, statistical methods issue information on the intervals of confidence. These aforementioned measures are incorporated when deciding a particular method for machine learning to apply in a particular organization.
The Utility Theory, Prediction, and Decision
Koehler (2018) suggests that artificial intelligence is a wide research field. Most research on AI includes rational thinking as a key prerequisite for various intelligence behaviors. AI involves the study of rational thinking and actions within the array of prediction, action, and decision-making. Moreover, the separation of identifying predictions or forecasts from identifying particular conclusions that result in a choice of a specific action leads to a particular behavior. Realistically, such events are entwined in complex ways, and some of the loops often overlap in the basis of human behavior. Nonetheless, separating such events is critical to an in-depth understanding of the tools involved in the implementation of AI systems. Also, the separation is important in the development of technologies for automation and support (Thompson, Li & Bolen, 2019).
AI incorporates the “decision and utility theory” in the representation and computation of preferences of agents of rationality to determine the events that an agent can undertake when ensuring the maximization of utility functionality. The theory identified above is founded in economies and researches circumstances where the aforementioned agents should deal with the element of uncertainty on the current state of the globe (Koehler, 2018). Also, the theory investigates how agents should handle conflicting goals; for instance, whenever the agents seek the maximization of profitability while minimizing risks.
The decision theory enables agents to evaluate conflicting and uncertain circumstances under an infinite or finite horizon for decision making and particular preferences. Koehler (2018) explains that such preferences are often represented as reward or utility functions, which allocate numeric values to probable conditions of the globe to convey the world’s desirability. In most instances, rational agents point out actions based on maximum utility. For instance, an agent could choose various measures that allow for the maximization of expected reward or utility that he/she wants to attain. Therefore, the function of utility establishes the level of risk of an agent that includes being neutral or averse.
Furthermore, the utilities are designed in the assumption of a ‘closed world.’ In such a case, they are caught for the known section of the globe and are required to disregard the elements of the unknown. Moreover, the theory identified above is normative. It is used to describe the behavior of a rational agent. It is not used in the process of describing the behavior of people. Notably, as observed in several instances, decision-making for humans’ diverges from various methods applied in theories of AI and people often pose an altruist behavior (Koehler, 2018).
Decisions undertaken by the AI-based systems can be analyzed by making a comparison with best practices examples. Such an approach can be compared with validation of the algorithms of machine learning on test information and data. The evaluation is designed to make a variation of the decision parameters and explore how critical outcomes are based on changes of utilities and probabilities that have been assigned. The models for decision-making can underlie various decisions that are undertaken in business procedures. For instance, to sell or buy stocks or providing advice to a client (Koehler, 2018). AI technology benefits from models of theoretic decision-making to deal with various information and find particular queries for the improvement of the knowledge of AI systems on the world. Enterprise trends such as cognitive-based computing offer linkages for decision making.
AI and the Global Economy
The “World Economic Forum” suggests that the economy of the world will grow to more than $16 trillion due to the application of AI in various business processes. It is an equivalent of the addition of the productivity of an economy the size of India and China. Half of the gains in the economy are predicted to result from the increase in labor-based productivity and customer demands for products and services that incorporate AI technology. The report shows that advancement will be greater in China, which will have a boost in their GDP by an average of 26%. Moreover, North America is second with 15% (Byrum, 2018).
China is experiencing a better share of the boost since it is a leader in the development and advancement of AI technology. The befits if the incorporation of AI technology is predicted to be equally distributed in various sectors and industries with healthcare, finance, and retail expected to reap the benefits of the technology the most. Nonetheless, the challenge of such predictions is that they incorporate the current technologies and economic order as their baseline for prediction. Automation is perceived in additive approaches as an element for the enhancement of productivity and not revolutionary advancement.
Klee (2016) elaborates that the world has moved to robots and the incorporation of artificial intelligence in various industries. The advancement is in manufacturing and health care. Technology is defining current innovation with algorithms used to predict various business functions. Moreover, Dysart (2017) elaborates that in five years, most agencies and businesses will incorporate various tools for artificial intelligence. He suggests that AI-based technology will vastly infiltrate the public sector. Transportation service providers may perceive intelligent technologies as key solutions to the evolution of their operations through the development of new models and streams of revenue (Bharadwaj, 2019; Aouad, 2019).
Byrnes (n.d) suggests that AI is a key technology that will steer economic growth in the future. There exists a growing concern on the nature of information communication technology (Oke, 2008). The necessity of AI research is motivated by various factors such as the issuance of new players in AI with knowledge of the simple structure of literature and increased interest in AI prompting increased investment on the technologies in the field. Researchers should be aware of studies undertaken by fellow scholars and share their knowledge of the field. Through sharing, knowledge on AI, new approaches and techniques can be designed so that a better understanding can be realized.
Unstructured and heterogeneous sources of data are combined increasingly to develop models of predictions. Most information is uncertain, and its veracity is not sure. Practitioners abstract and aggregate data; however, it does mean that such information is reliable. The prediction’s output model is characterized as uncertain (Koehler, 2018). Most of the techniques for a machine learning center on using a model without issuing the estimates of the potential accuracy (Sharma, 2019). To make a prediction, supervised learning of the machines can apply to the information present for business procedures. Such approaches undergo training with the use of particular data and further authenticated by various test data.
