Technologies alter the modern world and introduce radical changes to all spheres of human activity. The scientific progress and emergence of new devices triggered a reconsideration of old approaches and an establishment of new ones. They are characterized by the increased effectiveness, high output, and reduced input, which is vital for most organizations. For this reason, specialists look for new ways to integrate technologies and create a more digitalized environment with multiple options available for workers. Artificial Intelligence can be considered one of the most innovative solutions widely employed by different groups now.
It combines features attractive for organizations, such as effectiveness, reduced mistake rate, and stability. These factors are also vital for the auditing sphere as it contributes to the increased accuracy of conclusion and other benefits. The paper outlines the central aspects of using AI in the field and possible issues associated with its employment.
Definition
Artificial Intelligence is a complex and generalized term that is used to describe a unique technology. It presupposes building machines capable of simulating human thinking and intelligence to perform complex tasks unbailable and unresolvable by other devices (Rochwerger & Pang, 2021). In other words, it is a computer or software possessing some features of a human being with the ability to analyze the situation and make conclusions.
The availability of relevant and credible data is one of the basic demands for the stable functioning of the technology and the absence of critical mistakes (Moroney, 2020). Another vital characteristic of artificial intelligence is the ability to rationalize and offer strategies having the best chances of achieving the existing goal (Moroney, 2020). It helps companies to acquire a competitive advantage and avoid failures because of better planning and strategic analysis.
AI is closely linked with another concept that becomes fundamental today. Machine learning is a case of using AI to provide systems with a chance to automatically learn and evolve using the new experience generated during previous decision-making sessions (Shapiro, 2020). It does not presuppose additional programming or any other interferences, as AI with machine learning is capable of collecting and accessing data, its analysis, and structuring, which helps to improve future solutions and avoid mistakes. The benefits of machine learning include the increased autonomy, fast development, and applicability to various cases and environments (Shapiro, 2020).
Under these conditions, AI acquires the chance to carry on complex and human-like tasks, which offers a wide variety of options for its further implementation. Regarding the audit sphere, using AI is nowadays one of the trends leading to improved performance and better results.
In such a way, the fast spread of the technology and increased attention to it can be explained by the applicability and the opportunity to generate additional benefits. AI is viewed as the alternative to outdated methods that are not relevant today as it can learn and become more effective over time (AICPA, 2020). Furthermore, the growth of AI correlates with the constantly increasing availability of information and the rise of Big Data (AICPA, 2020). The term describes large and diverse sets of knowledge growing at ever-increasing rates (Moroney, 2020). The ability to analyze and process it is vital for the work of modern companies. For this reason, AI, with multiple opportunities for better data processing, is viewed as the potent measure to align the effective work of units and avoid failures or using irrelevant information.
Artificial Intelligence in Auditing
Implementation of AI in the audit sphere can offer multiple benefits. First of all, it is viewed as the central driver of efficiency and productivity in both internal and external audit functions (Ng & Alarcon, 2020). Machine learning tools provide specialists with an opportunity to analyze more contracts in a shorter period of time compared to traditional manual methods (Ng & Alarcon, 2020). It results in the growing effectiveness of a unit and contributes to the higher satisfaction of all parties involved in the process (Ng & Alarcon, 2020). Moreover, using unique pre-selected criteria, AI software can extract and analyze information from lease contracts, which is vital for the work of the sphere (AICPA, 2020). Automation of manual auditors’ tasks, such as documentation and correspondence, also results in better time management and growing effectiveness (Ng & Alarcon, 2020). These features make AI more attractive for the sphere of the audit.
Moreover, the implementation of AI can help to improve audit quality. Analyzing and comparing structured and unstructured data from numerous financial records, AI software can provide auditors with relevant knowledge about different contracts and their current state (Ng & Alarcon, 2020). Another advantage is the ability to make predictions and forecasts using historical transaction data and processing it to offer specific conclusions (Naqvi, 2020). The nature of AI and its features helps to increase the speed of accomplishing these tasks and ensures the higher accuracy of findings (Naqvi, 2020). For this reason, this innovative method is widely used by specialists.
Finally, benefits of using AI in the audit include the ability to identify anomalies and inform specialists about them. These might mean unusual payments, activities not detected by manual auditing, or unusual schemes (Naqvi, 2020). Detection of these features is vital for effective audit and credible conclusions. For this reason, this factor is also viewed as the central advantage of using and implementing AI in the audit sphere. The strong points mentioned above justify the importance of new technologies and help to understand why AI is viewed as a vital tool for the discussed field.
Risks of Using AI in Audit Sphere and Their Mitigation
Nevertheless, AI’s broad use in the audit sphere can also be associated with some risks that should be considered. First of all, employers might be resistant to using this technology to perform their tasks. They might have fears of being replaced by AI, which is one of the popular misbeliefs nowadays (Naqvi, 2020). Moreover, research shows that the unwillingness to use innovative technologies is linked to low computer literacy and awareness about the benefits of this method. Under these conditions, the lack of knowledge about AI can trigger specialists’ dissatisfaction and their desire to use traditional or manual approaches, which are less effective compared to the new ones offered by the AI.
Moreover, the risks of using AI in the audit sphere might involve some ethical issues. The technology enables the analysis of full population data (Dubber & Pasquale, 2021). It might give rise to some privacy issues and claims about the possibility of using AI and machine learning when working with people’s data (Naqvi, 2020). Furthermore, the correct functioning of the technology demands its improved understanding and the stable work of the IT department, which is a central requirement to the successful integration of this innovation in the life of different units. Otherwise, there is an increased risk of mistakes and failures preconditioned by poor alignment and integration (Dubber & Pasquale, 2021). Under these conditions, these risks are vital for considering the establishment of the framework for the increased effectiveness.
The mitigation of these risks is vital for the alignment of the successful work of the unit and the creation of the basis for future evolution. The first strategy is the provision of additional training for specialists to ensure they have an enhanced vision of how the AI can be used in various settings and applied to different environments to ensure better outcomes (Naqvi, 2020). Training will all guarantee that workers will not have resistance towards the use of technologies and possess the right idea of how machine learning and AI should be integrated into the work of their unit. This method will contribute to the boost of performance due to the increased motivation and awareness among the staff.
The Role of AI in Audit Tasks
Planning for Audit Task
Any effective audit process always starts with a well-thought-out audit plan which is vital for attaining success. An auditor is responsible for organizing his/her activities, varying between clients, and establishing risk assessment procedures to ensure positive results (Naqvi, 2020). However, one of the leading audit problems is that plans might have gaps influencing the process and decreasing the chance for success (Naqvi, 2020). The degree of uncertainty and unidentified risks are particular to a certain field and should be viewed as an internal part of the audit process (Naqvi, 2020). Under these conditions, any auditor faces several challenges:
The need to reduce unknown risks.
To consider all blind spots.
The necessity to confirm initial assessments about the industry (AICPA, 2020).
Today, these goals can be accomplished by using unique AI technologies that are available to auditors.
For instance, using specific machine learning and AI applications, auditors can improve planning for audit tasks. The team can enhance the risk assessment, which is a vital part of planning, by using AI tools, allowing to examine all company’s transaction data and acquire insights (Deloitte, 2018). Furthermore, because of the availability of big data portions and the ability to process it by using the most effective risk assessment techniques, AI helps to identify threat areas that can be previously omitted (Naqvi, 2020). It will lead to improved planning and minimization of the chance for a new audit aimed at correcting mistakes (Deloitte, 2018). Under these conditions, the use of machine learning and AI software becomes an integral part of the modern audit sphere as it helps to develop an effective audit plan that can be used by specialists to acquire desired outcomes and avoid critical mistakes.
Execution for Audit Task
Innovations also accelerate execution activities meaning that the audit sphere benefits from the emergence of new opportunities. For instance, this stage presupposes execution of actual audit work, evaluation of the process and control design, testing methods to outline control effectiveness, and examination of performance. Furthermore, there is a need for discussing items formulated as findings and their incorporation into a single summary and report. The scope of this task means that a specialist might face some problems at any of these stages because of the limited opportunity to consider all data or inability to look at the analyzed case from another angle, considering the existence of other sources vital for the scenario.
Application of the AI tools supplier with machine learning capabilities can lead to better performance in the given sphere. Thus, Ng and Alarcon (2020) admit that today auditors have the chance to avoid extra pressure during the execution phase due to the use of computers and unique software. For instance, the emergence of audit assistants contributes to better planning and execution activities via the high level of automation and data analysis (Ng & Alarcon, 2020). A specialist can use a set of different techniques, such as reporting and data structuring, by employing the AI tools available for them. It will improve outcomes and ensure the lack of gaps or failures.
Moreover, Ng and Alarcon (2020) state that by applying the innovative tools to the execution phase, specialists acquire the chance to reduce time, input, and maximize output due to the availability of data and new analysis methods. Under these conditions, using AI is recommended for the execution of audit tasks as it offers multiple benefits and helps to minimize mistakes or failures in the process.
Reporting for Audit Task
As stated previously, reporting is another vital part of the audit process as it presupposes structuring data, its synthesis, and discussion by specialists. The traditional report includes the auditor’s opinion on whether a unit’s financial statements comply with the existing regulations and offer data sets to prove this assumption (Ng & Alarcon, 2020). The document is critical as banks, creditors, or other agencies can require an audit of financial statements to prove the current state of a person or a firm (Naqvi, 2020). Under these conditions, the phase becomes vital for the whole process as it summarizes the structures conclusions.
The existing research body proves the idea that AI can be applied to reporting to attain better results. For instance, Naqvi (2020) says that using machine learning and specific software, it is possible to make reports more comprehensive and understandable for other individuals. At the same time, AI can structure the presented data and organize it into specific portions leading to a better vision of a complete image and every single scenario at the same time. Ng and Alarcon (2020) also state that AI can offer special patterns that can be used by an auditor to perform the planned task, process data, and organize it. Under these conditions, reporting using the AI becomes simpler and demands less time.
Altogether, works show that the discussed technology has multiple applications in the modern audit sphere (Naqvi, 2020; Ng & Alarcon, 2020). It helps to minimize risks by their better assessment, investigation, and discussion. Furthermore, most researchers state that the use of AI leads to more effective audit as it helps to improve areas that are traditionally weak or demand additional investigation or intervention.
The Use of AI Techniques in Performing Specific Audit Tasks
Fraud Investigation
Fraud investigation and detection play a central role in modern audit processes. It presupposes the ability to find activities or inappropriate financial data, investigate them, and use specific measures to manage them (Ng & Alarcon, 2020). One of the possible techniques to assist in performing this task is the use of Intelligent Analytics and AI. Combining the supervised learning algorithms and most relevant methods for fraud detection, this innovative tool helps to acquire an enhanced understanding of companies’ behaviors. It leads to the better tracing of the slightest changes and detection of frauds, which is vital for audit (Ng & Alarcon, 2020). AI also helps to free up fraud analysts, meaning they can perform other activities.
In such a way, researchers agree that fraud investigation benefits from the rise of the given technology. Naqvi (2020) says that AI helps to complete data analysis within milliseconds, which is vital for detecting and analyzing complex patterns and concluding about their nature. Additionally, AI can help to eliminate tasks demanding much time and provide fraud analysts with a chance to focus on critical cases requiring their interference (Naqvi, 2020). Under these conditions, fraud investigation benefits from the development of AI as it offers new ways to work with data and process it.
Risk Assessment
AI is also one of the key drivers transforming risk assessment strategies. Machine learning models can analyze large amounts of data in a reduced period of time to acquire results vital for making decisions and selecting the correct approach to work with risks and mitigate them. For auditors, the employment of AI is a key to better planning as they acquire the chance to use results offered by AI software and use them in making plans and executing the major processes. Effective risk assessment demands the high accuracy of data and its analysis, which is achieved by using innovative solutions.
The current body of literature also emphasized multiple benefits associated with the use of AI regarding risk-assessment procedures. Naqvi (2020) states that the availability of numerous machine learning models provides auditors with a chance to perform a better risk assessment and conclude about the exiting risks avoiding gaps in knowledge or failures. AI and risk management align when the necessity for handing and processing unstructured data emerges (Bullock, 2019). Under these conditions, most AI techniques can be applied to the audit sphere to outline existing risks by reviewing available data and structuring it.
Documents Review
Documents review also benefit from the emergence of new technologies. Ng and Alarcon (2020) outline an AI-powered document review technology allowing to process entire sets of documents. These might include invoices, expenses, texts, data collections, and other descriptions (Ng & Alarcon, 2020). Applying specific software to such cases, an auditor acquires a chance to reduce the time needed to work with this information, and, moreover, he/she can benefit from the increased effectiveness guaranteed by using AI. Furthermore, about 72% of financial controlling and documentation can be automatized using AI document review technology (Ng & Alarcon, 2020). It means a significant increase in the effectiveness of investigations and the ability to free specialists to work on other critical tasks.
At the same time, AI ensures the better accuracy of documents’ processing and analysis. Following the result of the study by Sadangi et al. (2020), auditors make more mistakes when reviewing specific documents compared to the machine learning models. Under these conditions, it is possible to conclude that document review can be improved by applying AI tools focused on better data analysis and processing. Most researchers support this idea and emphasize the need for the further development of this method to support the evolution of the sector.
Internal Control Evaluation
Evaluation of internal control is another activity vital for the audit sphere. It presupposes an examination of how the organization manages its internal control systems and deals with various operations (Huang & Rust, 2018). A potent internal control system helps to reduce the risk of frauds, failures, or inappropriate financial data (Huang & Rust, 2018). The scope of the activity leads to the emergence of new methods to improve outcomes, and using AI is one of the possible options. Sadangi et al. (2020) say that integrating this method allows auditors to align better internal control systems as they are provided with relevant and topical data about the work of organizations and factors influencing their internal processes.
Furthermore, using innovative technologies, specialists acquire the chance to create the basis for future improvement. Winslow (2017) is sure that internal control evaluation with the help of AI will provide this technology with new experiences vital for processing data, and it increased accuracy during new audit sessions. For this reason, by applying machine learning tools to different cases, auditors promote better learning and can expect benefits. In such a way, using innovative technologies in the sphere of audit, individuals can reconsider old approaches and replace them with new ones, more effective today.
Conclusion
Altogether, the existing body of literature shows that AI is a critically important technology that can be used in the audit sphere to increase its effectiveness. By employing machine learning models, a specialist can improve their planning, executing, and reporting activities. Moreover, most authors view AI as the future of the audit sphere as it offers multiple techniques aimed at simplifying tasks and promoting better outcomes. The further digitalization of society creates the basis for the wider use of AI and machine learning applications, meaning that the sphere of the audit will continue to alter and acquire new features.
References
AICPA. (2020). Audit and accounting guide: Not-for-profit entities. Wiley.
The technology uses scientific knowledge to invent, design, and monitor the equipment which helps to make human lives easier. Some people argue that technology can be detrimental to human lives. It is because some waste technological products do not decompose and end up causing soil and water pollution. However, the benefits outweigh the disadvantages when we consider cutting-edge technologies. The cutting-edge technologies devices use high techniques and employ high level and most current Information technology to achieve current developments. The innovative and leading industries in technology are the ones referred to as cutting edge.
I am going to discuss two examples of cutting-edge technologies, that is, Artificial Intelligence (AI) and Blockchain. Performance and tool life are mostly determined by cutting-edge technologies. Good technology is essential in improving process reliability, tool life, and wear and tear resistance. These two examples have improved the efficiency of our daily lives in terms of money transfer and medical practices.
Blockchain
Purpose
Blockchain is a digital platform for recording transactions. The transaction is made without a central authority such as the bank or government and it is distributed without a central repository. It helps to facilitate security, data management, and provenance, and it is capable of transforming healthcare (Leible et al., 2019). It is also used in healthcare by many stakeholders to maximize business processes, improve compliance, enhance patient outcomes, reduce costs, and improve the use of healthcare data. Cryptocurrency uses blockchain technology as applied in bitcoin which has popularized the technology (Leible et al., 2019). The main purpose of this technology is to enable information to be recorded and shared without editing. The timestamp in the documents cannot tamper.
Effectiveness
The technology enables a group of people to record the transaction in a common ledger within the group. It uses electronic money transfer which is protected in cryptographic and is untraceable with the central authority (Leible et al., 2019). The users can sign in their accounts and transact to another user and the blockchain records the transaction which is viewed by other users. They validate and verify the transactions independently. It is impossible to alter or forge the transactions because of the cryptographic mechanism (Leible et al., 2019). It is economically effective in terms of exchange value and banking.
It is self-governing and there is equality and shared ownership among the members. There is no need for an intermediary therefore it reduces the transaction cost (Prussi, 2020). It is less vulnerable to cybercrime and fraud than banks because of the algorithm used in verifying and validating accuracy.
Unanticipated Consequences
The blockchain can be used to record sensor and human input from the real world, but it is difficult to determine if the data reflects the real world. The systems might malfunction and record inaccurate data and humans can record wrong information either intentionally or unintentionally (Prussi, 2020). There is also an issue of trust concerning the third party to certify the transactions. Trust should be an issue in the blockchain network. There could be flaws in the cryptographic mechanisms, loopholes in the trust of bug-free and correct operation (Prussi, 2020). The developers produce bug-free software and also ensuring the users in the blockchain are not colluding secretly.
Empowerment
It helps business people to avoid counterfeit products. High-value items such as diamonds rely on certificate papers which might be tampered with or get lost (Xu et al., 2019). It is hard to determine whether the certificate is fake or genuine. In using the blockchain, the buyer can easily know if the seller is the actual owner of the product. It is also essential in processing paperwork in ocean freight. International sailors have many trails of paperwork. It increases the cost of paperwork processing and some might be subjected to fraud (Xu et al., 2019). Blockchain was a solution since it connects many sailors in a global network of ports, shippers, carriers, and customs.
Disempowerment
The banking sector is highly affected by blockchain technology. There is a high negative impact on the development and adoption of the technology. Banks act as the third party and paid a commission for their purpose but blockchain hinders the opportunity since there is no third party. The transactions are made between the users with ownership (Xu et al., 2019). Another part that is disempowered is the government since the money cannot be traced. It is difficult to collect taxes and trace malicious activities in the country.
Impact on Human Condition
Blockchain can be used to manage solid waste by changing the mode of payment and replace the current system of using paper. The system used green coins which have effects on human health. Blockchain avoids waste through the use of social currency. It saves time by avoiding paper trail and improves the quality of life (Prussi, 2020). It also improves clinical trials because of transparency.
Artificial Intelligence
Purpose
Artificial intelligence (AI) is a branch of information technology that is focused on building machines that can perform tasks that require human intelligence. It is a technology that tries to replicate human intelligence. Its purpose is to enable machines and computers to perform intelligence tasks such as problem-solving, communicate and understand with humans, perception, and decision making (Vinuesa et al., 2020). AI serves different industries such as healthcare and finance. It helps the healthcare in treatment and prescribing dose for patients and surgical procedures. AI mimics humans’ cognitive activity in doing both simple and complex tasks.
Effectiveness
AI helps the industry to improve the efficiency in manufacturing, therefore, improving performance and promote productivity. It helps managers to make complex and critical business decisions (Vinuesa et al., 2020). Automated machines produce quality products efficiently and faster. They are capable of forecasting the demand of a product by testing the mathematical models of production and possible failures. They can easily predict maintenance therefore the organization can plan for the maintenance procedure (Vinuesa et al., 2020). It is economical in many ways since it is essential in saving money since many operations are performed by machines and computers.
Unanticipated Consequences
AI acquires knowledge from the data incorporated and there is no other way. Human beings are prone to mistakes and any inaccuracy will reflect in the results. Any additional knowledge will be incorporated separately (Zawacki-Richter et al., 2019). The AI system is trained to perform a specific task, they are specialized on a single task and they are far from acting like human beings.
Empowerment
It is essential in the medical sector since it can provide X-ray readings and personalized medicine. They can be programmed to act as personal assistants to act as life coaches, to remind you to take pills and eat healthier (Zawacki-Richter et al., 2019). In retails, there is virtual shopping that offers recommendation and purchase options for consumers. The site layout and stock management can be improved with AI.
Disempowerment
AI is disadvantageous to some employed people because it takes their job. It is labor and capital intensive. The level of unemployment continues to rise globally. AI can be dangerous when it involves the task of creativity and sympathy (Zawacki-Richter et al., 2019). Some people may suffer since their demands may be unattended due to programmed machines. The small manufacturing companies are facing stiff competition since they have to improve the technologies but the cost is too high.
Impact on Human Condition
Many technologies are invented to serve human beings but they later turn against us. It has improved the immunity of humans and has enhanced productivity, efficiency, and accuracy in daily activities (Zawacki-Richter et al., 2019). AI has helped in saving people’s life because it is to detect cardiac arrest. It is capable of assessing the emergency call voice and determine the medical state.
Conclusion
In conclusion, cutting-edge technology has improved human’s life and made it easier. Many things can now be done efficiently with minimum time. In blockchain technology, cryptocurrency can be used in the future as a common currency since it is used in all operations. It has helped to reduce the cost in the mode of payment and fraud. Although it harms banks and the government it is better to serve the interest of many. In AI, it has helped industries to save their cost in terms of employment. Machines have been seen to produce quality tasks compared to human beings. The level of accuracy is high depending on how it was programmed.
The field of Artificial intelligence (AI) is one of the newest areas in science and engineering. Intelligence can be measured in terms of rationality, indicate the ideal performance, or in terms of fidelity to human understanding. When explained in terms of thinking critically, AI is the desired outcome of human effort to make computers think, portrayed as machines with minds in the literal sense. When in action, AI is the art of developing machines that execute tasks which require human creativity. When described as reasoning, AI is the study of computer processes that make it possible to comprehend, think, and act. When illustrated as acting rationally, AI is the study that integrates intelligent behavior in artifacts.
Artificial intelligence is exhibited by computational appliances and does not necessarily involve consciousness and emotions. Thus, AI is used to style computers that copy intellectual operations that people relate to a person’s mind, including studying and unraveling problems (Manoharan, 2019). Artificial intelligence has been subdivided into different fields, working independently and founded on technical concerns like particular objectives, e.g., robotics, using specific tools.
The modern abilities associated with artificial intelligence include:
Human speech understanding.
Completing higher levels in strategic gaming systems like chess.
Imperfect information systems like self-driving cars and content supply networks, and military imitations.
AI techniques have gained a revival with increased computer ability and massive amounts of information in the twenty-first century. Artificial intelligence techniques have been incorporated in the technology industry, involved in solving computer science, operations research, and software engineering problems.
Autonomous vehicles have been researched and have been on the rise in recent years. They have been shown to offer many benefits to society, from improving traveling to supporting the environment’s safety. It is essential to consider that when AI vehicles can minimize human driving risks, the future will be realized if anticipatory research is conducted today (Manoharan, 2019). The paper aims at discussing the description of an AI vehicle, methodologies used in AI cars, practical analysis of the AI cars, the advantages and disadvantages, and future research about AI in self-driving cars.
Background
The aspect of autonomous cars may look to have an impossible future. Still, from the evolutionary path from Leonardo da Vinci’s invention to the current ones, there is hope that it could be achieved. Companies like Mercedes, Tesla, and Ford are competing to develop autonomous vehicles to transform the consumer world. (Batista, 2018). For instance, Ford has increased its investment by triple in its autonomous vehicle project and is conducting tests on 30 autonomous vehicles in California, Arizona, and Michigan, considered Ford Fusion hybrids.
History of Autonomous Vehicle Technology
Leonardo da Vinci was among the first people to practice Artificial intelligence about 1500 centuries before the automobile. He designed a cart that moved without exerting any force on it. Springs subjected to high tension generated power to the cart, and its steering was set in advance, and the cat moved along in an already determined path. In 1868, Robert Whitehead then invented a torpedo that was able to propel itself underwater(Batista, 2018). Later, under his guidance, it resulted in the development of other weapons and autonomous devices.
In 1933 Mechanical Mike aircraft autopilot was designed by Gyroscope Co., who used Mechanical Mike as a prototype autopilot that fled a plane for 13000 miles around the universe and tracked the plane’s movement and integrated it with controls that ensured accurate direction. Teetor, an engineer in 1945, developed a cruise control which was aggravated by the fact that he experienced a rocking motion while driving a car with his lawyer. James Adams developed a self-driving wheeled vehicle in 1961 and was fitted with cameras, programmed to move in a specific solid white line on the ground (Batista, 2018). Currently, this technology has been observed to be vital in autonomous vehicle technology.
Tsukuba Mechanical engineering Company in Japan designed an autonomous passenger vehicle in 1977 that was capable of recognizing markings on the city streets while moving at a speed of about 20 miles per hour. In 1987, Ernst Dickmanns, designed micro-processing modules to detect objects on the streets. His major invention was on imaging vital to self-driving cars to determine potential dangers and locations (Batista, 2018). General Atomics MQ-1 Predator was designed in 1995; it was a crewless aircraft that moved around the world hotspots for 14 hours. It’s integrated with technologies that have been used by cars, for instance, radars that can detect clouds, imaging cameras with thermal capabilities, and moved at dusk.
The U.S Department of Defense Research Arm (DARPA) funded several competitions that spearheaded this technology from 2004 to 2013. In 2004, a challenge competition was conducted to test navigation on approximately 150 miles of arid paths. None of the cars completed the route, but these challenges have helped discover many capabilities (Simons, 2020). The challenge of 2007 led to a prolonged urban environment where four cars could allot a six-hour time limit. In 2015, Tesla company designed a semi-autonomous Autopilot feature that has led to hands-free control and freeway driving for highways and was in the form of a single software update (Simons, 2020). Additionally, the same year, the University of Michigan’s MCity was launched as a world-class test organization for autonomous vehicle technology, and ford became the first to test their vehicles.
Levels of autonomy in self-driving cars
The U.S. NHTSA has automation levels from level 0 to level 5, and people drive through driver assistance technology of autonomous vehicles. They include Level 1, a modified driver Assistance system (ADS) that helps the driver with braking, steering, speeding but not at the same time. The vehicle entails rear-view cameras, seats that vibrate, issuing caution alerts on shifting out of the traveling lane. Level 2 is an ADS capable of steering braking or accelerating simultaneously, whereas the operator is aware but acts as the driver. Level 3 is capable of conducting operational roles in specific scenarios like car parking. The operator must be cautious about retaking charge in such situations, whereas he remains the primary vehicle driver. Level 4 can conduct all operational roles and observe the driving environment in various conditions. Under such circumstances, the human operator doesn’t need to concentrate fully. Finally, Level 5 motors ADS performs all the driving in all situations. And the people present are only passengers but not drivers.
Disadvantages of AI in Self-driving car
The AI technology used in autonomous cars has the following demerits: Self-driving cars without human drivers could frighten those experiencing it for the first time (Batista, 2019). Hacking potentials can arise that could lead to interference with this project, loss of jobs since most people depend on driving activities as their source of livelihood, loss of driving skills among most people due to the automation of the technology; hence the skills may fade with time, the technology is not suited for all-weather for instance during winter, and rainy season it could pose a great challenge and finally a lot of resources is being used in the projects and will be required due to the need of new road layouts and infrastructure.
Details and Description
AI technologies are used to power the driverless car system. Manufacturers of these cars utilize an enormous volume of information from image recognition structures and neural networks, and AI to make autonomously driverless systems. The neural networks recognize pattern data that is nursed to the AI algorithms. The information fed includes images from interior cameras in driverless cars. The neural network then uses these images to identify the surroundings like curbs, pedestrians, street signs, traffic lights, trees, and other driving environment components.
The Waymo Google autonomous car project is an example of driverless car technology. The vehicle uses a combination of devices, lidar-light recognition, and vacillating expertise comparable to sensor and camera. It syndicates data generated by the structures to identify the surroundings and forecast what those items might do next in a fraction of seconds. The more the structure runs, the more data a system can integrate into the deep learning algorithms, making more nuanced driving adoptions. The following is how the driverless car system works; the passenger sets a destination, and the vehicles calculate the distance. The lidar device examines a 60 meters array on the car, and an active 3D map is created on the current car’s environment. The sensor is fitted on the left rear wheel and examines the drive detecting the car’s location concerning the 3D maps. Distance to the obstacle is calculated by the radar system positioned on the rear and front bumpers.
All the sensors are connected with AI software, collecting input from interior video cameras and google street view. It stimulates the human perceptual, makes decisions using the control schedules, and deep learning in the driver’s regulator system such as brakes and steering. In facilitating advanced notice of things like traffic signs and lights, the system consults with google maps. The system also has an override function that enables humans to control the car (Simons, 2020). Available features in driverless cars by 2019 are hand-free steering that centers the vehicle without the driver facilitation. However, the driver must pay attention, ACC down to a stop that inevitably preserves the selectable distance between the car in front and the driver’s vehicle, and lane-centering steering that mediates when the driver crosses lane patterns by robotically bumping the car toward the opposite lane marking.
Methodology
The autonomous navigation of robot cars is attained through continuous interaction between intelligence, perception, and action. It needs the implementation and derivation of well-organized sensors that are real-time and based on controllers. Successful control algorithms for this technology should emulate the means humans are operating the human-crewed vehicles. Also, the fuzzy logic technique is applied whereby it entails reasoning that is probabilistic rather than fixed. Undefined logic variables may contain a truth value ranging from 0 to 1. It can also handle the concept of partial truth. Its truth value may vary from entirely true to completely false (Manoharan, 2019). The system can alter and affects its environment by reacting through the effectors. An appropriate rule is selected in the fuzzy controller that later provides the asymptotical system stability.
Practical Analysis of AI in Driverless cars
The main practical application of AI includes; scene recognition, lane recognition, obstacle recognition, etc. Obstacle detection technology has been used to analyze the target area, which may block the vehicle’s normal driving. The process has been facilitated by sensors such as; vision sensors, ultrasonic sensors, radar sensors, and leader sensors fitted on the driverless car. The technology is based on vision sensors and comprises monocular and binocular visions (Manoharan, 2019). A practical analysis is done to ensure that binocular vision obtains the 3D information of the scene directly and supplementation of the geometrical relationship between obstacle and road surface.
Scene classification integrates information of the whole image and their relationship to holistic output. On the other hand, scene understanding divides meaningful regions labeling them with different semantic classes. Practical analysis in lane detection is divided into two categories; one stage and two-stage methods. In the one-stage method, parameters are directly output above the lane through the deep network (Simons,2020). The two-stage process involves two steps, i.e., semantic subdivision, which is done through the deep network to output the lanes’ pixel assembly. A curve through these pixels is installed to obtain the lane parametrization.
Advantages
Improved Road Safety
Humans fear the issue of technology taking over completely and don’t want to surrender control. It must, however, be understood that self-driven autonomous vehicles improve the safety of traveling and reduce accidents. The U.S. Department of Transportation (NHTSA) reported that a more significant percentage of road accidents were attributed to human errors and poor decisions. Other causes like environmental factors and machine malfunctioning contribute to a tiny percentage of the casualties. With the introduction of self-driving cars, human drivers will be eliminated. This interprets the reduction of human-caused fatalities that make up the larger percentage of the accidents experienced.
Reduced Traffic
With humans being the leading cause of accidents, they are also the leading cause of traffic. In most cases, it only takes a careless driver to make a wrong call, like a slam on brakes or cut another driver off a busy road to cause a traffic jam which could have a rippled effect that would go for miles. When autonomous vehicles are incorporated into the system, they will run on a network that would enable communication between them and result in a well-coordinated transport system that avoids traffic jams. The vehicles would no longer be controlled by individuals but rather a plan that would enable them to work as a unit.
Convenient Parking
It is always challenging to find a parking spot, especially in a large metropolitan city where parking lots are few with many demand vehicles. With AI self-driving cars, it can be convenient because they can park themselves. Therefore, one does not have to be present for it to park, which saves time. These vehicles are also convenient because they can park closely at tight parking lots. After all, humans won’t have to worry about getting in and out of the vehicle in a parking lot.
Environmental Benefits
These vehicles help reduce carbon IV oxide emissions, which will reduce the global warming effect. The petroleum-dependent transport system contributes a significant percentage of the nation’s climate-changing emissions. With the introduction of Autonomous vehicles that use electricity, the emissions that pollute the environment will not be experienced and improve environmental quality. The Hybrid self-driving cars would use petroleum but in smaller amounts which will also reduce their ecological effects.
Disadvantages of AI in Self-driving car
Despite this technology capturing the technology and innovation sector, it has got some demerits: Self-driving cars without human drivers could frighten those experiencing it for the first time (Batista,2019). Hacking potentials can arise that could lead to interference with this project, loss of jobs since most people depend on driving activities as their source of livelihood, loss of driving skills among most people due to the automation of the technology; hence the skills may fade with time, the technology is not suited for all-weather for instance during winter, and rainy season it could pose a great challenge and finally a lot of resources is being used in the projects and will be required due to the need of new road layouts and infrastructure.
Future Research
The automotive industry is one of the most growing, especially in a digital sense. The growth has been marked by NVIDIA’s announcement in October 2017 of the world’s first AI computer to support fully driverless vehicles. The information observed the propagation of AI and resulted in an upsurge of seismic alterations to the last year’s automatic industry (Simons,2020). Researchers anticipate that 50 percent of total cars sold in 2030 with be AI driverless, which are fully autonomous. However, research on various aspects of the car has to be considered to achieve this.
Mimicking of human cognition by the car using AI
Fully driverless vehicles must have to study to cope with all the other factors. Processing data collection from numerous sources like LIDARs, GPS, ultrasonic sensors, and cameras remains the biggest challenge facing the manufacturing of fully autonomous cars. The industry’s revolving point can be to deliver car services with intuitive and cognitive abilities making sure that the new generation cars reach decisions and think like human drivers (Simons,2020). Efforts should be put in place by the manufacturers to ensure that autonomous vehicles react to abstruse conditions and consider all the possible scenarios that might influence driving. Since the aim seemed impracticable concerning location, time, and capitals, manufacturers began seeking more optimized ways of building solutions-creating an assembly convoy of cars that could learn from each other. In a fast-stride erudition situation, cars can attain the full equality of self-governance — drivers are expected able to put their eyes, minds, and hands-off on the road.
AI to Direct Uncharted Territories
To make a fully autonomous vehicle, builders still have a missing puzzle to discover. In May 2018, a significant leap was created by MIT researchers by presenting autonomous driverless cars that can navigate unmapped roads. It has a development system, MapLITE, that does not need 3D plots but depends entirely on modest GPS data collected with numerous sensors that detect the road environments. The system is beneficial, especially to communities living in rural areas.
AI to Substitute the Steering Wheels
Although many carmakers have begun their expedition to computers on wheels, few firms exist in the world at the front of making a fully autonomous experience. One of the top companies working toward this is Waymo. This Google subsidiary has since 2009 developed and today displays the uppermost level of autonomous amid the carriages handling the driving. An example of such a vehicle is their Waymo car. Though it has a steering wheel and pedals, the initial prototype presented in 2014 by google was free from one. In complying with street legal requirements, a control manual was fitted. However, google formers cars are not the only in the mentorship (Manoharan, 2019). In 2018 driverless safety report posed a zero-emanation driverless car level 4 known as Cruise A.V. It has the uppermost level of mechanization in acuity, control processes, and planning globally—this paves the way for the next-level five-car by the company in 2019.
Addition of Fuel to the Moral Dilemma by the AI
One of the complications revolving around the release of driverless autonomous cars on the street is the contentious condition that suggests life intimidating results to the individuals involved. The autonomy may result in unpredicted accidents by pushing the computers to make decisions (Simons,2020). Hence, the goal of creating a next-generation car is majorly reliant on the powerhouse of top-notch project management skills and AI-driven algorithms. The AI analyzes volumes of information from various sensors attached to the vehicle prompting decisions to run the car. It interprets the data into tangible steps supporting the next cohort of autonomous transport.
Conclusion
AI technology is used to power driverless cars by predicting the car’s surrounding environment for safe navigation. Technology has been on the rise in the recent past and promises to have a better future transport sector. The current autonomous vehicles have been an improvement of the previously discovered inventions like drones. This technology has got six levels of automation that range from level zero to five. The technology in driverless cars has impacted road safety, parking problems, and environmental pollutions. The major demerit of this technology is the loss of jobs and skills among many drivers. However, the technology is yet to be fully implemented to achieve fully autonomous cars, hence further research.
This study focuses on investigating the effects of AI on the accounting profession and providing possible solutions to its negative effects from the prisms of education and training. Four research questions guided the review. They aimed to find out how AI affects the performance of accounting professionals, investigate whether there have there been changes in employee attitudes toward AI, explore factors that could influence changes in the attitudes of accounting professionals towards AI and understand the attitudes of accounting professionals towards AI. Data was collected using interviews. The respondents were teachers and accounting professionals.
Their views suggested that there was a low level of awareness about AI in the accounting profession. They also provided a mixed review about the effects of Ai on the accounting sector. However, it was established that low-levels accounting jobs are most vulnerable to job losses.
Introduction
Background
Many companies around the world are redesigning their business processes to maximise their operational efficiencies (Kirschner & Stoyanov 2018). The accounting industry is one of the key tenets of corporate operations that are experiencing such changes (Cortese & Walton 2018). Their effects are heightened by the introduction of artificial intelligence (AI), which has been linked to high levels of productivity, accuracy and low operating costs (Fox 2018). Fox (2018) defines AI as the ability to help machines think, almost as a human being does. This type of technology equips machines or computers to make predictions about operational issues and adjust them to boost performance (Kirschner & Stoyanov 2018). Similarly, they can adapt to their environments the same way as human biology equips people to do the same (Carriço, 2018).
Professions that require the meticulous implementation of tasks are deemed the biggest beneficiaries of AI because it adds another layer of efficiency to their tasks (Birtchnell 2018). This outcome often occurs by extending a computer’s normal input and output processes to increase productivity (Felzmann, Villaronga, Lutz & Tamò-Larrieux 2019). Based on a general review of industry reports, AI is increasingly being used to conduct administrative duties and present accounting results in business through various structural changes (Fox 2018). Some common accounting tasks linked with AI include quarterly close procedures, procurement planning, developing accounts payables, answering audit queries and expense management (Ferri, Lusiani & Pareschi 2018).
Kirschner and Stoyanov (2018) support the above assertion by saying that most bookkeeping tasks, which are traditionally associated with accounting, are already being partially done through AI. For example, the technology is already being used to compute accounts payables and receivables (Geiger 2017). Relative to this discussion, observers have reported that some accounting firms have already integrated AI tasks to initiate payments and match purchase orders (Kirschner & Stoyanov 2018).
Some data entry procedures are also being undertaken using AI (Geiger 2017). The same outcome is true for data categorisation processes. For example, some global accounting firms, such as Deloitte, have adopted an advanced usage of AI to analyse the language used in contracts when reviewing client agreements (Birtchnell 2018). They have also relied on the same processes to analyse financial trends (Geiger 2017).
The healthcare field has also started using AI to improve industry outcomes and enhance efficiency (Pakdemirli 2019a; Liu, Keane & Denniston 2018). For example, some facilities use it to analyse healthcare claims for thousands of patients (Pakdemirli 2019b). In this regard, potential complexities have been observed during the initial stages of claim processing (Pakdemirli 2019b). The accounting sector has also used AI in the healthcare field by subjecting transaction approval processes, which are ordinarily done by human beings, to linked processes (Cortese & Walton 2018). Consequently, payroll, auditing and tax remittance procedures are increasingly being undertaken by AI-aided systems (Birtchnell 2018).
A broad review of these processes and their importance to accounting tasks shows that AI has the potential to oversee the implementation of complicated tasks, which have been traditionally a preserve of human intelligence (Bechmann & Bowker 2019; Mann 2017).
The trend towards the adoption of AI in accounting partly stems from increased work pressures that have forced most professionals to look for alternative resources for completing their project tasks (Rana 2018). Consequently, artificial intelligence (AI) has emerged as a technique that works much as human resources do in completing organisational tasks (Ferri et al. 2018). The only difference it has with other methods is that it is much more efficient and effective in completing accounting processes (Carriço 2018). It has also gained popularity among most organisations because it could help managers to complete tasks quicker than when they use conventional ways and without asking for a salary at the end of the process (Carriço 2018). This is why some managers prefer it to human labour (Rana 2018).
Stano, Kuruma and Damiano (2018) say that artificial intelligence significantly escalates the level of computing in the accounting field. Notably, it helps professionals to make appropriate changes in their decision-making processes that would be beneficial to the realisation of their professional or industry goals. In this regard, computers can adjust systemic processes through machine-based learning, as would be the case if human beings did the same job (Rana 2018).
The accounting profession could significantly benefit from this capability because it is characterised by the fulfilment of role tasks and the improvement of human capabilities (Rana 2018; Carriço 2018). Firms or companies that have developed a strong competitive advantage in the last decade based on this competence could attribute their success to the AI development platform.
The MIT-Boston Consulting Group is one organisation that supports the above-mentioned assertion because it recently reported that about 80% of professionals believe that AI contributes to the development of competitive advantages in the accounting field (Rana 2018). The same report suggested that about 70% of people associate improved productivity in organisational processes to technological development (Rana 2018). This idea has encouraged many accountants to increasingly relying on AI to assess large volumes of data – a process that was previously time-consuming when done by human beings (Stano et al. 2018).
Small and medium-sized organisations do not have the same resources that large organisations do to employ AI or develop associated products for their internal processes (Carriço 2018). However, Dahbi, Ezzine and Moussami (2017) say that AI will become more readily available and affordable to different firms in the future. Already, industry observers have demonstrated the positive impact of this technology on the marketing sector and predict that the accounting field would not be spared either (Carriço 2018). Indeed, AI technology has created significant cost reductions and improved productivity in multiple business sectors (Kirschner & Stoyanov 2018).
The same benefits are likely to be realised in the accounting field as well. This advantage is in addition to the gains made through improved accuracy and precision in the development of new products and the provision of quality services (Kirschner & Stoyanov 2018).
Although AI is set to revolutionise how industry processes are undertaken, generating all this information without the involvement of an accountant would be unwise because there is a place for human intervention in accounting processes, especially when making meaningful conclusions from the data generated (Dahlin 2019). In other words, even though AI could lead to job losses, there is a place for accountants in the analysis of data. However, their professional roles need to change from the traditional model, which is akin to the use of a calculator to make sense of data to a consultancy and advisory role that gives direction on what to do about the pieces of information obtained (Dahbi et al. 2017). This way, they would be advancing the profession and at the same time helping their clients to grow.
Artificial Intelligence poses several advantages and disadvantages to the accounting field. However, most professionals in this sector are ill-equipped to manage them (Cortese & Walton 2018). The need for training is consequently emphasised to equip them to not only respond well to this trend but also understand its potential impact on their careers (Rana 2018). For example, Levine (2019) posits that artificial intelligence could change a company’s structure, while Oleinik (2019) adds that it may have a significant impact on organisational culture as well.
Therefore, managers have to make changes to embrace some of these developments, as they need not oppose the trend but exploit it to realise their organisations’ objectives. This dissertation focuses on investigating the effects of AI on the accounting profession and providing possible solutions to its negative effects from the prisms of education and training. The purpose of the study is explained below.
Problem Statement
Although artificial intelligence is largely regarded as the frontier technology tool for the next generation of business progress, there is little understanding of its impact on different fields of business and accounting (Clarke, Chambers & Barry 2017). In this regard, many uncertainties are associated with artificial intelligence because, through a poor understanding of the concept, myths and misconceptions about its effects are widespread (Cortese & Walton 2018). For example, questions linger regarding the probability of artificial intelligence to solve all problems humans can address (Clarke et al. 2017). In these debates, issues relating to the limits of artificial intelligence are often discussed because the discussions revolve around understanding whether human and artificial intelligence are equal, or not (Cortese & Walton 2018).
Doubts also exist concerning the potential of artificial intelligence to replace human skills (Frude & Jandrić 2015). These issues stem from fears that some jobs or positions in the workplace would become obsolete because of the increased adoption of technology in the workplace (Clarke et al. 2017). The potential implications of such an outcome on employees and their families is even more commonly discussed because it is unclear how people, who could potentially lose their jobs through AI, will cater to the financial needs of their families when they lose their main sources of income (Clarke et al. 2017). This issue has affected professionals from different fields, including accounting. Indeed, from this concern, some companies have experienced opposition from some stakeholders or worker unions about the adoption of AI (Cortese & Walton 2018).
A deeper focus on the effects of AI on employee perception further highlights performance challenges that may occur when implementing AI. In other words, there are concerns that AI could influence employee performance by affecting their motivations to carry out simple tasks at work (Frude & Jandrić 2015). For example, they could sabotage work processes when they believe that AI is replacing them. Similarly, they would not create synergy between machines and human capital when they do not understand the roles of the machines in the workplace in the first place.
The adoption of AI in the accounting field has also been clouded by concerns from some quarters, about the potential for criminals to commandeer the technology and engage in unethical or immoral practices (Frude & Jandrić 2015). This issue stems from concerns about the potential to centrally controlled AI machines without human detection. The bigger question is whether it could be deduced that machines can behave ethically (as human beings do) or whether ethics should be disregarded in totality when using artificial intelligence. Frude and Jandrić (2015) delve further into this argument by questioning whether a machine can have a “mind” that is similar to a human being.
They also ask whether it could be conscious of the challenges or opportunities that exist in the real-world professional environment (Frude & Jandrić 2015). Questions regarding whether a machine can be treated with the same rights accorded to a human being also arise in such debates and it is still unclear how they can be solved in a world where people are still demanding for their rights in different social and political fields.
Purpose of the Study
There has been a lot of scepticism about AI in different fields of business management (Frude & Jandrić 2015). The same reservations have been noted in the accounting field because many professionals are still unclear about its effects on the practice and its relevance to the field (Dahlin 2019). Consequently, some researchers have developed negative attitudes towards it (Dahlin 2019). Nonetheless, the rate of development in artificial intelligence is increasing and it is worrying many professionals who believe that it would replace their jobs or make them irrelevant to the field (Dahlin 2019).
Although the development and integration of AI in the accounting field are still in their infancy stages, there is a need to properly understand the challenge and benefits of artificial intelligence in the accounting field, especially from an education and training point of view. The focus on training and education is pursued in this study because the integration of human and artificial intelligence depends on the effective understanding of AI through similar processes (Frude & Jandrić 2015). This need is supported by the fact that many accounting professionals are unfamiliar with AI and unaware of how it would impact their practice (Dahlin 2019).
Regardless of their lack of misinformation, the human element of accounting cannot be ignored even in the wake of increased developments in the AI field. Therefore, while developments in AI continue to improve through rapid technological changes, human intelligence needs also be improved through education and training. This is why this paper adopts a similar focus in understanding the challenges and benefits of artificial intelligence in the accounting field.
Aim
To investigate the effects of AI on the accounting profession and providing possible solutions to its negative effects from the prisms of education and training
Research Questions
How does AI affect the performance of accounting professionals?
Have there been any changes in employee attitudes toward AI in the past five years?
What factors could influence changes in the attitudes of accounting professionals towards AI?
How can the attitudes of accounting professionals towards AI be improved?
Overview of the Research
This study will be a qualitative investigation aimed at understanding the challenges and benefits of AI in the accounting field. The implications of the technology on the discipline will be based on its implications on education and training. The phenomenology research design will form the research design for the review because AI is considered a phenomenon in the business world (Fox 2018).
The research informants will be professionals who worked in accounting firms that have used AI. They were based in China and contacted by the researcher using the WeChat platform – a popular social media tool in China. Data will be analysed using the thematic and coding method, which is generally associated with the review of qualitative data. However, before delving into these discussions, it is important to conduct a review of past findings. The literature review section outlines the findings.
Literature Review
Introduction
This chapter provides an overview of what scholars have written about the impact of AI on accounting and businesses. Key sections of the chapter explain attitudes towards AI, its advantages, applications across different industries and the connection it shares with the accounting field. At the end of the chapter, the research gap, which necessitates this study, will be demonstrated. However, before delving into these details, it is essential to understand the theoretical underpinning of AI.
Theoretical Review
It is important to conduct a theoretical review of AI to understand the concepts and models that have underpinned its application. A broad review of current literature suggests that there is no specific of group of theories that directly explain AI integration (Ferri et al. 2018; Rana 2018; Carriço 2018). This gap in literature could be explained by the dynamism of the concept and its involvement with different aspects of business intelligence, including adaptive systems, intelligent distribution systems, data mining and knowledge discovery among other factors (Rana 2018; Carriço 2018).
The absence of theories explaining Ai integration has created a vacuum in analysis, which has been filled by the emergence of various mathematical models of application (Ferri et al. 2018). These models include numerical methods of learning, data assimilation techniques and language theories for analytical development, such as linear and logistic regression (Rana 2018; Carriço 2018). Collectively, they create value for organisations and industries. Although there is scanty evidence explaining AI integration, select studies show that two types of models are applicable in understanding the phenomenon: descriptive and normative (Frude & Jandrić 2015; Carriço 2018).
Descriptive models are often developed by observing the natural environment. The associated data is later used to make predictions about the future or guide people’s behaviours. Comparatively, normative models rely on the formulation of specific assumptions and theories to explain technological integration (Frude & Jandrić 2015; Carriço 2018). Therefore, accepting a model’s assumptions implies that their associated conclusions should also be tolerated. AI integration often assumes a dichotomous nature because it is both descriptive and normative. The models used to implement it are often designed to understand commonalities between how computers and the human brain work (Ferri et al. 2018). The basic premise of model development is that computers can effectively undertake the functions of employees but certain aspects of human function remain difficult to replicate.
The purpose of an AI model is to bridge the distance between human and machine capabilities. Based on this understanding, Kirschner and Stoyanov (2018) say that many AI models seek to identify similarities and differences between human and machine functions. One of the differences and similarities observed so far is that human intelligence occurs naturally but AI has to be built over a long period. This observation means that AI theories should be descriptive in their characterisation of human intelligence and, at the same time, be normative in their review of artificial intelligence (Frude & Jandrić 2015; Carriço 2018).
Stated differently, such theories should explain how the human mind works and outline how systems may be developed to replicate the same type of intelligence. Intelligence is the common concept underlying most of these models. Its application has been linked to several advantages associated with AI.
Advantages of AI
Several researchers have examined the potential benefits of AI to business processes (Fuatx 2019). Broadly, their findings explain how the technology influences specific aspects of business operations. According to figure 1 below, the main benefit of AI to companies is the enhancement of features and performance of products and services.
Figure 1. Primary Benefits of AI to Companies (Source: Fuatx 2019).
Multiple research studies have also shown that AI has helped many companies to make better decisions about their operations (Felzmann et al. 2019). Consequently, participating entities have improved the quality of their decision-making processes. A reduction in the number of workers (as a cost-reduction measure) and the optimisation of marketing processes through sales are the least documented benefits of AI (Fuatx 2019). However, some researchers have drawn comparisons between the effects of AI on business processes with the impact of the internet on corporate operations (Cortese & Walton 2018). They suggest that AI will have the same effects on business operations, or at least follow the same pattern of change, as the internet did in the 1990s.
There is little doubt among scholars that AI is already changing how people conduct businesses and who is running them (Fuatx 2019). From this assertion, there is a growing school of thought, which suggests that AI will first destroy conventional business principles before it is rebuilt again to accommodate new developments (Felzmann et al. 2019).
Here, the integration of AI into business processes should be conceived in the same manner as the growth of a plant because they both require nurturing because different business styles are assimilated into the broader technological platform (Felzmann et al. 2019). Therefore, it is difficult to conceive a situation where change occurs abruptly and an organisation absorbs the shocks without affecting core organisational processes. The transition has to occur slowly because seamless integration is likely to be realised only when all stakeholders consent to it and new pathways of integration are identified.
The realisation of improved connectivity will happen through increased data collection, enhanced training and improved engineering processes (Cortese & Walton 2018). Consistent monitoring also needs to occur at this stage to make sure that AI improves accuracy and refrains from becoming a destabilising force for an organisation (Cortese & Walton 2018). Some studies have shown that changes involving AI should be carried out through an explicit recognition of the challenges affecting a business and the prevailing metrics of success used to measure performance (Frude & Jandrić 2015).
However, running multiple AI models at the same time is the main problem associated with making sure this alignment is realised. Therefore, there is a need to make sure data, business and technical teams are working seamlessly together to guarantee success. According to figure 2 below, the transformation of AI processes has to occur by cementing four key pillars of excellence: business, data, technology and learning.
Figure 2. AI Transformation (Source: Fuatx 2019).
The business pillar of AI transformation requires the proper alignment of existing programs with the overall corporate strategy. Therefore, the AI technology to be adopted needs to help establish a business case. Stated differently, the use of AI should make business sense (Fuatx 2019). This process may involve a cost-benefit analysis but a success metric needs to be identified first.
Comparatively, the technological pillar of AI integration requires the creation of a consolidated database of analysis and on-demand cloud scaling. This process also demands the recognition of the concept, which should define the data acquisition strategy. This process demands that companies should continuously collect data that would be used to develop AI-based algorithms (Fuatx 2019). There should also be an established governance model that would define the data treatment process (Fuatx 2019).
According to figure 2 above, the learning component is the last pillar of AI transformation. This tenet of the transformation plan defines the focus of this study, which is training and education because it suggests that these two processes should keep professionals in the loop of AI transformation processes. It also encourages high levels of performance monitoring and retraining to equip personnel with the requisite skills for completing the transformation process (Fuatx 2019).
A different group of researchers suggest that the integration of AI into organisational processes requires the creation of a centre of excellence in corporate entities (Rana 2018; Carriço 2018). Furthermore, they suggest that there needs to be a new department formed to oversee the process and follow business-led decisions aimed at fulfilling the corporate strategy (Rana 2018; Carriço 2018). The centre of excellence is expected to consolidate and govern all organisational processes that require AI integration. Indeed, from a business standpoint, all AI functions need to be closely integrated with existing corporate objectives (Fuatx 2019). Based on this relationship, machine learning objectives are linked with data analytics and the development of measurable outcomes.
It is projected that new roles and positions, such as a Chief AI officer, will be formulated or developed to manage the above-mentioned functions (Fuatx 2019). There is also a high possibility that new positions (such as AI strategists and product managers) will be developed to ensure data collection supports the overall corporate mission. The creation of these new roles in organisational structures would make sure there is a proper definition of team success (Fuatx 2019). They would also make sure there is preferential treatment for projects that have the most impact but require the least amount of resources (Fuatx 2019).
Most AI-driven organisations recognise the above developments and depend on technology and data to drive their organisational processes (Kirschner & Stoyanov 2018). Concisely, new technologies are often needed to acquire and process data. This is because AI models demand a significant incorporation of new data to produce reliable solutions for business problems. Therefore, data creation is the most important part of AI development and many organisations are exploiting the opportunities created by the generation of free data from content users to enhance their systems (Fuatx 2019).
Creating this type of data also requires significant computing power to train and run models. However, many organisations will not invest heavily in the development of sophisticated analytical processes because they come with significant infrastructural requirements on organisations (Kirschner & Stoyanov 2018). Instead, they prefer to outsource the process through could-based computing services (Fuatx 2019).
Based on the above insights, data generation should not be conceived as a one-step process but rather a continuous activity that needs regular monitoring. Therefore, when organisations create new roles and positions in an organisation, it will be easier for them to indentify existing data and find a way of capturing it to improve AI systems. However, privacy and security policies should govern the process.
Broadly, several researchers take a positive view of AI by saying that its benefits cut across different industries and functions (Cortese & Walton 2018; Ferri et al. 2018). However, most of them suggest that its early stages of adoption and implementation were confined in consumer application systems (Felzmann et al. 2019). Over time, the trend has changed and businesses are increasingly adopting it in different aspects of their operations with striking results (McKinsey & Company 2019). Their findings have shown that AI can improve business applications in different areas of operation, including predictive maintenance and the detection of anomalies in factor line assemblies (McKinsey & Company 2019). These advantages are ordinarily observed when high volumes of high-dimensional data are used in AI (McKinsey & Company 2019).
The aviation sector provides an example of an industry that has effectively integrated AI applications in its operations (Kaartemo & Helkkula 2018). For example, aircraft manufacturers have used the system to improve the detection of engine problems (McKinsey & Company 2019).
In some cases, technology has been used to autocorrect operational functions (Fenwick & Edwards 2016). The transportation sector has also had a similarly successful application of AI technologies in delivery route maintenance, optimisation of service orders and improvements in fuel efficiency (McKinsey & Company 2019). The service industry is also another group of businesses that have effectively integrated AI in the internal business processes and are reaping its benefits through improved customer service and sales (through improved integration of customers’ demographic details with service-centred processes to provide individualised services) (McKinsey & Company 2019).
The increased productivity and efficiency generated from AI mostly come from improvements in traditional analytical techniques (Kaartemo & Helkkula 2018). Statistics also support this finding because they show that AI improved productivity and efficiency in about 60% of the times used (McKinsey & Company 2019). Figure 1 below shows that AI can improve the incremental value of products and services offered in different industries by varying percentages.
Figure 1. Potential incremental value from AI over other analytical techniques (Source: McKinsey & Company 2019)
According to figure 1 above, the travel industry stands to benefit the most from AI services. The transport and logistics industry has also shown potential in improving process outcomes using AI. However, the fields of advanced electronics and aerospace engineering have benefited the least from AI. Nonetheless, the technology remains one of the best catalysts for improving business process outcomes.
Researchers have pointed out varying scopes and extents of AI adoption in different countries (Cortese & Walton 2018). The pace of adoption and the extent of AI integration have particularly defined this variation across multiple sectors (Kaartemo & Helkkula 2018). Several studies suggest that most companies have adopted AI in at least one of their business processes (Cortese & Walton 2018; Kaartemo & Helkkula 2018; McKinsey & Company 2019).
About a third of companies are also considering using AI in their internal systems but their efforts are still confined to pilot phases of review. The adoption of AI across multiple business processes is estimated at 20% but the highest percentage of integration has been observed in giant multinational firms, which are projected to adopt AI in at least 97% of their business processes (McKinsey & Company 2019).
Current research suggests that companies or firms that adopt AI tend to think of the technology as having vast effects on their mid-term and long-term goals (Liu et al. 2018). The potential to expand market share is the main motivation for using AI among most of these businesses sampled but slow adopters are more focused on cost reduction (McKinsey & Company 2019). Researchers have also noted that corporations or enterprises that have devolved their functions on the digital platform are the best adopters of AI (Lazzini et al. 2018). They are also considered the most probable companies to get the most value from the technology (Dudhwala & Larsen 2019).
An analysis of AI adoption across different sectors shows a widening gap between companies that base their operations on virtual production platforms and those that do not (McKinsey & Company 2019). For example, technology companies and financial entities are the leading adopters of AI. The gap between early and late adopters of AI in this field is deemed impactful on the competitive advantage of a business because early adopters gain a lot of experience with AI and use the same capability to leverage their operations, thereby making it difficult for the competitors to “catch up” (McKinsey & Company 2019). Figure 2 below shows that different industries and sectors of the global economy have varying adoption levels of AI.
Figure 2. Future AI demand trajectory (Source: McKinsey & Company 2019).
As mentioned above, the financial and technology sectors are the leading adopters of AI. The rate of technological adoption in the tourism industry, the transport sector and healthcare are also high compared to other fields, such as construction, education and consumer packaging businesses (McKinsey & Company 2019). These varying levels of adoption are not only linked to the nature of businesses in these sectors but also the attitudes of professionals in the industries (Frude & Jandrić 2015).
For example, the information technology (IT) sector is often comprised of professionals who are versant with current developments in AI, thereby allowing them to understand the technology better than employees from another sector, like the healthcare industry, will. Therefore, IT professionals are naturally going to have a stronger inclination towards the adoption of AI compared to their counterparts in the healthcare sector. Alternatively, it could be assumed that professionals in the technology industry have a more positive attitude towards AI compared to their healthcare counterparts. Nonetheless, several challenges to the adoption of AI persists and the burden of implementation has been left mostly to business leaders and managers who are expected to be committed to its adoption.
Attitudes towards Artificial Intelligence
The role of accountants in presenting the true financial performance of a company cannot be overemphasised in a fast-paced world where multiple variables affect corporate performance. This group of professionals not only maintain various systems of recording but also verify data contained in financial books of accounts. In this regard, the purpose of an accountant, in business management, is to present a monetary snapshot of a business or company’s financial health. By relying on professional insight, they could also predict what financial outcomes could occur in the short-term or near-term (Frude & Jandrić 2015).
Indeed, by understanding a company’s financial performance, it is easy to comprehend its strengths and weaknesses and their influence on corporate success. The introduction of AI threatens to change this traditional view of accounting by isolating key processes from human control.
Many researchers have pointed out that developments in AI could influence different aspects of professionals and personal lives (Cortese & Walton 2018). Evidence has been given of its potential effects on the labour market, transport industry, healthcare, education and other fields (McKinsey & Company 2019). Although pundits and policymakers have started to discuss the ramifications of AI, the opinions of professional groups have yet to be understood.
Particularly, the views of accounting professionals towards AI need to be framed within the context of public opinions towards the same phenomenon. For example, Zhang and Dafoe (2019) say that most people (44%) in America express support for AI, while only a small percentage (about 22%) have expressed strong reservations about it (McKinsey & Company 2019).
Evidence suggests that demographic differences could influence people’s support for AI (McKinsey & Company 2019). For example, people with high levels of education express more support for the practice compared to those who have lower qualifications (McKinsey & Company 2019). To explain this position, Zhang and Dafoe (2019) noted that college graduates expressed higher support (51%) for AI compared to those who had a high-school education (29%). Income disparities within households have also been linked with the support for AI because there was higher support for the concept (59%) among people who had an income of more than $100,000 annually compared to those who earned less than $30,000 annually (33%) (Zhang & Dafoe 2019).
People’s professions have also been associated with support or disapproval for AI because employees who hail from a technology background, such as computer programmers, have expressed higher support for AI (58%) compared to those who do not come from such a background (McKinsey & Company 2019). If this assertion were to be applied to the context of this study, it could be assumed that accountants would have lower support for AI compared to computer programmers or software developers.
Gender differences have also been associated with the support for AI because researchers have noted that men express a higher approval rating for AI (44%) compared to their female counterparts, who only have a 35% support for AI (McKinsey & Company 2019). Broadly, although demographic differences have been used to explain support or disapproval of AI, many researchers suggest that a majority of people believe that AI needs to be managed (Zhang & Dafoe 2019). The same view is also held by researchers who have explored people’s views of the use of robots to carry out human functions (Kaartemo & Helkkula 2018).
How Accountants are Using AI
To understand the future of AI in the accounting field, it is important to review how professionals in the same discipline are using AI today. Stated differently, it is vital to understand how AI is being used to solve accounting and business problems relating to today’s globalised environment. However, to gain a proper understanding of this role, it is pivotal to note that many accountants are commonly motivated to use their skills and expertise to help business managers and stakeholders make better decisions (Persson, Radcliffe & Stein 2018).
To do so, they rely on high-quality financial and non-financial instruments of accounting to make their technical analyses (Napitupulu 2018). This role is reflected in their work through several tasks, responsibilities and areas of specialisation that strive to enhance the quality of data available for review (Kaartemo & Helkkula 2018).
Technology has been used to improve the above-mentioned decision-making systems and has been deployed to solve three main types of problems: providing high quality and cheaper data for better decision-making, generating new insights from data analysis processes and freeing up the time to solve other pressing business challenges (Fenwick & Edwards 2016). Indeed, the nature of machine learning itself is predicated on the ability to improve accounting roles and equipping professionals with new skills and competencies to undertake their tasks (Sterne & Razlogova 2019).
Therefore, it is essential to identify accounting problems that could easily be solved using machine learning and those that may not benefit from the same process (or require human intelligence). This approach to problem-solving will make sure that changes are primarily driven by the fulfilment of business objectives and not technological requirements.
Studies suggest that there is limited use of AI in the accounting field but some common areas of operation where implementation has been relatively well-received are in the use of machine language to code accounting entries and improve the accuracy of rule-based approaches to accounting (Nijam & Jahfer 2018). AI has also been partially adopted in fraud detection and the prediction of possible areas of fraud activities (Napitupulu 2018). Machine-based predictive models of learning have also been developed using AI. Moreover, deep learning models for analysing unstructured data have also been developed from machine-based learning (Nijam & Jahfer 2018).
Practical Challenges Associated with the Adoption of AI
Although studies have shown the potential of AI in improving professional practice (Bechmann & Bowker 2019), many challenges still stand in the way of its full implementation. One of them that relates to training is the need for immense human effort in labelling data. This problem is challenging because supervised learning requires the proper labelling of data (Clarke et al. 2017). Furthermore, many organisations today have a problem of developing large amounts of data that are sufficient for training (Bechmann & Bowker 2019).
The complexity of machine learning is also problematic in data analysis and implementation because it makes it difficult to understand decision-making processes that lead to the formulation of potential solutions (Napitupulu 2018). This challenge is notably increased by the use of multiple variables in decision-making but it is impactful to the accounting field because the relationship between clients and professionals is often underpinned by trust. Since trust is a human attribute, it becomes difficult to account for it in the AI-aided decision-making process. Nonetheless, current research on AI is designed to address this problem by increasing transparency in model development (Sterne & Razlogova 2019).
Another challenge associated with the implementation of AI is the difficulty in generalising applications (Birtchnell 2018). The problem arises from the challenge of transferring experience from an AI model to another one. Stated differently, machine learning is often programmed to suit specific business contexts and it becomes increasingly difficult to exchange applications across different learning groups spread across various corporate sectors (Birtchnell 2018). This problem creates a governance issue.
Different researchers have highlighted the use of AI in the accounting field because human resource management has been the norm for many organisations in the sector (Lazzini, Iacoviello & Ferraris 2018). However, when there is a threat to the traditional role of human resources in the field, issues of governance emerge because it is difficult to ascertain how robots or machines can be managed, devoid of human control. Consequently, issues of governance have emerged as a key area of research in the integration of AI in the accounting space. More importantly, professionals and observers alike have raised concerns regarding how to prevent AI-aided technology from contravening existing liberties and privacy concerns in the business sector (Lazzini et al. 2018).
Similarly, the use of AI in propagating false information or misinformation through virtual platforms have also been explored in the same way as the potential threat of using AI to carry out cyber attacks on governments, companies and institutions have been investigated (Bechmann & Bowker 2019). Therefore, the main issue emerging in the investigation of governance issues relating to AI are commonly reviewed through a need to protect the privacy of data held by people and organisations.
Importance of Education and Training in Artificial Intelligence
Different scholars have highlighted the importance of education in accounting (Nijam & Jahfer 2018). Their goal is usually to equip professionals with skills and competencies relating to major developments in the sector (Stevenson, Power, Ferguson & Collison 2018). This goal is necessitated by the fact that the accounting field is linked with multiple changes in the operating environment, including technology, law and ethics (Nijam & Jahfer 2018).
AI falls within the context of technological development and education has been emphasised as a way of sensitising professionals about its implications on the sector. The current emphasis on research has been on how to use some of the skills and competencies offered by AI in the accounting field and how professionals or managers could harness them to solve existing problems, including employee retention and other human resource issues.
The importance of training is not only confined to improving practice but also reinvigorating traditional accounting practices by making them better and more efficient. Rapid changes in globalisation and the need to standardise accounting standards and procedures have further emphasised the importance of effective training in the accounting field (Gammie, Allison & Matson 2018). AI-related education processes are even more central to the growth of the discipline, relative to the impact that they have on the field.
However, some researchers have noted that some organisations are hesitant to plan for education seminars because of the perceived lack of adequate teachers or personnel to oversee the process (Gammie et al. 2018). Moreover, some managers and CEOs still deem some of the developments made in AI to be insufficient in necessitating radical changes in an organisation (McKinsey & Company 2019). This development is informed by the fact that there are still many major developments going on in AI that may deem some of the information currently available obsolete.
Summary
Anecdotal evidence gathered in this paper show that most of the accounting journals and works of literature that have investigated the link between AI and accounting do not sufficiently emphasise the role of education and training. In other words, the existing evidence is summarily explained without a keen understanding of the role of education and training in explaining what the concept is about and its influence on accounting. This study aims to fill this research gap. The techniques used by the researcher in fulfilling this goal are explained in chapter three below.
Methodology
Introduction (Project Summary)
This chapter will highlight the strategies adopted to answer the research questions. To recap, this study is designed to investigate the effects of AI on the accounting profession from the prisms of education and training. The study was guided by five questions, which focused on evaluating whether there have been changes in employee attitudes toward AI in the past five years, examining factors that could influence the attitudes of accounting professionals toward AI and investigating how the attitudes of accounting professionals towards AI could be improved. The approach taken by the researcher in answering the above-mentioned questions is discussed below.
Research Approach
The qualitative and quantitative research approaches are the main techniques used in academic research (Steen, DeFillippi, Sydow, Pryke & Michelfelder 2018). The latter approach is often used in investigations that measure quantifiable variables, while the qualitative technique is applicable when measuring subjective variables (Archibald, Radil, Zhang & Hanson 2015). The researcher used the qualitative research method in this study because it focused on the attitudes of accounting professionals regarding AI.
Attitude is a subjective variable because it is individualistic. In other words, two people may have completely different experiences when subjected to the same stimuli (AI). Similarly, they may have different perspectives on the technological phenomenon. These qualities of the research variable made it difficult to use the quantitative technique to undertake this analysis because it was not possible to measure the variables numerically.
The justification for using the qualitative research approach is rooted in the fact that it provided the researcher with an opportunity to get in-depth details regarding the questions asked (Allana & Clark 2018). In other words, it allowed the researcher to go beyond the superficial elements of the investigation and probe the respondents’ reasons for making specific statements. This advantage is highlighted by several research studies, which have investigated the merits of the qualitative research approach (Walker and Baxter 2019; de Block and Vis 2018; Wagner, Kawulich & Garner 2019).
Research Design
According to UMSL (2019), there are five main research designs associated with the qualitative research approach. They include phenomenology, grounded theory, ethnography, historical methods, and case studies (UMSL 2019). The characteristics of each of these designs are provided below.
Grounded Theory
The purpose of the grounded theory design is to develop a theory (Chun Tie, Birks & Francis 2019). Typically, many researchers use it to assess social problems and examine how people address them (Ralph, Birks & Chapman 2014). Before findings are developed, scholars often use prepositions to explain specific research phenomena after formulating, testing and redeveloping them (Collins & Stockton 2018). These characteristics of the grounded approach made it inappropriate for use in the study because its focus was not on theory development but rather on understanding the impact that AI would have in the accounting field. The second research design considered for review was the case study approach and it is discussed below.
Case Studies
Researchers often use the case study research design to gain an in-depth understanding of the workings of a specific social group or institution (Ebneyamini & Sadeghi 2018; Rule & John 2015). The main method of data collection is observation because scholars often immerse themselves in a group’s social structure and observe patterns of behaviour (Rashid, Rashid, Warraich, Sabir & Waseem 2019; Herdlein & Zurner 2015). Although these characteristics of analysis make this research design useful in understanding the inner workings of a professional group, it did not apply to this study because its focus was not on a specific social group or institution but a wide profession – accounting.
Ethnography
Researchers who want to describe the characteristics of a cultural group have typically used the ethnography approach to undertake their studies (Lubet 2019; Jerolmack & Khan 2017). They do so by gaining access to culture and gathering data by immersing themselves in it to observe behaviour (Rashid, Caine & Goez 2015; Newmahr & Hannem 2018). The focus on the cultural aspects of scientific dogma made it difficult to use this research design in the investigation. Instead, the study was focused on examining the effects of AI on accounting professionals. The next approach considered for review was the historical technique. Its characteristics are described below.
Historical Methods
The use of historical methods in qualitative research approaches is informed by the need to examine past events to make sense of current findings or anticipate future outcomes (Stutz & Sachs 2018; Chong 2014). This methodological approach is systematic in the manner research variables are assessed and by reconciling conflicting evidence (Crossen-White 2015; Kelly 2019). This dissertation did not follow a systemic approach to data analysis because the questions posed to the respondents were semi-structured. Furthermore, the focus of the study was not on the use of historical records to make sense of the research phenomenon. Consequently, this research design was not selected for use in the study. However, the phenomenology research approach was reviewed and its findings highlighted below.
Phenomenology
The main goal of the phenomenology research design is to describe people’s lived experiences (van Manen 2017). As its name insinuates, this research design is often associated with the examination of different phenomena that affect societies or professions (Ignatow 2018). From this analysis, the uniqueness of people’s lived experiences is observed because the main premise of analysis is understanding that people have different perceptions of reality.
According to Strandmark (2015) and Bovin (2019), scholars who use this research design are often preoccupied with the need to understand what people’s lived experiences mean for a research phenomenon under investigation. These characteristics of the research design made it appropriate for use in the study because AI was the phenomenon under investigation and the researcher intended to understand its impact on accounting professionals – a process, which is akin to understanding their lived experiences.
Data Collection
Data was collected using semi-structured interviews. The questions posed to the respondents were professionally developed because the informants were accounting professionals in different fields of business. Interviews were selected because they allowed the researcher to explore the research questions in-depth compared to alternative data collection methods, such as surveys, which only require a “yes” or “no” response. The interviews were conducted online on the WeChat platform. The researcher selectively used this technique because of the logistical difficulties of conducting the interviews face-to-face because the respondents were in China.
Research Participants
The target population was comprised of five professionals who have advanced experience with the use of artificial intelligence in the accounting profession. They were based in China and worked as teachers or accountants. The respondents were made up of two accounting teachers from a Chinese education institution and three professionals who worked as accountants in a Beijing-based financial institution. The accountants held junior, semi-senior and senior-level positions in the workplace.
The exact positions held in the organisation were that of a chief financial officer (CFO), technology manager, chief executive officer (CEO) and a human resource (HR) manager. Since all the informants were Chinese, the researcher spoke to them in the same language. However, for purposes of data analysis, findings were transcribed into the English language and the final report presented in the same manner. The interview questions posed to them are provided in appendix 1.
Sampling Procedure
The researcher used the purposive sampling method to contact the respondents. This non-probability sampling method is selective in the manner respondents are chosen because there is a bias to only recruit informants who are knowledgeable about the study area (Ignatow 2018). In other words, the selection of the informants depends on the interviewer’s judgement. The researcher employed the purposive sampling method because some of the respondents were known through family members. Therefore, it was easy to contact them and schedule an interview. This technique was appropriately used for this study because it is commonly applied when a small sample is desired (Brayda & Boyce 2014).
The small number of participants was chosen for the study based on the recommendations of Brayda and Boyce (2014), which suggest that a sample of fewer than 12 participants is appropriate for a comprehensive qualitative study. This figure is targeted because a high number of participants could make it difficult for the researcher to establish a good relationship with all of them. This concern is raised because qualitative investigations require the researcher to develop a good rapport with the respondents.
Data Analysis
The researcher used the thematic and coding method to analyse data. Armborst (2017), Aveling, Gillespie and Cornish (2015) say that many researchers have successfully used the technique to analyse qualitative data. Today, it is among the leading models of information analysis for the qualitative research method (Armborst 2017). Its proven reliability made it attractive for this study (Elo, Kääriäinen, Kanste, Pölkki, Utriainen & Kyngäs 2014; Ando, Cousins & Young 2014; Nowell, Norris, White & Moules 2017).
Consequently, important themes were identified from the interview responses provided by the informants. The process was done by recognising patterns of assertions that would help to answer the research questions. The identified patterns were later correlated with different research nodes, as suggested by Hilton and Azzam (2019). Developing the nodes later paved the way for the coding process and the development of the research findings. Overall, the researcher undertook the thematic and coding method by following the steps highlighted in figure 3 below.
Figure 3. Thematic and coding steps (Source: Developed by Author).
Ethical Considerations
It is important to review the ethical considerations of this paper because human subjects were used. According to Colnerud (2014), Saunders, Kitzinger and Kitzinger (2015), the use of human subjects emphasises the need to conduct a study ethically to protect the rights of the respondents and the researcher. The main ethical issues the characterised the study are as follows:
Anonymity and Confidentiality
Sugiura, Wiles and Pope (2017) say that anonymity and confidentiality in qualitative research are closely associated with the respect for human dignity and fidelity to the research objectives. Relative to this assertion, the views of the respondents were presented in the study anonymously. In other words, their identities will not be revealed unless expressly stated by the informant in writing. The aim of doing so is to protect them from any consequence that may arise through a publication of the research findings (Lowman & Palys 2014). As suggested by Hannes and Parylo (2014), doing so will make sure that the views presented in the investigation are solely aimed at answering the research questions and not to probe an informant’s personal life. This ethical provision also allowed the participants to give or withhold as much information as they wanted (Scarth 2016).
Voluntary Participation
Brewer (2016), Dixon and Quirke (2018) say that informants should voluntarily be allowed to participate in research studies. Relative to this provision, all the respondents who took part in the study did so voluntarily. Stated differently, they were not coerced or paid to give their views. Furthermore, they were informed of their rights, details of the study and its purpose before participating in the investigation.
The purpose was to equip them with all relevant information that would help them to make an informed choice about their involvement in the research. Before volunteering to participate in it, they had to sign an informed consent form. Lastly, the informants were also informed of their right to withdraw from the study without any repercussions. Therefore, they did not feel like they were indirectly forced to participate in it.
Reliability and Validity of Data
It was important to understand the reliability and validity of the interview data because researchers note the difficulty in replicating these types of investigations (Doll 2018; Jackson, Kay & Frank 2015). Stated differently, the flexible nature of qualitative inquiry makes it difficult to replicate the findings, thereby making it easy to question the reliability of information generated. To address these issues, the researcher used the member-check technique to improve the reliability and validity of information obtained from the respondents. This method works by relaying the study’s findings back to the respondents so that they assess whether they represent their actual views, or not (Oranye 2016).
Therefore, the researcher furnished the informants with a statement of the final findings before they were included in the edited report. The respondents had an opportunity to state whether the information presented represented their views, or not and adjustments were made appropriately. Broadly, the member-check technique is an appropriate tool for improving the reliability of qualitative findings. Its merits have been highlighted by several researchers, including Jackson, Kay and Frank (2015).
Findings
Introduction
This chapter outlines the results obtained from implementing the strategies outlined in chapter three above. Key sections of the analysis explain the themes and codes developed from a review of the data pertaining to the respondents’ views regarding different areas of AI implementation. However, before delving into these areas of analysis, it is important to review the demographic characteristics of the informants.
Demographic Findings
As highlighted in chapter three of this dissertation, the researcher interviewed five respondents who practiced as teachers (2) or accountants (3). The accountants held junior, semi-senior and senior-level positions in the workplace. The exact positions held in the organisation were CFO, technology manager and a HR manager. The CFO gave valuable insights regarding the effects of AI on computing the financial performance of a company because this position is associated with long-term financial planning, management of company finances, risk management and record-keeping (among other tasks). These responsibilities are closely aligned with accounting tasks because the profession is related to the development of financial statements to present the true performance of a company.
The above-mentioned tasks and responsibilities mostly relate to a CFO’s duties. Therefore, the inputs provided by this respondent were insightful in understanding how AI affects current accounting and financial processes of an organisation. The views provided by the technology manager were similarly insightful because they provided explanations regarding the effects of AI on IT-related tasks. Therefore, discussions relating to technological integration and the hardware components of AI were comprehensively discussed in the interview with the technology manager.
The views of the HR manager were similarly vital to the analysis because the effects of AI on human resource were a key part of the discussions. Therefore, integrating the views of the HR and technology managers in this report provided a balanced understanding of the study questions because AI is a technological tool, while human intelligence is a HR topic.
Broadly, the respondents held the above-mentioned positions for more than five years and had not vacated them from the time of company inception. This lack of mobility could largely be attributed to the fact that they were all working for small businesses, which did not have a high staff turnover. The long-term nature of their employment statuses meant that they observed the effects of AI on business performance much more keenly than an employee who had worked for shorter periods. Lastly, the integration of the view of teachers and practicing professionals in the interviews was to gain a dual understanding of the effects of AI from professional and educational perspectives. Therefore, the discussions presented in this chapter are holistic.
As highlighted in the methodology chapter of this dissertation, the thematic and coding method was used as the main data analysis tool. The researcher used this technique to review hours of interview data. Seven themes were generated as outlined in table 1 below.
Table 1. Themes and Codes Generated (Source: Developed by Author).
Themes
Codes
Impact of AI on job losses
1
How AI is changing accounting processes
2
How artificial intelligence will affect the industry
3
AI and the professional context
4
AI and education
5
Effects of AI on organisational structure
6
Skills needed in AI
7
The first theme identified in the discussion was the impact of AI on job losses. The findings appear below.
Impact of AI on Job Losses
One of the biggest fears associated with the implementation of AI in the accounting field is the possible loss of jobs because conventional literature suggests that increased integration of AI machines in organisational tasks would render some tasks obsolete (Miller 2019). The respondents were asked to comment on this matter and they presented mixed views. The accounting professionals suggested that job losses are inevitable because, unlike a human being, a machine can do multiple tasks at the same.
The teachers disagreed with this view and said that new areas of competence requiring human intervention will emerge through the increased adoption of AI functions. However, they admitted that some job losses are inevitable because of the increased efficiency of machines, subject to human potential. Accounting areas that require an interpretation of large amounts of data were identified to be the most vulnerable areas of job losses because they were most likely to be influenced by machines.
However, many organisations have already automated most of these tasks (Miller 2019). Therefore, it is unlikely that significant job losses would emerge from this area of competence. However, human intelligence will be relied on in corporate decision-making areas and in the development of operational strategies in an unpredictable business environment.
Therefore, all information that were to be captured by AI, but are unusable, had to be disregarded. Thus, there was a strong need to check the information relayed in these machines to improve relevance. Human expertise is required in this task because it makes sure the “big picture” is observed and respected. The path to the realisation of the “big picture” can be redefined or changed but the respondents insinuated that the goal had to remain constant.
The integration of human and artificial intelligence was also proposed as a viable strategy to prevent job losses because it is easy to create solutions through this development. The place of human intelligence in the decision-making structure is still safeguarded because new developments have to be made to improve organisational processes. It is easy for such a situation to happen because AI works by following a set of algorithms.
These algorithms are often repeated until an update is required. The respondents referred to this review as a “key human function.” Stated differently, people will be needed to constantly re-evaluate AI functions and make sure they are still working towards achieving their intended goals. More importantly, employees are required to examine the system from multiple angles and develop solutions that do not necessarily have to align to specific or existing patterns of development. To support this view, one of the respondents said,
You have to look at it like a blend of interpersonal and intrapersonal influences. A combination of both forces would help the system to adapt accordingly. Therefore, when AI is introduced into an existing system, it does not only create new processes but also develops new areas of development that require adaptation. In other words, human beings have to adapt and not the machines. By orienting themselves to the new order of things, the professionals would later find that novel areas of job development could emerge. For example, look at market research… the demand for new knowledge is extremely high. It is almost like a niche market. Human intelligence is required on this front.
The above assertion shows that the respondents favoured a segmented labour structure where job losses would be reviewed based on their importance to organisational functions. Low-paying jobs were deemed to be most affected because machine learning could replace simple tasks. However, higher-order jobs emerged as having the least probability of replacement because their complicated nature required human sophistication. Therefore, the worry about job losses was confined to lower ranking jobs.
One of the respondents affirmed the above assertion from a data sourcing perspective. He said that there is insufficient data to use in AI-aided processes to make effective and rational decisions regarding complex tasks. This assertion stems from current data collection problems linked with AI because there is an insufficient supply of quality and reliable data to use in these machines. Furthermore, some areas of application are insufficiently explored. Therefore, there is a similarly inadequate source of data coming from these quarters. In such cases, expert opinions are highly valued, thereby securing the place for human intelligence in AI processes.
How AI is Changing Accounting Processes
The review about the effects of AI on accounting processes comes from the conflicting views regarding whether it has positive or negative effects on the accounting profession. The literature review findings suggested that the effects are largely positive but the experts sampled presented a mixed review of its impact on the accounting sector. One of the respondents said that understanding the effects of AI on accounting processes requires an examination of how some giant multinational firms are using the technology. He said,
Look at how EY and Deloitte are using AI in their daily accounting processes. Their document review strategies are being updated using AI everyday. Initially, these organisations had to rely on extensive and protracted labour-intensive processes to undertake the same tasks and to transfer ownership of client assets. This has fundamentally changed. Just based on these examples alone, I would say that AI is doing the job that most accountants would do when examining convoluted contracts. I should add that this is just “scratching the surface” (laughs).
One of the respondents also said that stakeholders should consider AI a tool for improving the efficiency of undertaking accounting procedures and refrain from looking at it as a negative tool of development, which is designed to take away their jobs. Relative to this assertion, one of the respondents said the best way to encourage accounting firms to appreciate AI is to make them look for areas of redundancy or bureaucracy that could be solved using the technology. By doing so, they would be looking at AI as a solutions provider, as opposed to a problematic “trend” in accounting.
Another respondent said that professionals should stop opposing AI because it is a “lost war.” Instead, he proposed that they should adapt to the new times and re-evaluate how it would enhance their roles. When asked to clarify how such an evaluation should occur, the informant said that accounting professionals should consider looking at their positions as that of an “adviser” and not a “doer.” In other words, since machines can carry out conventional accounting processes with higher accuracy and efficiency than a human being, their accounting inputs should be confined to advisory.
Furthermore, few employers would choose an employee over a machine because people require a salary at the end of the month and a machine does not have such demands. Consequently, the respondents considered comparing AI to human beings as a fruitless exercise because human beings cannot match the efficiency of a machine. Furthermore, employees come with their challenges, such as the need to keep them motivated or happy in the organisation, while machines do not have this problem. Therefore, the respondent said, from a suitability perspective, accounting professionals need to rethink their role in the field.
How Artificial Intelligence will affect the Industry
One of the respondents suggested a different view of the effects of AI on the accounting field by saying that it has the potential to create new jobs, as opposed to “killing” them. He believed that AI would create new positions for accountants, especially in the areas of training and testing because a lot of work is needed in developing workable AI tools and integrating them into accounting processes. This exercise will not only be dependent on machine learning but also human intelligence. He also suggested that there would be new job opportunities created in auditing algorithms because of developments in AI intelligent systems.
However, he cautioned that this progress will only happen if accountants take part in projects that involve AI integration because it is the only way they would be able to come up with solutions to familiar challenges. He also said that integrating results into business processes requires the involvement of accountants in linked projects. Other professionals are expected to be more involved in the direct handling of inputs and outputs. For example, exception handling and data preparation may be associated with these tasks.
The respondents also said that the evolution of the accounting practice, in the wake of changes that are stemming from AI, would have to be reflected in their skills and competencies. For example, they needed to have sufficient knowledge in machine learning to understand AI processes. The respondent also said that critical thinking should emerge as one of the most coveted skills accountants should have. In addition, they recommended that communication skills will also become increasingly important in the future.
AI and the Professional Context
When asked whether AI could replace human jobs in the accounting sector, one of the respondents said there is a need to review this issue within an institutional context because accounting is a profession exercised within this limit. Notably, he drew attention to the role played by regulators and standard setters in the accounting profession because they have veto power to define acceptable and unacceptable accounting practices, regardless of whether they involve AI or not. Therefore, if they are not comfortable with the risks of AI, they could easily stifle its advancement in the profession. To support this assertion one of the informants said,
For you to understand the importance of institutional change in the accounting profession, it is pertinent to look at auditing… for example. This is a highly regulated sector of the accounting field and it would be difficult to make any significant change without effective reforms in the legal environment. For example, I believe it is standard procedure for regulators to ask auditors where they get their evidence. If it comes from AI, they would ask different types of questions. More importantly, if they do not understand how AI helps to generate data, they would completely “shoot it down.”
The same respondent noted that even when regulators understand how AI should be integrated in the profession, issues of reliability might emerge. Therefore, they need to overcome this hurdle as well.
Regulation emerged as a significant issue of concern involving the integration of AI in the accounting profession. However, when the researcher asked the respondents about the same issue, they did not only have a bleak outlook of AI because regulators in the sector are already discussing the regulatory limitations of AI adoption. Therefore, they argued that it is possible for the limiting framework of AI to change in the coming years as stakeholders in the sector continue to understand its implications on the industry.
One of the informants also said that he has been a part of similar discussions in China because considerations about machine learning already form part of their discussions. He also said that many institutions in China are having similar discussions and some have even gone ahead to implement AI, as a competitive advantage over their rivals, without much consultations from stakeholders.
One of the respondents said that the skills and training agenda for accountants in the context of AI use has been a subject of debate for many accountancy-linked professional bodies as well as educators and peers in the field. Current discussions propose the need for more training, especially in areas of technology and data management. One of the respondents also mentioned the need for improving soft skills in the areas of analysis, thinking and adaptability. The need for life-long learning was also mentioned as a tool for making sure professionals always remain abreast with developments in the field. To support this statement, one of the respondents said,
What we need to realise is that there are competing visions for the future of AI, especially for highly qualified professional bodies and lower-cadre employees. Particularly, the use of technology to access expertise knowledge influences most of the decisions and perceptions of the future.
Another respondent said that change and adaptability to AI in the accounting profession will only be realised when there is a consensus among most stakeholders on how to overcome this challenge. Particularly, it will be easier to embrace change when they agree on one direction to follow concerning the adoption of AI.
When asked to explain their views regarding the ability of machine learning to replace human intelligence, one of the respondents said that such an outcome is unlikely to occur because of the difficulty in replicating human intelligence. In this regard, he said that accountants should not be fearful of losing their work because human intelligence also evolves. To support this assertion, he said..
I do not believe people should be worried because we cannot be threatened by a system that we created in the first place. I do not conceive a situation where human beings would be helpless to machines, especially when they are believed to cause harm. All you have to do is read a few books about human resilience or just look around to know that we have survived many adversities and AI is not going to be the one that stops us on our track.
Based on the above assertion, the respondent said the best way to secure the future of accounting is to make sure that human beings and machines work together.
AI and Education
One of the main issues discussed with the respondents was the correlation between effective AI use and higher education. This topic of discussion was necessitated by the sophistication of AI technologies and the possible need to develop new skills to be able to operate or integrate them into everyday practice. There was a consensus among the informants that some type of higher education is needed to operate AI-aided machines. In fact, they alluded to the creation of advanced specialised companies that would be servicing these machines because their sophistication would not allow untrained personnel to understand what to do. To support this assertion, one of the interviewees said,
I believe it will be more like how we purchase and use software. The software creators will essentially be the developers of the technology and they will sell it to accounting firms who will then rely on their expertise to integrate the technology onto their existing platforms. I do not think much of the maintenance and operational activities of AI will be left to the accountants… perhaps only the learning and orientation parts. That may need some level of education.
The above assertion suggested that the respondents did not think higher education was of critical importance to the operationalisation of AI technologies in accounting. However, when the respondents were asked to explain the kind of changes that should occur in the accounting sector within the nest two, five or ten years, they suggested that the mindset of stakeholders need to change before any meaningful reform can be realised. They believed that the greatest hurdle to the implementation of AI in the sector was not because of hardware of resource limitations but people’s resistance and ill-conceived attitudes about the technology.
However, one of the informants differed with his colleagues when he argued that the curriculum needs to pave the way for the realisation of significant changes in the practice. He believed that the software changes mentioned should happen in the classroom setting, as professionals need to be equipped with the requisite knowledge needed to comprehend the benefits of AI. Therefore, he believed that curriculums need to be updated to include more educational materials about the impact of technology on the practice. However, this respondent was cognisant of the limitations of such a development by recognising that technology keeps changing and it will not be sufficient to equip the students about one aspect of IT, such as AI.
Therefore, he believed that technology should be reviewed in its totality and incrementally be added to existing curriculum at a basic level of learning. The respondent also said that current accounting certifications need to be reviewed and updated to include courses on AI. The idea stemmed from the belief that it was impossible to be an effective accountant in today’s business world without having a proper understanding of AI. Stated differently, it was inconceivable to think of a certified accountant who had little understanding of AI.
Besides the analysis on education, the informants also said that in the short-term, educational institutions, professional bodies and accountants should reallocate their resources to support AI integration. This area of management is relatively underfunded because few managers understand the importance of AI in the field. However, with a change in attitude, emphasis can be redirected to researching the best ways to integrate AI into accounting processes.
In the long-term, the researchers proposed that organisations should develop separate departments for managing AI-related matters. They also said that the creation of AI posts should be done because this is a specific area of application that requires critical attention. However, they recognised that this proposal might affect the governance structure of the existing financial system because it is designed to rely on brick-and-mortar processes or outdated accounting practices.
However, digitising financial systems could pave the way for the integration of AI-aided accounting processes, thereby eliminating the need for physical visits or audits of financial accounts. Furthermore, integrating AI processes into the financial system would enhance transparency and increase investor confidence because people often trust machines better than they do human beings. One of the respondents added that if these changes are adopted, they would affect the recruitment and professional training systems of accountants.
Effects of AI on Organisational Structure
The researcher sought to find out the respondents’ views about the effects of AI on organisational structures. This issue emerged from a review of studies, which suggested that AI could influence how businesses are organised because of its ability to render certain positions obsolete (Cortese & Walton 2018; Kaartemo & Helkkula 2018; McKinsey & Company 2019). The ability of AI to create new positions in an organisation also informed this discussion because there was a need to find out whether this change would affect the structure of the organisation.
Most of the respondents said that AI would have a significant impact on organisational structure by creating new models of data analysis based on its potential to create different ways of doing business.
This predicted change was compared with the reforms that the internet brought to the business environment in the 1990s. For example, the position of information manager was non-existent in the pre-internet era. Therefore, the adoption of IT tools in the contemporary society necessitated the creation of this position. This type of change is expected to influence the accounting sector when AI becomes widely adopted. Two of the respondents also said that organisations have consistently reorganised their departments to cope with such changes. From the process, new internal organisations and roles have been created, as different tasks require a new set of skills and competencies. The sum total of the projected changes is expected to significantly affect organisational structures.
Skills Needed in AI
One of the issues discussed in this study was the nature of skills required to operate AI-aided machines. This topic of discussion emerged from concerns about job losses that could occur because of the widespread use of AI. The respondents suggested that professionals should acquire computer literacy skills as a key part of their training plans. This view was informed by the belief that most AI technologies that will be developed in the future will be computer-based. Three of the respondents also said it was vital for professionals to acquire administrative and leadership skills because these are key tenets of future accountancy practices when machines replace most low-level jobs. To support this view, one of them said,
Leadership is a human attribute that will not be easily replaced by machines. Therefore, the acquisition of such skills would improve an employee’s job security by equipping them to take new positions.
At a technical level, the researchers said professionals who acquire mathematical and algorithm skills will have an added advantage over those who do not get the same skills. These competencies could be complemented with the acquisition of problem-solving and analytical skills because most AI processes are geared towards such tasks. Therefore, the respondents believed that gaining a deeper insight into this area of analysis would enhance the skills that a professional would need to navigate current changes in the field.
One of the respondents disagreed with the above view by arguing that most of the skills required in AI development are not related to accounting but other supporting sciences, such as computer science. Therefore, he believed that a distinction should be recognised between the skills an accountant would need in AI development and the competencies underlying AI change in the first place. The competencies required to develop AI is a prerogative of software developers, computer sciences and other professionals in the IT field. However, future accounting tasks should be focused on human tasks.
The informants also recognised that professionals who have acquired specialised knowledge in AI would be considered for high-level positions in the industry. This is because AI is an “added advantage” in accounting because it offers a new tool for completing human tasks.
Summary
The findings generated from this study show that the successful implementation of AI in the accounting practice requires both dedication and commitment to the process. There is also a need to acquire commensurate skills of operation to effectively manage any change that would occur from its implementation. The biggest challenge that many organisations face is abandoning long-held traditions and procedures in accounting (Miranti 2014).
Therefore, it is difficult to effect the change process. Particularly, this is true for many organisations because many accountants were not trained to have AI skills. Therefore, they have to participate in organisational processes and seminars that would update them on developments in the field. The findings highlighted above presented a holistic picture of the study issue because professionals and educators alike provided the insights documented.
Although the views outlined were insightful, they portrayed a mixed review of the effects of AI on the accounting practice. In other words, it was difficult to develop an absolute understanding of the effects AI on accounting. Based on this outcome, it was necessary to compare the findings with what other researchers have said about the study topic. The discussions of the investigation appear below.
Discussions
Introduction
This chapter is a critical review of the findings highlighted in chapter four above. Key sections show how AI can be integrated in the accounting field, challenges associated with the process and the future of the technology on the practice. However, a broader review of its impact on accounting is provided below.
Impact of AI on Accounting
Some of the first AI tools developed were reliant on expert advice, which was later built into existing systems, thereby enabling them to make decisions or develop recommendations in the manner a human being would (Gammie et al. 2018). Although this technique produced viable results, the quality of output was not the same as human intelligence (Gammie et al. 2018). Therefore, despite the technicalities involved in making such models, they could not withstand the realities of the business environment. Particularly, the system was ineffective in making decisions, which had to rely on ambiguous knowledge.
Based on the findings highlighted in this paper, the place of AI in the accounting sector is secured because evidence shows that it is increasingly influencing decision-making processes (Kirschner & Stoyanov 2018). Although accountants have always used technology to undertake their business processes, AI offers an opportunity to improve the quality of decisions made. In this regard, it should not be seen as an invasive element in the profession because its purpose is to improve the quality of output.
Therefore, to achieve this goal, AI should be conceived as a tool for making prudent accounting decisions. Consequently, it would be unwise to obstruct its use because of fear and cynicism, which are mostly unsubstantiated. Furthermore, few researchers have explored its impact in the accounting sector. Possibly, existing fears are motivated by wrongful applications of the tool in other industries. However, stakeholders may not understand that other industries have a different set of variables affecting AI implementation.
The findings of this study suggest the existence of mixed views regarding the adoption of AI in accounting. However, the examination of contracts in the accounting field using AI is one area of interest that emerged in the study to show the extent that the technology has been integrated into the discipline. This issue was mentioned by the respondents and highlighted in excerpts of studies done by Amnuai (2019), Naujokaitiene, Tereseviciene and Zydziunaite (2015). Both sets of informants notably mentioned how giant multinational, accounting firms, such as Deloitte, have used AI to examine contracts and client agreements.
It is important to understand that this task was mainly a preserve of the professionals because it is a qualitative process, subject to the ability of human beings to understand varied implications of contractual agreements. However, as demonstrated by the informants sampled, AI tools have already been equipped with the capability to analyse such contracts, thereby carrying out tasks that would otherwise be deemed impossible for a machine to do.
Accounting firms that are unfamiliar with AI need to rethink how they undertake their organisational processes because they will lose the competitive advantage they should have over other industry players who are yet to acquire any experience by using it. However, integrating AI into existing organisational processes is difficult because of the need to make several adjustments concerning organisational processes (Dudhwala & Larsen 2019). As Murphy and Quinn (2018) point out, these adjustments may involve fulfilling their roles as trusted professionals and even devising new ways of meeting client expectations.
The accuracy of predicting the impact of AI on accounting largely depends on people’s ability to understand differences between human and machine intelligence and how the two work together to improve business outcomes. So far, several researchers have explored this issue and pointed out that human intelligence could broadly be classified into two categories: intuition and reasoning (Lazzini et al. 2018; Kaartemo & Helkkula 2018). People’s subconscious thinking often influences the intuition part. Decisions made within this context may be quickly processed or involve little effort from concerned parties because of the use of well-established thought patterns. This type of thinking may require support from professionals to recognise patterns of analysis and make intuitive decisions based on history.
Logic and reasoning also affects human intelligence because people often rely on existing knowledge to make informed decisions. Accountants are typically trained to use intuition and logic because of their reliance on knowledge and training to make informed decisions about various issues. For example, most of them make intuitive decisions based on their experiences in the field.
Although people use intuitive thinking on different aspects of their daily lives, it is susceptible to bias. AI has the potential to address this concern because it is detached from human emotion. For example, Kirschner and Stoyanov (2018) point out that it can address availability and confirmation biases because of their high accuracy. Current approaches to AI will continue to improve on human competencies but they come from a long line of logic-based rules and decision-trees that may be difficult to change (Lazzini et al. 2018). However, AI will continue to try to replicate human intelligence and thinking patterns.
Integration of AI with Other Technologies
This study has demonstrated the advantages of AI and its potential in improving the efficiency of operations in the accounting field. However, what many respondents sampled in this dissertation failed to account for was the multifaceted nature of AI and how different technologies could be plugged into the platform to improve accounting process. For example, developments in the technology sector could be easily interfaced with accounting processes (using AI) because the latter outlines a broader group of machine learning technologies and not necessarily a single application. Therefore, the kind of applications that could be developed and interfaced on the AI platform is unlimited.
Block chain and quantum computing are only a few technological platforms that easily meet the criteria of AI integration in today’s accounting practice (Kirschner & Stoyanov 2018). As accountants ponder on how to integrate AI into their existing tasks, it is equally important to recognise that there is a high pace of technological development taking place around and the world and it could significantly impact the capability or capacity of accounting firms to embrace AI. Consequently, learning-based and data-driven systems of technological development should be developed further to make sure that the pace of development and integration continues to be at par with current trends in industry development.
It is essential to be clear about the unique characteristics of AI and its potential to solve real-time problems when integrating it in accounting processes. Kirschner and Stoyanov (2018) say that the process may be lengthy because of the long time taken to complete technological development and implementation processes. Here, AI seeks to address specific problems in the accounting profession by improving how professionals use existing tools in data analysis. However, more discussions and engagement among technology and accounting experts need to be fostered to come up with the right applications of AI. These discussions should help to redefine how the technology helps to solve fundamental business problems, particularly using it as the baseline for making changes.
Change can only be realised if professionals choose to be open-minded, as opposed to merely opposing AI for the sake of maintaining status quo. Therefore, as AI makes greater improvements in data analysis, the ability of human intelligence to make accounting decisions should also be significantly enhanced. Therefore, professional bodies regulating the discipline need to be adaptable to change because their standards of operation have to match market demands as environmental factors evolve. The same is also true for accounting firms that have lasted for many years because they have had to change to current market conditions to remain relevant in business. AI integration requires the same level of commitment.
Lastly, the uniqueness of AI, relative to other technologies, is its tendency to adopt a bottom-up approach where past mistakes are used to improve existing models. Traditional data analytical approaches typically relied on the top-down management structure where superiors were the main source of organisational intelligence. Although there are different types of fields associated with AI, machine learning (through pattern recognition) is the main driver for the change and hype caused by AI (Kaartemo & Helkkula 2018). By combining the need for pairing human and machine learning, computers could be effectively used to improve human thinking and enhance intelligence.
Training and Education
The views presented in this study suggest that AI would have a mixed impact on the accounting field. This ambiguity may be stemming from low levels of awareness among accounting professionals regarding the effects of AI. Consequently, the need for education and training should be emphasised because they are the best strategies to use in creating more awareness about the effects of AI. However, both techniques should not be conceived as a homogenous concept that applies to all groups of accountants because there is a distinction in the kind of assistance that existing and new professionals should acquire. Notably, education should be directed to new and younger professionals because their training curriculum should equip them with necessary skills associated with AI.
Comparatively, training exercises should target existing professionals because their training did not include AI. Therefore, they should attend workshops and seminars on AI to update their knowledge on the practice. Doing so would help to address the weaknesses of employees that are linked to poor AI adoption. Training would enable them to strengthen the skills required to implement AI, thereby minimising some of the weak links associated with its implementation.
In addition, it would improve collaboration and team effort in the workplace because attending seminars means that more employees would become knowledgeable about AI and they will be able to transfer the same knowledge to others. In this regard, there would be an overall improvement of skills and knowledge on AI, which could later be harnessed to improve organisational performance.
Lastly, training and education would improve employee performance by enabling them to acquire knowledge associated with AI. Doing so would enhance their confidence in working with the technology because they would no longer see it as a threat to their jobs but rather a tool of analysis. This strategy would also improve the importance of the professionals in the accounting field by giving them access to new and cutting-edge technology in the industry.
Future of AI
The effects of AI on the accounting profession were a significant topic of analysis in this study because the literature review findings pointed it out as a key concern among observers. Although the literature review findings suggested that the effects of AI are largely positive, the respondents sampled in this review presented mixed reviews of the issues. In other words, an almost equal number of people thought that job losses would occur compared to those who thought such an outcome would not suffice. However, of importance to this study was the emergence of a biased view of the findings because the respondents alluded to massive low-level job losses.
Therefore, people who worked simple tasks are the most vulnerable to retrenchment compared to their peers who hold high-ranking positions. Consequently, it could be assumed that job losses are unlikely to occur from supervisory to senior-level positions because this group of employees carry out complicated tasks, which machines cannot do. However, jobs that require a direct analysis of data or simple commands can be easily replaced.
The above findings suggest that AI may complicate or simplify organisational structures depending on how managers reconfigure their work processes. For example, a simplified organisational structure may emerge when lower ranking jobs are minimised or eliminated and higher-level positions maintained. Since fewer positions exist at the top, it is possible for managers to coordinate organisational functioning using a leaner organisational structure. However, firms that may resist change and want to maintain their original organisational structures may experience conflict because people tasks may conflict with machine functions.
Several researchers have explored the impact of AI in other industries and found that it will not be significantly as disruptive to organisational processes as much as automation did from the 1950s to the 1980s (Frude & Jandrić 2015). The fear of job losses mostly come from reports which predict a “doomsday scenario” when AI is implemented. For example, an article by Fuatx (2019) suggested that there would be massive unemployment in the US because up to 46% of jobs in the country are at risk of being eliminated by AI. These reports have fanned fears but they fail to take into account the contextual factors that influence job outcomes.
For example, the nature and characteristics of the industry involved is a significant determinant of the effects of AI because some sectors are more vulnerable to automation than others are. For example, the manufacturing sector is susceptible to job losses because of AI. Comparatively, service-oriented businesses, such as accounting, may not have such type of exposure.
Broadly, the views of the respondents highlighted in this study suggest that the use of algorithms to undertake accounting functions cuts into a space that has never been explored before. This approach means that there is going to be an unprecedented application of logic in accounting process. However, human intelligence is not often dependent on logic because its main function is to safeguard a company’s vision. Logic and vision are two key parts of organisational processes that need to be nurtured to realise the overall long-term vision of the company. Notably, the two need to be implemented concurrently to complete organisational tasks.
This is because the use of one technique could be problematic for organisations that intend to use both qualitative and quantitative aspects of process reviews. Therefore, it is unlikely that one of the functions would be substituted for the other. Consequently, people should not be concerned about job losses in accounting, which may arise from the implementation of AI.
The uniqueness of the accounting sector as a wing of investment decision-making should guide future discussions on AI because its potential advantages could significantly improve current performance. The increased efficiency and accuracy of AI output reported in multiple studies supports this assertion because decisions made using artificial intelligence networks are better in terms of timeliness and accuracy (Bechmann & Bowker 2019). Future applications of AI should focus on educating and training stakeholders about its advantages to eliminate the stigma associated with it. Doing so should prepare professionals to work alongside machines.
It is difficult to predict a future where the accounting profession will be immune from the influences of AI technology because it is an inherently analytical discipline and machines are good at that. Indeed AI could be a powerful data analytics tool. Therefore, it can be naturally integrated with the accounting profession. The potential increase in the quality of output that could be reported from its use could only be imagined for now because few accounting firms admit to using AI and even fewer can comprehensively understand how it can influence their operational plans.
The evidence gathered from the literature review and expert opinions suggest that multinational accounting firms, such as Deloitte, were early adopters of the technology and are using it to undertake complicated tasks, such as analysing client agreements (Rana 2018; Carriço 2018). This analogy means that medium and smaller accounting firms are yet to realise the benefits of AI because they have not acquired the experience of working with it.
Capital and infrastructural requirements of AI could be impeding efforts to use the technology extensively across different subsectors of the accounting practice but this problem could be solved in the future through increased investments and awareness of the benefits of AI. Technological advancements in the AI sector could also fan this development by making expertise cheaper to obtain in the same way as computers have become accessible to the average person. Overall, concerns about the future of AI need to be disregarded because they are based on the lack of a proper understanding of its potential implications on practice.
Summary
This chapter shows that the findings of this paper align with the views of other researchers who have investigated the impact of AI on different aspects of business performance in the sense that they both present a mixed review of its impact on accounting. This problem is exacerbated by the minimal levels of awareness about the technology. However, education and training could help to change this situation.
Conclusion and Recommendations
Conclusion
From the onset of this study, the possible effects of AI on corporate performance was established as a significant area of business analysis, relative to its potential to impact human resource performance in the short, medium and long-term. The focus on the accounting field was necessitated by rapid developments in the industry, particularly in the areas of data analytics, which favour the use of machine learning as opposed to human intelligence. The review was skewed towards investigating the research phenomenon from a training and education perspective because most of the challenges associated with AI integration stemmed from a poor awareness of the technology in the industry.
Furthermore, observations were made to suggest that current professionals lack the requisite skills to thrive in a post-AI world. Key to these discussions was the long-term viability of human labour in the accounting profession. This topic of discussion underscored the development of the first research question, which sought to find out how developments in AI would influence performance among accounting professionals.
The respondents sampled in this study presented mixed reviews about this study topic but their general sentiments suggested the possibility of massive job losses at the lower end of the work continuum. They did not conceive a situation where such job losses could occur in higher-level positions because they were more attuned to human intelligence through different relationship-based competencies, such as leadership and communication. Therefore, there is a potential mixed impact of AI on the performance of accounting professionals.
A key part of the findings suggests that there have been insignificant changes in employee attitude towards AI, which have occurred in the past five years. In other words, most accountants are deemed to have negative or neutral attitudes towards AI because of their lack of experience with the technology. This problem could be addressed through increased seminars to educate professionals about the technology. By doing so, there would be a greater awareness of the technology, which would allow meaningful discussions to take place thereafter.
However, since few steps have been taken to realise this outcome, the integration of AI in the accounting sector may take a long time. The process should ideally involve meaningful discussions among stakeholders about its potential ramifications on the practice but awareness needs to be created first. This is why the focus of this paper was on training and education.
This area of analysis was analysed because of its potential to influence accounting professionals to change their attitudes about the technology. There seems to be a larger and growing trend among stakeholders to achieve this goal because many employers are organising workshops that would equip their employees to learn about current changes in the industry. Furthermore, professional accounting bodies, such as the Association of Chartered Certified Accountants (ACCA), are already organising workshops where such issues are being discussed. Consequently, accountants should have a strong focus on purpose if they are to effectively exploit the advantages of AI.
Stated differently, people’s understanding of AI needs to shift from a methodological review to a goal-oriented perspective. Doing so would change people’s understanding of AI as a threat to a tool for achieving organisational outcomes or meeting accounting objectives. This transition will be in alignment with the nature of accounting because the profession is not an end unto itself; instead, it is a tool for helping stakeholders to make informed financial decisions about a company.
The integration of AI into accounting processes should help stakeholders to underpin their investment, growth and business decisions. The development of more intelligent systems will enable stakeholders to make fundamentally different decisions regarding their corporate plans and in turn expand the effectiveness of accounting. AI has the potential to significantly improve the quality of decisions made by investors because it increases their confidence and trust in the financial decisions made by companies, which are properly audited.
For example, tax compliance, resource allocation and expenditure control are some areas of corporate governance that could benefit from this progress. It is important to make these changes because they support the realisation of organisational objectives. Accountancy is an important tool for making sure the process happens smoothly.
The use of AI can help to transform new problems using new data by creating intelligent systems of analysis. However, it is important to recognise that a lot of work needs to be done to make this vision a reality. At the very least, there should be an effective criteria for allocating resources, which are vital in making informed choices. AI-aided technologies would help to promote accountability when making these decisions. Furthermore, it is important to understand that the focus should always be on identifying important decisions that could be influenced by the use of AI and position them to effectively exploit the advantages of the technique.
The above insights highlight the importance of exploiting the advantages of new technology. This study highlights the strengths of machine learning in the accounting profession not only because they are expanding new areas of research but also because they are sufficiently addressing some of the perennial problems noted in accounting. However, technology is complex and its integration in the accounting profession may be problematic, subject to privacy concerns and the general mistrust that some professionals have towards it.
The findings of this study will be instrumental in building a positive image of the future, especially regarding the integration of AI and human intelligence. The goal is to minimise the scepticism associated with AI and focus more on the probability or potential of the technology to solve perennial accounting problems and enhance existing professional skills to address current challenges in the field.
Overall, based on the findings generated from this study, it is important to acknowledge the difficulty in correctly predicting the effects of AI on the accounting field because many factors have to be considered in evaluation. Furthermore, the field of analysis is too broad to understand all the variables that have to be evaluated to come up with correct predictions. Therefore, the long-term trajectory of the accounting field will ultimately shape how professionals choose to interact with machines. Furthermore, a myriad of social, economic and political factors will have to be evaluated to come up with a correct prediction of how this relationship will be formed. The kind of AI technology that will be available in future and people’s understanding of it will also change in coming years, thereby highlighting the need to be adaptable to change.
Recommendations
Overall, to comprehend the challenges and potential ramifications of AI in the accounting field, it is essential to understand that AI equips machines with the ability to think. In other words, AI centres on the development of intelligent machines. While there are reports and criticisms regarding the role of this concept in improving or ending human existence, this paper focused on a scientific perspective of its integration with human intelligence in the accounting field. The findings suggested that there are low levels of awareness of AI and a low capacity to adopt new changes. These issues could be addressed by implementing the following recommendations.
Compulsory Training
Based on the insights provided in this paper; there is a strong need for compulsory training of accounting professionals about AI. The training and education requirements need to be made compulsory through a decree or legislative amendment because professionals have to be compelled to be versant with the technology. Indeed, it would be difficult to exploit its benefits if accountants are ignorant about its implications and yet it will not only change the sector but also others related to it. For example, the need for compulsory training is supported by the double-faceted nature of the accounting field because it is often pegged with auditing. Training and education would help to master both conceptual and practical aspects of AI and its relationship with the discipline.
Compulsory training of accounting professionals means that they need to gain access to the right skills that would help them to exploit existing opportunities for development. The starting point should be in the acquisition of technical skills linked to machine learning. However, similar to other skills and competencies implemented in business contexts, accountants also need to understand the context of these skills and their potential to align with today’s contemporary globalised business environment. The need to acquire these skills and competencies rests in the understanding that accounting roles are already changing and adapting to the capabilities that exist in the data analysis sector.
Accountants should exploit some of the advantages associated with using advanced data analytical techniques because their conventional training processes already include an understanding of numerical data, which could be easily combined with their awareness of business dynamics to create a perfect blend of strategy that would improve the business’s goals, vis-a-vis existing accounting strategies.
Relative to the above assertion, Birtchnell (2018) says that some training procedures will need to emphasise technical accounting expertise, but others need to lay emphasis on the need for human intelligence to solve complex issues. Nonetheless, organisations should make it a point to increase the scope of collaboration between accountants and other professionals as well as business departments. This effort will improve synchrony in task assimilation across multiple levels of organisational control. The AI trend is supposed to accelerate this process because it has the potential to integrate different aspects of a company’s operational tasks to create a bigger and more coherent operational plan. In this regard, different professionals in an organisation (not only accountants) will derive the right meanings from the existing data and models.
Generally, accountants need to develop new skills and competencies that are aligned to AI development if they are to maximise the potential that this technology holds. For example, dedicating most of their time to predictive and proactive learning would help them to stay ahead of the curve in terms of leveraging AI potential in their careers. Here, they could put predictions in context or develop capabilities that would enable them to change their course quickly because AI technologies support such a future in business. Broadly, new ways of thinking are needed to make this happen.
Improve Capacity for AI Absorption
Some of the respondents interviewed in this study claimed that AI needs to be reviewed within the institutional setting because professionals and policy makers regulate rules and procedures of accounting. In this regard, there is a need for institutions involved in regulating accounting practices to adapt to these new technologies, otherwise it would be difficult to reap their benefits, as most professionals would be bound by traditional accounting regulations and policies that do not support integration.
However, regulation is often slow to recognise innovation and correctly adapt to its potential. Therefore, there could be many missed opportunities for AI integration in the accounting profession because of fixed regulations and rules. Consequently, there needs to be an expanded capacity for regulators to understand the potential positive ramifications of AI on the accounting profession for its advantages to be truly understood or conceptualised.
The need for better integration between human and machine intelligence is necessitated by the fact that the sum of both is better than individual applications. Research studies conducted in the areas of chess and medicine affirm this view (Cortese & Walton 2018; Ferri et al. 2018). Nonetheless, it is equally vital to understand that AI developments are still ongoing and they may move to complicated areas of decision-making, which may lead to job losses.
However, it is unlikely that specific human capabilities, such as leadership and creativity will be substituted using technology. Furthermore, it is unwise to present a “dooms day” scenario concerning the adoption of AI in the accounting field because human beings are extremely resilient and adaptable to new situations. Therefore, it is unlikely that a permanent crisis will be created because of the adoption of AI in the accounting field. However, decisions involving human judgement may be significantly changed through the adoption of AI because the latter has access to more superior data, thereby making it difficult for accountants to compete with it. Therefore attempts at suppressing the use of Ai in accounting, simply to protect the viability of traditional accounting processes, may fail because AI may supersede the need for human judgement in a majority of the cases.
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What role do you hold in your organisation? What does it involve?
Have you been doing this job since you graduated? What was your previous work experience like? (the reason for changing; effects of AI on different periods, how to adapt to the changing business landscape)
With the development of artificial intelligence, how do you think it will affect the industry based on your professional field or working environment? Prompt for challenge and benefits.
Do you think this will affect recruitment processes in your organization?
As we know, many jobs have been replaced by artificial intelligence robots. What skills do you think employees need to have to keep their jobs even as companies embrace AI? How do you think these skills should be acquired or nurtured?
Do you think artificial intelligence could replace professional personnel to some extent? What kind of work can be finished by AI and what cannot be accomplished?
Will development in AI require higher education?
Will developments in AI change the organization structure?
What kind of changes or revolutions should be made in the next 2, 5 and 10 years?
The advent of artificial intelligence (AI) became a rather important milestone in the history of technological innovations because of the increasingly high number of valuable interventions. The majority of use cases are related to business and manufacturing, making it much easier for organizations to make sure that their improvements are beneficial to business [1]. Three areas are examined: personalized customer service, unprecedented automation opportunities, and prediction capabilities.
When discussing personalized customer services, it may be safe to say that the implementation of AI earnestly appealed to the customers. The latter could easily find any product they needed with the hints from the system that guided users through all the available resources to let them purchase everything they needed plus something else [2][3]. Customer behavior has changed significantly since the implementation of AI mechanisms because consumers are willingly spending their resources on products they like (even if they do not need them now).
On the other hand, there is the concept of automation that makes it easier for businesses to computerize operations that were previously completed by human workers. For example, automated warehousing software prevents the local stores from running out of stock and ensures that consumers are going to receive personalized offers based on their previous purchases [4]. This is a perfect opportunity for businesses to provide their customer base with the required products and services promptly.
The last concept that will be discussed within the framework of the current paper is prediction capability. With an opportunity to envisage consumer behavior and foresee potential purchasing patterns, businesses gain a competitive advantage [5][6]. The latter quickly pays for the installation and setup costs of complex IT systems expected to calculate customer behavior.
Ultimately, modern business operations are practically impossible to run decently without a well-thought-out combination of AI, big data, and IT experts that know how to install and maintain even the most intricate software and hardware. From the current essay, it becomes clear that AI plays a huge role in how existing businesses work with their customers and process all the required data. Without AI, there would be almost no automated business processes, exposing businesses to human error and numerous other limitations.
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Anyone has used Apple products at one time in their lives. One of the most well-liked personal assistants that Apple offers on the iPhone and iPad is Siri. Even if one does not own an Apple product, they already use Cortana, Watson, or Alexa. Artificial intelligence (AI) refers to computers that have been taught to think like people and carry out activities that typically require human intellect, such as visual perception, speech recognition, language translation, and decision-making. At the moment, artificial intelligence (AI) spans a broad range of subfields, from general learning and perception to the specialized, including proving mathematical theorems, playing chess, driving a car on a crowded street, detecting illnesses, and writing poetry. Any intellectual job may benefit from AI; this makes it a genuinely global area. There are many applications of artificial intelligence in daily life. AI is promising because it offers changes in work, our personal life, and the economy.
Brief History
The origin of artificial intelligence (AI) may be traced back to ancient myths, tales, and legends of artificial creatures that master artisans gave intellect or consciousness. Philosophers’ attempts to characterize human thought as the mechanical manipulation of symbols laid the groundwork for current artificial intelligence. The programmable digital computer, a device built on the abstract core of mathematical reasoning, was created due to this work in the 1940s. A few scientists were motivated to start seriously debating the viability of creating an electronic brain with this gadget and the concepts that went into making it. The first attempts to characterize human thought as a symbolic system by classical philosophers are where contemporary artificial intelligence (AI) started. The phrase “artificial intelligence” was first used in a conference at Dartmouth College in Hanover, New Hampshire, in 1956, although the discipline of AI was not fully established until then.
Current Applications
Artificial intelligence aims to help people do activities that might otherwise be too challenging and time-consuming. As a result, AI applies to all intellectual tasks, and neural network development has been advanced using contemporary approaches. Well-known uses of AI include autonomous vehicles, like self-driving cars, bomb-detecting robots in combat scenarios, drones, interplanetary travel, game creation, modeling natural events, flight prediction, medical diagnosis, search engines, judicial judgment choices, event simulation, mathematics, online assistants, targeted marketing, and journalism. Scientists can now create consumer profiles automatically by using artificial intelligence to generalize human behavior from digital traces. As a source of news, social media websites are displacing radio and television. Consequently, news organizations increasingly depend on social media sites for content creation, and large publishers use technology to spread the news more efficiently, leading to high website traffic.
Projects
Over time, technology has changed how farming is done and has affected the agriculture sector. In many nations worldwide, agriculture is the primary source of income. As the world’s population rises from 7.5 billion to 9.7 billion people, according to UN estimates, there will be increased strain on the earth’s resources because only 4% more land will be cultivated by 2050 (UN/DESA Policy Brief #102: Population, Food Security, Nutrition and Sustainable Development | Department of Economic and Social Affairs, n.d.). Farmers will thus need to work harder with fewer resources. The same report estimates that to feed an incremental two billion people, food output must rise by 60% (UN/DESA Policy Brief #102: Population, Food Security, Nutrition and Sustainable Development | Department of Economic and Social Affairs, n.d.). Traditional approaches, nevertheless, are unable to meet this enormous demand. The adoption of AI technology by farmers, for instance, using AI for intelligent spraying chemicals, has resulted in cost savings. AI-based robots in harvesting have eliminated the labor challenge as well. Additionally, using AI for predictive analytics has helped make appropriate decisions like time to sow.
Ethics and the Future
Although most people will benefit from the advent of artificial intelligence, according to experts, over the next ten years, many people are worried about how these developments will change what it means to be human, to be productive, and to have free will. The human contribution to extensive public-health initiatives based on vast volumes of data that may be gathered in the upcoming years about anything from diet to individual genomes will be enhanced by artificial intelligence. However, some ethical issues might arise with the benefits and efficiencies of AI algorithms.
Most AI technologies are now in the hands of profit-driven businesses or power-hungry governments. The computerized systems that make decisions for individuals sometimes lack values and ethics. These networks are interconnected on a global scale and are challenging to control. Additionally, machine intelligence will remain disruptive to all facets of human employment due to its efficiency and other financial benefits. While some anticipate creating new employment, others are concerned about significant job losses, expanding economic disparities, and societal unrest, including populist revolutions. Moreover, due to the rapid development of unmanned military applications and weaponized knowledge, falsehoods and propaganda may be used to destabilize human groupings severely. Some expect increased destruction of established sociopolitical systems and the potential for significant human casualties. Others worry about cybercriminals’ access to financial systems.
Conclusion
In conclusion, Can AI take over the world in that there will be a total replacement of humans in places? How long do you think it will take before this happens? Our daily lives are incorporating artificial intelligence, which is developing quickly. AI is becoming increasingly popular faster, changing how we live, engage, and enhance consumer experiences. AI influences our decisions whether we use cell phones, browse the internet, or make purchases online. This technology will become more widespread and utilized frequently in the military, education, transportation, and medical fields.
The United Arab Emirates is globally known for its technological prowess and the application of the same in various sectors. Consequently, the country has made significant investments in Artificial Intelligence (AI), which is the future of technology. To ensure that industries and citizens are ready for the full implementation of the innovation, UAE has created the National Strategy for AI. The plan acts as a foundation listing all objectives crucial in achieving related milestones. Moreover, it supports the UAE centennial 2071, which ensures that the region is ranked as the best country in innovation by 2071 (UAE National Strategy, n.d.). It is also important to note that this AI strategy will significantly impact UAE’s economy, education, and government development. One objective of the national strategy is to build a reputable name as an Artificial Intelligence destination. The other important listed goal is training talents in preparation for future jobs that will be AI-enabled. This implies that all industries in the country are supposed to fully embrace the plans meant to make AI an integral component of UAE’s operations.
The Fourth Industrial Revolution Strategy
The Fourth Industrial Revolution is one of the most significant milestones in securing future developments in the UAE. The component presents exceptional opportunities and numerous issues that should be evaluated, improved, and implemented. Consequently, the country aims to become a technology hub where interested parties worldwide can visit and collaborate in innovation-related experiments (The UAE Strategy, 2021). The strategy’s vision is to become a global lab for experimenting with innovations including AI, and this means that UAE has to be prepared to host scientists and groom home talents. Moreover, there are six pillars of the Fourth Industrial Revolution plans, including humans, security, experience, productivity, and frontiers, which have to be integrated to achieve related objectives.
United in Ambition and Determination Strategy
One of the values of the UAE that has contributed to the country’s excellence over the years is unity. The principle has been crucial in promoting perseverance and forward visioning, which originated from the country’s founding citizens who worked hard to build the country. UAE has made several significant achievements since its establishment and is currently ranked as one of the most developed regions globally. Consequently, the vision of the ambition and determination strategy is to use innovation and related knowledge to create an aggressive and robust economy founded on a powerful union (UAE Vision 2021, 2021). This plan aims to ensure UAE citizens work cohesively in improving the country’s living standards and achieving a sustainable environment. One of the components of the strategy is to support the heritage of the country’s founding fathers, which means the UAE has to remain among the most advanced nations. Consequently, this plan also applies to establishing a better future that will operate on artificial intelligence.
Government in 2071: Preparing for New Frontiers
This strategy was established by senior dignitaries of the UAE led by the country’s Prime Minister, Vice President, and the Ruler of Dubai during the annual World Government Summit of 2018. The purpose of the yearly meeting is always to discuss and exchange knowledge crucial in enhancing future developments of independent governments. Moreover, the summits propose milestones relating to technology and innovation meant to counter various challenges, and this should be achieved in a set timeline. Consequently, the Government in 2021 Guidebook was created from the summit, and its purpose was to prepare the country for future developments (Government in 2071 Guidebook, 2018). The document acknowledges robotics and automation as crucial megatrends that affect humanity. Even though they exhibit challenges, they also provide numerous opportunities for advancements. The guidebook gives strategies that UAE will use in assessing, supporting, and adopting evolving technologies, including artificial intelligence.
Dubai Plan 2021: Dubai Industrial Strategy 2030
Dubai is a significant tourist attraction recognized worldwide due to its technological advancements. Consequently, the implementation of the Dubai Plan 2021 has started, and to ensure its success, the UAE government has created an industrial strategy to facilitate the process. The plan is to be achieved by integrating economic sectors into the strategy to develop an attractive investment hub characterized by innovations (Dubai Industrial Strategy 2030, 2021). Moreover, the Dubai Industrial Strategy targets six major industrial subunits, including pharmaceuticals and medical equipment, maritime, aerospace, machinery and equipment, and aluminum and fabricated metals. Artificial intelligence covers all these subsectors, and the mentioned industries must be ready to embrace and integrate it into operational activities.
The Abu Dhabi Economic Vision 2030
As the capital of UAE, Abu Dhabi needs to lead in development if the overall economic objectives of the country are to be achieved. Consequently, in 2006, UAE’s leadership tasked the Abu Dhabi council of Economic Development to create an economic vision that will secure the Emirate’s future. Additionally, the plan is supported by the 2008 Abu Dhabi Policy Agenda, which involves stakeholders from the private and public sectors (The Abu Dhabi Economic Vision, 2021). One of the focus points of the strategy is developing a workforce that is both productive and highly skilled. Artificial intelligence is the future of human resources, and it will be essential to prepare future employees for the technology through training.
The UAE Industrial Strategy “Operation 300bn”
The UAE aims to achieve a sustainable national economy and has introduced The Industrial Strategy “Operation 300bn” to attain this objective through the industrial sector. Consequently, the government has commissioned the Ministry of Industry and Advanced Technology to develop projects that will empower over 13,500 businesses to increase the nation’s Gross Domestic Product (GDP) to AED 300bn by 2021 (The UAE Industrial Strategy “Operation 300bn”, n.d.). One of the advantages of artificial intelligence is that it aids in the accomplishment of sophisticated tasks while minimizing the costs used. Consequently, the Ministry of Industry and Advanced Technology will be keen on integrating AI into the programs and initiatives meant to improve the country’s GDP.
The Connection Between the Strategies and the Thesis
The thesis acknowledges that artificial intelligence is steadily becoming an essential part of private and public sectors, and therefore the United Arab Emirates is equally incorporating the technology into its operations. However, the paper also recognizes that the effectiveness of AI in various industrial activities strongly relies on preparedness. The technology can create both benefits and challenges for UAE’s industrial and economic sectors. Consequently, the discussed strategies are aligned to components of the thesis because they contain the government’s plans for technological developments. For instance, the National Strategy for Artificial Intelligence focuses on building a good name regarding technological innovations globally. The strategies pointed out issues to be addressed in ensuring the UAE is among the top countries in AI advancements by training the current and future workforce.
AI and UAE
General Results of the Think AI Workshop
Artificial intelligence is a priority in UAE’s short-term and long-term plans, and as such, the country is keen to involve relevant stakeholders in ensuring its implementation and effectiveness. Consequently, the workshop organized by the government in May 2019 was meant to involve various professionals in identifying AI challenges and proposing relevant solutions. The 70 participants listed several issues hindering the successful implementation of artificial intelligence in different sectors. Trust was on top of the list, and this means that most individuals working in organizations that have embraced the technology are not confident in its use. Without the trust element, it is impossible to convince people to embrace AI fully.
Moreover, the participants also indicated a lack of understanding of the components of AI and how it works. The majority of people in the UAE only come into contact with technology when they get employed or in their final college years. This issue is directly related to trust because workers cannot have the confidence to use a particular tool if they do not understand its purpose. The other mentioned challenge was the underinvestment of artificial intelligence across UAE industries. The seriousness of AI can only be seen when the government makes noticeable investments in technology. If AI is to support UAE’s strategies regarding technological excellence, it has to show commitment by purchasing relevant materials and sponsoring related research.
Similarly, the participants also mentioned that there was high competition externally from global fast movers. As indicated in various UAE development strategies, the country aims at establishing its name as one of the most technologically developed nations globally. However, governments across the world have invested more in AI compared to UAE, making it impossible to attract investors and scientists. Another barrier to the implementation of artificial intelligence mentioned in the workshop is the absence of talent. AI entails sophisticated technology that requires the right expertise to use in various organizations effectively. Unfortunately, few professionals can understand how AI functions and how it can be used to benefit institutions.
Aside from the challenges, the participants also identified solutions that can enhance the application of the technology in the future. For instance, since UAE is classified among technologically advanced countries, it should focus on exporting AI innovations and ideas. Similarly, the nation is among the pioneers of AI and should therefore not act as followers. UAE should be at the forefront of investing in related innovations and sponsoring related research. Moreover, the government has to ensure that citizens and individuals in the workforce thoroughly understand artificial intelligence. The key to AI success in the country is to ensure people are confident enough to trust and interact with the technology.
Trust in the Use of Artificial Intelligence
Although predictions indicate that artificial intelligence will have a tremendous impact on UAE’s economic growth, participants of the workshop mentioned trust as a severe challenge to AI’s success. For instance, it was noted that the existing regulations relating to the use of artificial intelligence are not sufficient, and the ones in use tend to overlap. Rules are essential in communicating the importance of artificial intelligence to a team of employees. Additionally, it boosts people’s trust since all questions on AI can be answered by definitions of the regulations. Nonetheless, the overlap between the rules stipulated by various authorities makes it difficult for individuals to understand artificial intelligence functions.
The other issue relating to trust listed by the workshop participants is data governance. The members indicated that the existing standards are unclear and therefore need to be redefined. Standards are meant to guide AI users on the “dos and don’ts,” and when they are not well-stipulated, professionals find it hard to apply related tools in various activities. The other identified problem relating to trust was technological acceptance. The participants indicated that several safety-related risks characterize AI’s application. For instance, most passengers will not be confident riding in an autonomous vehicle because they are not assured of their safety. Moreover, since AI is a new technology that will take time to be accepted, participants also mentioned a lack of coherence regarding consumer rights. Additionally, data privacy is not assured, and ethical guidelines related to using AI applications are not well-stipulated.
The Application of Trust as a Moderator in the Thesis
Trust is the foundation of any relationship, human or non-human, and therefore it determines the success of working with something or someone. It has been used as a moderator in the research since it is applicable in testing the readiness of institutions and employees to adopt the technology into their operations. Moreover, the Technology, Organization, and Environment (TOE) model identifies the three elements as part of AI readiness, which is a dependent variable in this case. Artificial intelligence preparedness determines its adoption in various UAE industries. Nonetheless, trust is the independent variable, and this means that the element plays a crucial role in ensuring that prepared organizations and individuals can comfortably embrace AI. Manipulating the variable helps in developing trust-related solutions that can enhance artificial intelligence acquisition.
References
Dubai Industrial Strategy 2030 (2021). Web.
Government in 2071 Guidebook (2018). World Government Summit. Web.
The Abu Dhabi Economic Vision 2030 (2021). Web.
The UAE Industrial Strategy “Operation 300bn” (n.d.). Web.
This study will explain how Artificial Intelligence (AI) and Machine Learning (ML) could improve clinical trials in the pharmaceutical industry. The contents of this paper will give details on how the two technologies could help advance the efficacy of clinical trials using data and statistics generated from companies that have adopted them. At the same time, to draw contrasts on the application of AI and ML in the health sector, the limitations of the technologies will also be elucidated to highlight areas of improvement that could be explored for future improvements and integration in clinical practice. To understand these areas of research in detail, in this analysis, emphasis will be made to highlight the role of the technology in addressing poignant challenges associated with clinical trials, such as the recruitment and selection of participants. This area of the investigation will encompass most of the analysis included in the present text but the role of AI in optimizing dosing regimens and improving the design of effective interventions will also be explored as supplementary analyses. Before embarking on these areas of analysis, it is important to understand some of the most significant challenges clinicians experience when completing their trials.
Cost and Time Associated with Clinical Trials
Recruiting patients for clinical trials is marred by challenges relating to the incompletion of tests and rising costs of sustaining volunteers throughout the investigation. This is why pharmaceutical companies pay a lot of money in research and development before they develop and present new drugs to the market (11). The costs associated with developing such drugs could stretch into millions or even billions of dollars, depending on the design and site selection protocol of adhering to clinical research guidelines (10). These findings mean that pharmaceutical companies have to invest many resources in minimizing the time taken to conduct clinical research and present safe drugs to the market. Part of the process involves making sure that a participant who is willing to engage in a clinical research trial is committed to staying in a program for its full length. However, clinicians do not always achieve this objective because of the high failure rates associated with past assessments and the costs of keeping the participants engaged throughout different stages or phases of clinical research (11). Figure 1 below shows the average cost of maintaining a volunteer, across different phases of a typical clinical research trial.
Figure 1. Cost of clinical trials (Adapted from Roth 11)
According to figure 1 highlighted above, the cost of maintaining the participation of research participants rises across four phases of clinical trial development. On average, the first phase of clinical research could cost pharmaceutical companies up to $15,700 to maintain one participant in the first phase of drug development. In the second stage, this cost could rise further to $19,300, after which it later increases to $26,000 in the third and final stages of the clinical trial. For most drug developers these costs are often prohibitive and inhibit their ability to develop reliable drugs and present them to the market affordably (11). This is why the cost of some drugs is often high and unreachable to patients who need them the most (10). High rates of incomplete clinical trials, reported in some cases, further compound the problem. Figure 2 below shows that researchers fail to complete about 80% of clinical trials fail on time, while another 20% are delayed for more than six months due to reasons attributed to recruitment and retention of patients in these investigations (11).
Figure 2. Challenges associated with completion of clinical trials (Source: Adapted from the works of Roth 11)
The findings highlighted above show that many pharmaceutical companies are experiencing challenges maintaining a healthy balance between the overall cost of conducting clinical research and the time it takes to complete them. This problem has affected clinicians across different areas of research (10). Consequently, there is a need to find better ways of managing these variables. AI and ML provide opportunities for doing so as highlighted below.
Bolstering Patient Recruitment Efforts
As mentioned above, recruitment is one of the most significant barriers for researchers to undertake successful clinical trials. In one study authored by Woo (15), this challenge manifested in identifying patients who suffered early-stage breast cancer. It was reported that out of the possible 40,000 women in the US who suffered this type of cancer, researchers only managed to recruit 636 patients in five years (15). It was proposed that AI and ML were useful in increasing the number of participants and the duration of identifying and accessing them. Particularly, AI was highlighted as having the power to help researchers to achieve this objective using different types of technologies associated with it (1). For example, Natural Language Processing (NLP) – a technique that identifies written and spoken words to find patients who have been diagnosed with specific conditions – was mentioned as having the ability to identify researchers who could take part in such an investigation within a short time. For example, it could be used to search doctors’ notes to find cases involving patients with a specific type of disorder. By doing so, clinical trials could be better focused to investigate the efficacy of drugs that are intended for a specific subpopulation.
In line with the above recommendations, A California-based company, known as Cedars-Sinai Smidt Heart Institute, used AI analytics to identify 16 participants suitable for a clinical trial in one hour (15). The use of traditional methods of recruitment could have taken months to achieve the same outcome. This example shows that AI is effective in recruiting relevant participants for a clinical trial within a short time. It does so without compromising the integrity of the research or its participants (5). Mayo Clinic, which is located in Rochester, Minnesota, has also reported similar impressive results in recruiting volunteers for clinical research because they reported an 80% increase in recruitment for participants wishing to take part in breast cancer clinical trials (15). By most standards of measurement, an 80% increase is significant enough to pay attention to the strength of AI in improving the recruitment and retention of research participants.
Cohort Enrichment
Another way that AI and ML may help to improve patient recruitment levels is via cohort enrichment. This could happen when the two technologies help to identify a subset of the population that a clinical trial outcome is best applicable. Broadly, the action means that the technologies are not designed to highlight the effectiveness of treatment options across a population of randomized control trials (1). Instead, they can help to improve the efficacy of trial outcomes by identifying patients that are not suitable for a specific investigation because their involvement would undermine the effectiveness of the trial outcomes (6). Relative to this observation, Woo (15) cautions that, even though AI may help to increase the number of participants that would be involved in a clinical trial, the surge does not guarantee an increased likelihood of success. However, the inclusion of unsuitable candidates in a clinical trial is almost definitely likely to minimize the success of such outcomes (3). AI and ML technologies help to minimize this risk by identifying persons who would undermine the efficacy of the clinical trials and by doing so, help to enrich the outcomes.
In an ideal clinical trial setting, the recruitment of patients should be done using genome patient-specific diagnosis tools where biomarkers that are targeted by a drug are present in a patient that should be the ideal recipient of a drug under development (15). Trials that follow this methodology exist but they are few in percentage compared to the overall number of investigations that are reported in research (2). At the same time, they are more expensive compared to conventional trials, particularly when medical imaging techniques are deployed to identify the right group of participants for an investigation (10). AI and ML help to bridge this gap in the research by identifying patients with unique biomarkers that would be relevant to a specific trial (4). For example, sophisticated AI and ML techniques that are under development have the potential of merging specific Omic data with electronic medical records (EMR) to identify patients or clinical trial participants with specific types of data that would be relevant to a clinical trial (17). This process helps to identify endpoints in clinical research trials that can be sufficiently measured for improved efficacy and outcomes. They improve a researcher’s ability to identify and characterize specific subpopulations of patients that are suitable for specific trials through AI and ML-based techniques, such as NLP and Optical Character Recognition (OCR) methods (15). The application of these techniques in clinical research trials means that the process of reading and compiling pieces of evidence relating to clinical trials will be fully automated using the technologies.
AI and ML techniques also have the potential of harmonizing EML data because they are commonly scattered and available in different formats owing to their large volumes and velocity (5). The data source-agnostic nature of AI data helps to overcome these barriers, thereby leading to the development of harmonized EMR datasets for comprehensive analysis (8). This process of analysis is important in designing tools for clinical trial enrichment and the discovery of patients with unique biomarkers for specific clinical research. Other benefits associated with AI and ML use that are realizable using this technique include pre-clinical compound discovery and improved techniques for highlighting compounds associated with clinical trial testing (4). Prediction-based AI and ML tools could also help to achieve these outcomes by identifying correlations among patients, biomarkers, and clinical outcome indications. This process has the potential of identifying candidates that have a higher likelihood of success in the implementation of clinical trials as well as those who are unsuitable for a trial before they take part in it (3). In this regard, AI and ML help to improve the selection of patient cohorts for clinical research, thereby enriching cohorts of patients willing to volunteer in an investigation.
Improving Clinical Trial Design
The design of clinical trials is important in understanding the flow of information in a given investigation that would ultimately lead to the collection of reliable data and the development of quality findings. Indeed, as highlighted by Harrer, Shah, and Antony (3), clinical trials often contain protocols that stipulate processes and procedures that researchers should follow when designing clinical trials. Given that most of them rely on a variety of sources to develop their findings, AI and ML could help to analyze such data faster and more efficiently than a human being does, thereby providing a stronger foundation for developing more reliable clinical trial designs (7). For example, the technologies could scan information from relevant clinical journals, drug labels, and information emerging from private pharmaceutical companies, faster and more effectively, thereby making it easier to calibrate information that would be better suited for a treatment design.
By using such information, it is easier to understand how different aspects of a proposed trial could influence the cost and eligibility requirements for a specific case or analysis (16). Broadly, the above findings are important in understanding specific aspects of a clinical research design that affect key success factors of a clinical trial. In this regard, AI and ML emerge as data-driven guides for developing better clinical trial designs compared to conventional means. Therefore, they are useful in improving the clinical trial design, thereby enabling researchers to make improvements in specific areas of research. At the same time, the use of AI and ML in improving protocol design makes it is possible to develop drugs faster and at a cheaper cost than conventional methods do.
Optimizing Dosing Regimens
Clinical trials aimed at improving the efficacy of drugs could also use AI to optimize dosing regimens. This may happen through improved efficacy in assessing issues relating to treatment and drug administration (9). For example, AI and ML have been used to understand the efficacy of combining different types of drugs by tinkering with existing schedules and developing a larger body of literature that estimates their efficacy and safety (13). By optimizing dosing requirements, AI and ML also help to minimize the risk of patients suffering from adverse events relating to clinical trials (10). At the same time, they could help to minimize the incidence of trial delays, which are commonly associated with insufficient dosage requirements (17). Indeed, by analyzing data relating to how different groups of patients respond to specific treatment plans, specific dosages could be better tailored to suit unambiguous demographics to minimize their side effects and improve their overall efficacy.
The role of AI and ML in optimizing dosing regimens has been demonstrated by companies that have used the technique to find the right treatment plans for patients with cancer (15). For example, Zenith Epigenetics used these technologies to find the correct treatment plan for a patient suffering from Prostate cancer. Notably, the company used AI algorithms to find the right dosage for the drug ZEN-3694, which when combined with Enzalutamide, could treat the same condition (15). The AI algorithm used was known as CURATE and the efficacy of the treatment plan was assessed by reviewing the patient’s clinical data and comparing it with tumor size before and after treatment (15). The findings were more vivid using the two technological tools. Cancer biomarkers in the blood were also assessed on the same platform to understand the efficacy of the treatment plan and the results were used to find the best treatment regimen for the patient (15).
Overall, this example shows how AI and ML could be used to find individualized treatment options for patients suffering from different types of conditions. Their importance is magnified when understanding the best combination of drugs that patients could take to treat a specific condition. Therefore, their use is important in improving the efficacy of existing and new treatment plans, thereby improving the administration of drugs. Doing so will be a departure from traditional eyeballing techniques that are commonly used by physicians worldwide to improve treatment regimens (15). Despite their commendable track record in enhancing this area of clinical research, some challenges have been associated with the adoption of AI and ML.
Limitations of AI and ML
Implementing AI in clinical settings is one of the foremost challenges associated with its adoption. However, given the unstructured nature of doctor’s notes, it may be prudent to have background information about specific cases associated with patients who manifest specific symptoms or have been diagnosed with specific conditions (12). For example, specialists may describe one condition differently or in multiple ways. For example, some doctors may describe a heart attack as a myocardial infarct, or a myocardial infarction, depending on their training or institutional environment (7). In some cases, the same condition may be described as MI (15). Such discrepancies may limit the ability of ML techniques to generate accurate data to support clinical trials. However, it is possible to address such concerns through improved feedback loops where machine learning is deployed to train AI to detect and correct the effects of such variations on clinical outcomes (9). This possibility leaves room for further application of AI in clinical trials.
Additionally, although machine learning has been proposed as one of the most significant ways of improving AI efficiency, it still requires significant investments in data generation (13). This is a challenge for most data analysts and researchers because they need many hours to manually annotate data that would be used in tests (14). Variations in the management of data across different medical fields and data management processes across medical institutions are still too significant to ignore because they could cause discrepancies in data usage, which may affect clinical trial outcomes (2). In this regard, there is no universal understanding of clinical trials data.
Another challenge associated with the use of AI involves the use of third-party tools to access patient data. Most developers engage third parties to extract and manage data to improve research outcomes (12). This strategy could cause ethical violations stemming from a breach of confidentiality agreements between patients and their medical service providers (12). Particularly, the involvement of third-party actors in data mining poses the biggest challenge in this regard because they are foreign players in the management of doctor-patient relationships. Therefore, there is a need to be cognizant of the effect of including other players in the use of AI and ML in clinical trials especially because different countries and institutions have varied interpretations of this concern.
Conclusion
The findings of this investigation show that AI and ML are useful tools for recruiting the right participants for clinical trials and maintaining their participation throughout their lifecycles. They also help to reduce the cost and time taken to complete such trials because they are more effective and inexpensive to implement compared to traditional methods. However, it is important to be mindful of their limitations because they could cause ethical violations and misinterpretations in data analysis, depending on the institutional policies and environments of various healthcare facilities.
References
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One of the biggest aspects that significantly affected my understanding of the issue of utilizing artificial intelligence for a variety of tasks is the increasingly important role of human interventions. Even though the initial reason behind implementing artificial intelligence instruments was to replace humans, the existing evidence shows that individuals managing specific instruments are still necessary, as machines would not perform as robustly as they do now (Coombs et al., 2020). From my point of view, artificial intelligence should be researched further in order to help the community as a whole develop a different outlook on how digital tools could be utilized to improve everyday life, manufacturing processes, or governmental responses to natural calamities, for example. The key problem that remains unsolved is the questionable influence of human factors that cannot be either mediated or mitigated with the help of available resources. Either way, it is going to take humans sometime before they come up with proper artificial replacements that would not commit mistakes.
Another important thing to mention is that I have significantly enriched my knowledge base via the interface of the completed research paper since I had gained access to additional aspects of artificial intelligence and its areas of utilization. The impact of artificial intelligence on society has always been a problematic topic that was addressed by researchers only to a certain extent due to the presence of multiple elements of bias. Many researchers believed that artificial intelligence instruments are never going to outperform their human counterparts due to the lack of behavioral responses and unique human logic (Parry & Battista, 2019). Nevertheless, all the evidence that I had the opportunity to scrutinize showed me that artificial intelligence is currently on the rise and already showing the first signs of emulating human decision-making processes. To my mind, this is a huge step forward that has to be reckoned with if humanity expects to reap the most benefits from technological advances.
It may be safe to say that the existing research paper also allowed me to look at the issues related to artificial intelligence from a completely different perspective, especially knowing that this tech-related phenomenon is not going to disappear. The omnipresent nature of artificial intelligence makes me believe that the future holds an incredibly high potential for this phenomenon in all spheres of human life. Researchers are exerting an incredible amount of effort in an attempt to humanize artificial intelligence instruments, and it may be considered one of the biggest prospects for all people across the globe. The process of redefining the value of artificial intelligence continues today, as many civilians are reconsidering their outlook on different technologies when they “meet” them in real life. Once believed to be science fiction, artificial intelligence currently holds its own against humans when it comes to quite a few essential tasks – and that is truly exciting.
At the beginning of the semester, I thought that most artificial intelligence technologies were only used for complex calculations and predictions that did not have anything in common with everyday human lives. With Coombs et al. (2020) claiming that practically every sphere of life could be altered with the help of artificial intelligence, I did not believe them at first, but now I see that from supply chain management and manufacturing to social work and personal assistance, machines based on artificial intelligence are actually taking over. This does not mean that I have instantly reconsidered the role of human contribution, but it definitely hints at the fact that many activities performed by actual people could be completed by machines in order to reduce potential harm and the number of resources required to maintain the required level of performance. The idea should be to embrace technological advancement without putting a strain on human workers that might be on the verge of being replaced by machines. Now, I am certain of the fact that artificial intelligence is an inextricable part of the human future that cannot be removed or altered for the worse since humanity actually relies on technology to an incredible extent.
I believe that the article written by Parry and Battista (2019) had the biggest influence on my understanding of the subject. The increasing prevalence of technological advancements has created multiple opportunities for humans to improve their wellbeing while saving vital resources, such as time and money. The pace of transformation is also rather high, and it makes me think that Parry and Battista (2019) have captured the role that artificial intelligence plays in helping employees find their vocations and enhancing the significance of their contribution to organizational success. While many other researchers saw artificial intelligence as a viable threat, Parry and Battista (2019) showed that even the processes inherent in human resource management could be strengthened with the help of artificial intelligence. Of course, there are numerous probable risks affecting teams that utilize this technology, but the core idea should be to invest in research and development and make sure that staff members are on the same page. When looking at the evidence presented by Parry and Battista (2019), I saw an objective view of technology that was connected to everyday human life in a reasonable, well-thought-out manner.
As soon as I started writing the research paper, I realized that my thoughts about artificial intelligence were practically biased since I had been genuinely certain of the fact that such technologies cannot replace human workers in all positions. While it is generally true, I found that there are quite a few important advancements that make it safe to say that artificial intelligence is much closer to replacing humans nowadays than it was several decades ago. The progress is real, and I was able to grasp its influence throughout the process of writing the research paper. Owing to Coombs et al. (2020), I was also able to eliminate the prejudices that I had in regard to the multidisciplinary applications of artificial intelligence. Evidently, there are some occupations that cannot be replaced with machines, but the trend is rather lucrative, and more similar opportunities have to be investigated. Overall, the key takeaway for me is that the role of technology cannot be underestimated because its complexity increases and paves the way for an incredible future.
When writing the research paper, I have also recognized the essential role that my classmates and the professor played in my understanding of the topic. A multitude of opinions and valuable insights into artificial intelligence allowed me to reintroduce myself to the topic of the importance of technology and look at it from several different perspectives. If not for the professor and classmates, I would not be able to grasp the essence of artificial intelligence and get rid of my slight bias related to the role of human workers within the environment where technological solutions continually gain more attention from researchers. The best way to describe my interactions with classmates and the professor would be to label them as a lucrative collaboration that allowed me to improve my professional and personal skills. There are numerous things that I still have to learn, but I can definitely say that my knowledge base related to artificial intelligence has been replenished and extended in a meaningful way.
References
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.
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.
Experiences of isolation, social exclusion, and loneliness foster numerous detrimental health outcomes, including depression, anxiety, low self-esteem, and chronic feelings of worthlessness, increasing a person’s risk of mortality and morbidity. The lack of companionship is a rising global public health concern whose prevalence has reached epidemic proportions, exerting enormous pressure on the healthcare sector and communities. A global survey indicated that an estimated 33 percent of adults worldwide and over three in five Americans experienced solitude (Varrella, 2021; Demarinis, 2020). These statistical findings underpin the pervasiveness and severity of the crisis, necessitating the adoption of such innovative interventions as artificially intelligent emotional surrogates (AIES) to alleviate the loneliness and associated trauma. However, these technologies enhance the distress through their mechanical nature, overdependence on humans, the inability to satisfy a person’s emotional needs, and relative incompatibility with senior citizens. Although adopting artificial intelligent-driven technologies can modify social norms by replacing and reducing the significance of human relationships, their use should be encouraged to help mitigate the detrimental consequences of loneliness.
Rising Levels of Loneliness
Loneliness is a fast-growing societal problem with severe negative impacts on health and the overall wellbeing of individuals. It encompasses an affect distress and cognitive discomfort emanating from a perceived gap between a person’s desire for social relationships and the actual degree of connectedness (Hoorn, 2018). The global magnitude of people reporting the lack of meaningful companionships has reached alarming levels, with various statistics indicating that 33 percent of adults experience solitude globally (Demarinis, 2020). According to Varrella (2021), over three in five Americans feel socially excluded, which is a strong agent for subsequent isolation. From this perspective, the frequency, intensity, significance, and degree of social interactions, relationships, and connectedness have been shrinking rapidly in recent years. Notably, these changes have been associated with accelerated adjustments in the family structure, increased digital orientation, urban social lifestyles, and sedentarism. Macia et al. (2021) contend that these developments and alterations impact the quantity and quality of social interactions as well as people’s values and expectations about them. Therefore, loneliness and social exclusion are rising global public health concerns affecting millions of individuals and communities worldwide.
Additionally, loneliness and increased social disconnect are major causal pathways and risk factors for numerous morbidities, including psychiatric illnesses such as psychosis, depression, and anxiety, coronary heart diseases, and attenuated immune systems. According to Loveys et al. (2019), the lack of companionship increases peoples’ mortality risk by 32% and exerts an additional financial burden on America’s healthcare system by $6.7 billion annually for older adults only through increased facility utilization. This implies that social connectedness and belongingness are innate human needs with severe health ramifications whenever they are unfulfilled. Notably, innovators have progressively harnessed technology and integrated artificially intelligent robotics with sophisticated natural language, emotional, and social processing abilities to help overcome loneliness and the resultant trauma. According to Barreto et al. (2021), AIES is a prominently successful and promising intervention to prevent and mitigate the adversarial ramifications of solitude. Therefore, the adoption of these technological advancements should be encouraged since they provide a substitute for the increasingly elusive traditional social connections.
Contradictory Viewpoint
Although artificial intelligence-driven technologies are providing multiple solutions to numerous challenges, their continued adoption as a replacement for humans has profound societal ramifications. Indeed, the roles individuals play in other peoples’ lives are increasingly being mediated by robotics with remarkable linguistic, social, and emotional sophistication. The implication of this phenomenon is the progressive and accelerated destruction of human connections, communal bonds, and authentic interactions and interrelationships. Caic et al. (2019) assert that social robots irreversibly affect a person’s behavior through the perceived replacement of people. This explains the increasing failure of humans to fully appreciate others and the growing propensity to undervalue one another. In this regard, AIES technologies are destroying social arrangements by creating the notion of human substitutability and replaceability.
Additionally, AIES and robotics do not adequately satisfy the innate human need for social connection and may instead aggravate the social disconnect and loneliness due to their complete dependence on humans. Despite the sophistication and level of advancement, social robots and AIES are mechanical and bereft of feelings and emotions. They fail to provide and deliver authentic emotional connection and completeness to people, thereby suppressing the need for the search of actual and meaningful relationships. Indeed, lonely people desperately need genuine companionship and that psychological bond that extends beyond the basic physical presence of an inanimate object robotically responding to cues, at times predictably. For instance, words, language, and communication are more than their denotations and their understanding and conveyance. However, AIES cannot effectively integrate these profound nuances of interaction or express agreeableness, conscientiousness, and openness. From this perspective, adopting AIES to alleviate loneliness and trauma could be an expensive expedition that does not comprehensively capture the core components of human relations or provide value after the functional interaction ends. Machines cannot deliver the elevated sophistication of person-to-person engagements and could therefore be delivering false companionship.
Artificial intelligence-driven surrogates can potentially transform and disrupt social norms and individual behavior. As these innovative technological advancements increasingly gain more ground into peoples’ lives and attempt to replicate human habits, they may change various aspects of a person’s character and their interactions with others. For instance, AIES and other social robotics are unable to express such feelings as warmth, friendship, kindness, and cooperation and may ultimately impair peoples’ ability to embody, convey, and demonstrate such traits. From this perspective, the increased adoption of humanoid robots could be detrimental to social norms and erode the distinct attributes which define and characterize human nature.
Arguments for the Adoption of AIES in Combating Loneliness and Trauma
Social interactions and relationships are an innate human need and are fundamental to peoples’ holistic wellbeing. According to Holt-Lunstad (2017), companionship and belongingness are profound biological necessities vital to the overall health and survival of an individual. However, despite the indispensability of these connections, global statistics indicate an increasing prevalence of loneliness and solitude. Holt-Lunstad (2017) asserts that existing evidence points to growing relationship distress with additional findings illustrating the shrinking frequency, intensity, quality, and quantity of interactions. These patterns engender detrimental health ramifications and increased risk of morbidity and mortality. In this regard, it is imperative to harness technological innovations and artificial intelligence to serve and fulfill the unmet human needs and mitigate the adversarial effects of loneliness (Barreto et al., 2021). With the rapidly declining social connections and networks, these advancements are critical. Therefore, the adoption and integration of AIES in combating loneliness and trauma is paramount and reflects the realities of the modern world.
The proliferation of Global Levels of Loneliness
Multiple statistical findings illustrate the alarming trend of rising loneliness and declining social connectedness. For instance, Holt-Lunstad (2017) notes that the communal networks have progressively become less diverse and that the average sizes of such relations have declined by one-third over the last two to three decades. The phenomenon and its prevalence are exacerbated by the increase in the global percentage of the aging population, rendering more people less socially disconnected. Okabe-Miyamoto et al. (2021) posit that the emergence of the COVID-19 pandemic has aggravated social disconnect, isolation, loneliness, and exclusion, resulting in a profound psychological toll on physical, emotional, and psychological wellbeing. From this perspective, the absence of meaningful companionship is a pressing global health issue, which necessitates the adoption and subsequent deployment of all available options to address it effectively.
Additionally, previous studies have demonstrated the effectiveness of AIES, such as Chatbot and other social robots, in addressing social exclusion and the accompanying adverse effects. The advancement of cutting-edge technology has generated numerous solutions for many health challenges, and humanoid robotics have mitigating impacts on the adverse ramifications of loneliness (de Gennaro et al., 2020). From this perspective, there exists robust evidential information depicting the potential of AIES in addressing health-related challenges emanating from solitude and providing symptom relief. A meta-analysis evaluating 23 randomized controlled studies established that these intelligence-driven robots provided effective assistance almost equivalent to person-to-person interaction (de Gennaro et al., 2020). Moreover, these technological advancements offer a substantial amount of companionship, social and emotional support, thereby enhancing the individual’s overall health and reduced exposure to such psychological disorders as anxiety and depression. From this perspective, the adoption and usage of AIES should be encouraged and escalated since these technological advancements provide effective relief against loneliness and trauma as well as promoting people’s health.
Reducing Burden and Pressure on the Healthcare Systems
Numerous pieces of scientific evidence indicate that people who feel socially connected and are embedded in high-quality relationships are at a reduced risk for multiple morbidities and causal pathways for mortality. This implies that social relationships and companionships are a major health determinant and a health risk marker (Holt-Lunstad et al., 2017). This implies that social exclusion stretches and exerts pressure on the healthcare systems through increased hospital utilization. Unlike most animals, people obtain what they need from their social groupings, indicating that human capacity for socialization is critical for their survival. This view is corroborated by Holt-Lunstad (2017), who illustrates the indispensability of companionship in the use of solitary confinement as a form of punishment and torture. In this regard, the absence of meaningful associations contributes negatively to the overall wellbeing of an individual, thereby increasing their use and dependence on health services and detrimentally affecting the economy (Barreto et al., 2021). From this dimension, adopting AIES technology provides an avenue through which this strain and burden on healthcare facilities can be eased by preventing loneliness and mitigating the detrimental health consequences.
Moreover, AIES provides an effective alternative to the increasingly elusive traditional social connections. Over the years, advances in robotic technology have allowed the development of sophisticated human replicas with remarkable progress in their linguistic, social, and emotional abilities, effectively enhancing their capacity to offer interactive and meaningful engagement (Macia et al., 2021). In this respect, social robots reflect the reality of the changing social norms and declining frequency, quality, quantity, and intensity of the conventional social networks. Therefore, the use of AEIS should be encouraged since they attempt to fulfill a genuine human need.
Better Option than Pharmacological Interventions
Emotionally attuned and intelligently responsive AIES provide a better non-pharmacological alternative for helping people to overcome loneliness than the medicinally induced interventions. Since a deficiency in social interactions and the feelings of disconnectedness precipitates severe health repercussions and immunologic impairment, it has generally been regarded as a symptom of psychological disorders. Consequently, physicians may prescribe medications to alleviate the emotions of loneliness and prevent the resultant health deterioration. However, this approach is not the most desirable since it only suppresses the clinical manifestations of the condition without addressing the foundational need. Since most of those affected by this phenomenon are the elderly, the risk of unwanted and unexpected pharmacological complications is considerably high (Heser et al., 2018). In this regard, AIES is arguably the best option compared to the potential pharmacological strategies, and therefore, their use should be encouraged as the ideal standard for the prevention of loneliness and alleviating the associated trauma.
Recommendations for the Use of AIES
Social association and relationships are critical constituents for a person’s overall wellbeing and survival. Multiple statistical findings provide evidence regarding the central role of interactions and engagements for peoples’ existence and enhanced quality of life through such effects as improved the perception of life as meaningful and self-esteem. Despite this profound nature of the need for connections and their impact on a person’s health, the traditional versions of communal belongingness and meaningful companionships are increasingly becoming elusive and significantly difficult to accomplish (Holt-Lunstad, 2017). However, a growing number of artificial and intelligence-driven applications and devices are assisting humans in satisfying this basic psychological need and achieving some degree of connection and companionship. Thus, the available technological advancements, such as the AIES, can be harnessed and exploited to provide solutions to the challenges of loneliness and mitigate the detrimental health impacts of the unfulfilled human need of association.
Moreover, the social structures and norms are undergoing tremendous transformation resulting from the adjustments in the family structure, increased digital orientation, urban social lifestyles, and sedentarism. According to Macia et al. (2021), these developments and societal alterations have a profound impact on the quality, depth, and quantity of social interactions and peoples’ values and expectations. This illustrates the continued decline in the value and significance of engagements as well as the efforts people direct in pursuing friendships, connections, and associations. In this regard, fewer individuals are currently engaging in communal activities, outgoing expeditions, and other forms of participatory interactions compared to previous periods. Consequently, the trend increases their risk and susceptibility to a wide array of morbidities and even mortality. However, AIES provides practical solutions, which reflect these social transformations and the prevailing realities. From this perspective, AIES is an effective substitute to the shrinking traditional social connections resulting from changes beyond the control of individuals. Therefore, the use of AIES should be encouraged as a realistic and feasible alternative to human associations.
As technology advances, AIES developers are incrementally enhancing the abilities of these robots to encompass remarkable sophistication in the products’ linguistic, emotional, and social capacities. Indeed, their effectiveness in serving and satisfying the unique human need of connection and companionship is improving rapidly and providing value with minimal or no unintended consequences. For instance, de Gennaro et al. (2020) posit that some chatbots can evoke emotional and social responses and serve as a buffer against the detrimental effects of loneliness and solitude. According to Hoorn (2018), humanoid robots also stimulate and strengthen peoples’ independent ability and resilience in countering exclusion. As a result, individuals develop a positive adaptation and reinforce their coping potential to the absence of companionship, which minimizes the risk of negative health outcomes. This cements the position of AIES as agents for improved overall health and social support. Therefore, they should be adopted as a preventive intervention for loneliness and a mitigation strategy for the associated trauma and adverse health outcomes.
Conclusion
Social relationships, associations, and interactions are critical ingredients and vital biological components for peoples’ overall wellbeing and survival. The absence of this connectedness and meaningful relationships is an increasingly pressing global public health concern with severe adversarial impacts on individuals and communities. Despite this indispensable nature of companionships, their frequency, quality, intensity, depth, and quantity are rapidly dwindling, exposing large segments of the population to numerous morbidities and an increased risk of mortality. However, innovation and cutting-edge technological advancements, such as AIES, provide feasible substitutes to the conventional social connections, thereby protecting people from loneliness and the associated negative health outcomes. These social robots replicate human relationships and serve an unfulfilled innate need. They significantly augment the declining natural person-to-person engagements through their sophisticated interactive abilities, minimize the pressure on healthcare facilities, provide a better alternative to pharmacological options, and reinforce peoples’ coping abilities. However, despite their sophistication and advancement, AIES and other social robots are mechanical and bereft of feelings and emotions and could destroy social norms and arrangements by undermining the natural human connection.
