Artificial Intelligence in the Information Technology Industry

Subject: Tech & Engineering
Pages: 8
Words: 2219
Reading time:
9 min
Study level: College

Introduction

There are numerous debates on the topic of artificial intelligence (AI) algorithms and devices in the business environment and the global economy today. That is not a surprise, considering the recent progress, success, and demonstrations of AI in e-commerce, healthcare systems, and cybersecurity (Parry & Battista, 2019). These aspects have influenced the speculation that artificial intelligence could usher in unprecedented radical changes in how people work and live. AI is a dynamically changing business operation regarding automation and the need for adaptability, increasing the potential application of AI by companies. As opposed to the common belief that AI is acting as a replacement for ingenuity and human intelligence, it serves as a supporting tool to automate work to increase efficiency in operations and foster business innovations.

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Artificial Intelligence Technologies

Before establishing how AI technologies impact the business world, it is critical to understand the various types of AI predominant in business. Machine learning is a common type of AI applied in business development purposes today. Machine learning (ML) is utilized in the prompt processing of enormous data sets and is a type of AI algorithm that learns progressively and becomes better (Uzialko, 2019). That means, that when a machine learning algorithm is fed with additional data, there is an improvement in its modeling. Deep learning is another type of AI technology that is a more specified model of ML. Hence, it depends on neural networks to influence nonlinear reasoning; therefore, it is key to conducting such state-of-the-art operations as fraud detection. It does this by analyzing various factors concurrently; for instance, for self-driving cars, deep learning algorithms help contextualize information obtained from their sensors, including the distance of other objects, their speed and the predicted location that it would be in 5 seconds (Uzialko, 2019). Deep learning provides excellent promise in the business, and its use is likely to come sooner. The last category of AI is robotics, which intersects science, engineering, and technology to produce robots that substitute human actions. There is a growing concern that robotics will reduce human presence in the business world, thus reducing labor costs. Looking into these technologies will thus help establish the effect of AI in the IT industry.

Applications of Emerging Technologies in different Sectors

Hospitals and the transport sectors are among the large industries whose dependence on information technology has increased over the years. Regarding the use of AI, the healthcare sector has shown increased interest in robot-assisted surgical procedures. Robot-assisted surgery, for instance, is less invasive and involves a shorter patient recovery period (Coombs et al., 2020). There are suggestions that robotic surgery could allow easy learning for the surgeon without long cognitive training, but there is no evidence from literature proving this hypothesis. There has been increased research analyzing the potential of fully autonomous robotic surgery tools. For example, an autonomous robot for mastoidectomy successfully removed bones without causing damage to major structures. However, there must be a human supervisor for the robot due to the risk of clinical error if a wrong starting point is selected. Therefore, although AI is helpful in the business environment, it is clear that it cannot fully erase the need for physical human presence in the work environment.

Machine-to-machine communications and remote monitoring sensors, which eliminate humans while substituting automated functions, are now approved in the medical sector. The presence of sensors for recording significant symptoms and electronically transmitting the data to medical doctors presents a substantial change in health communication, diagnosis and care provision. For instance, patients with heart diseases are currently using monitors to track blood pressure, pulse rate, or blood oxygen levels. The results are transmitted to the physician, who makes adjustments to medications according to the displayed client’s condition. Implantable monitors facilitate routine management of patient disease and therapy. For example, the utilization of pulmonary artery pressure measurement systems has proven to minimize hospitalization risk resulting from heart failure. These devices are facilitating machine-to-machine communications, thus allowing prompt response to potential problems. Automated machines are being integrated into hospitals, such that rehabilitative robots for aiding individuals with specific tasks (Parry & Battista, 2019). While service robots help individualize treatment, companion robots improve quality of life through sociability and interactivity. Through these functions, the integration of robotics and another form of AI is improving medical treatment

In the transport sector, research focuses on human-machine interactions. Habib et al.’s (2017) study of rail signaling automation comparing a realistic automation model and experienced human rail signal operators found that with the increase in automation levels, human operators’ perceived workload decreased and performance consistency increased. A study exploring smart cars found that human drivers’ experiences with assisted technology were positive, and malfunctions were rare (Coombs et al., 2020). However, Clabaugh and Matarić (2018) showed that in case of a conflict during human-computer interaction, it could result in the degradation of performance because the operator focuses on resolving conflict rather than finding an alternative course of action. Conflicts are essential to understand that the user’s actions jeopardize information security and need to be overridden, especially during aircraft flights. Without a proper understanding of automation behavior, humans are likely to ignore the warning and attempt to take control of the automated system, leading to errors that will sabotage the entire IT system used in that setting.

Impact of Artificial Intelligence on Innovation

The diverse AI domains can influence innovation in a number of ways. First, AI can attain super-human production across various human cognitive capacities. One may note that the recent advancements in robotics and deep learning are significant revolutions requiring critical human planning. They apply to a significantly narrow problem-solving realm, such as face recognition; fetching particular objects and other roles. However, additional breakthroughs in this field can lead to a technology that will mimic human subjective intelligence and emotion, thus reducing the need for human presence in the workplace (Cockburn et al., 2018). There is some evidence on applying robotics in manipulating the physical environment that application of robotics beyond manufacturing needs. For instance, advances in ‘pick and place’ capabilities and swift growth in autonomous vehicles prove robotics’ chances of escaping manufacturing and being used more widely. However, looking at it more broadly, AI seems more of a supporting tool than a replacement for human intelligence and ingenuity. Its high data processing and analyzing capability that exceeds the human brain can help streamline the decision-making process and other operations, thus promoting innovative approaches.

The onset of a general-purpose IMI shows an overly unpopular event that can influence economic growth and have an extensive impact on society. As a general-purpose IMI, deep learning will influence the development of institutions and policy environment that enhances innovation to promote completion and social welfare (Coombs et al., 2020). Since deep learning algorithms’ performance relies primarily on the training data from which they were developed, the likelihood that its application in a specific area or company could gain a significant innovative advantage by taking control over data unconventional to conventional economies of scale demand-side network impacts. Such market competition could have numerous consequences. It develops motives for duplicating racing to yield a data advantage in specific application domains, followed by selecting durable entry barriers that influence competition policy. Moreover, this behavior can affect data balkanization in the industry, thus reducing innovative products and reducing spillovers to deep learning GPT (Cockburn et al., 2018). That indicates that the dynamic progression of organizations and policies encouraging competition, data sharing, and openness will be a determiner of economic benefits from deep learning development and use.

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The broad applicability of deep learning and possibly robotics across industries could engender a race in all sectors to develop a proprietary benefit leveraging new strategies. Therefore, the onset of severe learning problems creates competition policy issues. In each industry, companies can develop an edge in the early stages, thus gaining the ability to obtain more data concerning their technology, consumer behavior, or organizational processes. That will enable the firms to set up a deep-learning-driven barrier to entry, ensuring market dominance over the medium term. That indicates that rules enhancing data accessibility are inclined to research productivity or aggregation and show the potential to protect against lock-in and in competitive conduct (Cockburn et al., 2018). Currently, there seems to be a large number of firms seeking to take advantage of AI across different domains, including research on autonomous vehicles, and this high-level activity reflects on expected prospects for considerable market power in the future. Ensuring deep learning does not promote monopolization and increasing entry barriers across sectors is a matter of concern.

Impact on the Workforce and Business Economy

The use of AI is limited to the information technology industry, but one may question whether non-technological factors influence machine-learning implications in the workforce. Substitution of the factors of production is one of these elements. Technologies offering improved results, cost-effectiveness, or both serve as the appropriate alternative. Technology is substituting for labor in different fields, which is dramatically affecting middle-class jobs and incomes. Most large tech companies are achieving a broader economic scale with a lower number of employees. With the high level of accuracy, productivity, and efficiency of operations, robotics, machine learning, and AI could replace humans. For instance, while Google is worth $370 billion, it has only 55000 workers (Zhao, 2018). The study by Manyika et al. (2017) on the effects of automation on work found that existing technology can automate about half the time used in task completion. That indicates the probability of machines replacing people for jobs in the current economy through automation of functions and advanced production methods to operate a business.

With the use of AI in business operations, employees will have fewer manual duties to perform. Autonomous systems can complete repetitive and monotonous work. The same is applicable for back-office activities in service sectors, whereby algorithms can automatically gather data, transfer it from purchasers to seller systems, and find solutions to client problems. When an interface is set between the buyer and the seller’s system, workers will not manually enter data into the IT system. The telecom industry uses AI to reduce customer service employees’ workload in tracking reasons for contract cancellations and enabling effective customer service call management. AI is easing employee workload through these applications, allowing them more free time to use creative or individual recreational activities. With the reduced amount of work comes a reduced need for physical presence in the business world. The change will influence the loss of jobs to some people, but the benefits to firms and employees by reducing workload and labor costs cannot be overruled.

Augmentation is a channel within the production line that artificial intelligence may impact. When it comes to work and capital, companies devote approximately 20 percent of digital investment budgets to artificial intelligence tools (Manyika et al., 2017). If the investments will increase, there is a possibility of new labor and capital investments across economies, hence, promoting efficacy. AI investment also offers complementarities for such other factors as jobs. Coombs et al. (2017) assert that AI transforms knowledge and service work, offering a strategic opportunity for an organization to increase its value. For example, a company could apply intelligent automation to average-income employment to improve its value (Coombs et al., 2017). Similarly, it can exchange AI resources with high-skilled labor, or engage the former with complex and non-routine assignments. However, more research is needed to examine the effects of intelligent automation on the value of a business.

More opportunities are needed for the development of AI infrastructure and entirely monitoring its functions. Currently, there are 10,000 raters working for Google, with their work involving watching YouTube videos and testing new services (Zhao, 2018). From 1980 to 2000, up to 9 percent of the American labor force worked in positions that did not exist years before (Zhao, 2018). That is an indication that investing capital in AI will increase the demand for jobs. The use of AI will therefore redefine existing occupations, augment human capabilities and make employees more productive. The future of work-study by the MGI suggested that sixty percent of industries have 30% of activities that could be automated by adapting new technologies (Manyika et al., 2017). With machines overriding specific actions, employees gain some freedom to engage in more valuable operations using AI tools, thus becoming more productive in tasks that are impossible to automate.

Conclusion

Artificial intelligence has sparked significant transformations in humans regarding technology, influencing new tools that have automated business operations. It is inherent in any organization that seeks to attain a competitive edge. The techniques functional in AI, including machine learning, deep learning, and robotics, have, to a great extent, improved human interaction with machines to facilitate operations, reduce errors, and increase efficiency. Because the world of IT is constantly changing, AI’s impacts on businesses are highly unpredictable. Ideally, common-sense tasks will become easier for computers to process, making robots increasingly valuable for routine operations. AI is making what was previously impossible possible, such as driverless cars, which are now a reality due to access to training data and fast GPUs. With the ongoing development of AI technology, the world will experience new start-ups, business applications, and the displacement of some jobs while creating entirely new ones. Alongside other modern technologies, AI will potentially influence dramatic changes in business environments.

References

Clabaugh, C., & Matarić, M. (2018). Robots for the people, by the people: Personalizing human-machine interaction. Science Robotics, 3(21), eaat7451. Web.

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Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation. NBER Working Paper No. w24449. Web.

Coombs, C., Hislop, D., Taneva, S., & Barnard, S. (2020). The strategic impacts of Intelligent Automation for knowledge and service work: An interdisciplinary review. The Journal of Strategic Information Systems, 29(4), 1-30. Web.

Habib, L., Pacaux-Lemoine, M. P., & Millot, P. (2017). A method for designing levels of automation based on a human-machine cooperation model. IFAC-PapersOnLine, 50(1), 1372-1377. Web.

Manyika, J. V., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017, January 12). A future that works: Automation, employment, and productivity. McKinsey & Company. Web.

Parry, E., & Battista, V. (2019). The impact of emerging technologies on work: A review of the evidence and implications for the human resource function. Emerald Open Research, 1(5), 1-13. Web.

Uzialko, A. (2019). How artificial intelligence is transforming business. Business News Daily. Web.

Zhao, H. (2018). Assessing the economic impact of artificial intelligence. ITU Trends. Emerging Trends in ICTs, (1), 1-80. Web.

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