Introduction
Artificial is something that is made by humans which does not occur naturally but often emulates something natural. Intelligence is the skill to obtain data then apply it to achieve a task. Therefore, Artificial Intelligence (AI) is to be made by man to acquire knowledge and use that knowledge to achieve a task. Scientifically, AI is the philosophy and advancement of computer structures to accomplish tasks that would normally require human intervention. The tasks include recognizing speech, perceiving vision, making decisions, and translating language. Its basic objective is to execute tasks accurately and efficiently.
AI is a concept that was coined in 1956 at least for the modern part of it. However, the idea of an artificial object coming into life with intelligent human capabilities has been around since the ancient Greeks had mythologies about robots and the Egyptian and Chinese engineers who built automatons. Scholars like the Greek philosopher Aristotle are part of the early team that dreamed about the automation of an intelligent system that would reason like man. He attempted to understand AI by coding the process of philosophy and logic thinking. Aristotle, through a system of reasoning called a syllogism, tried to analyze the process of logic intelligence. That that in which, certain things are being stated, something other than what is stated follows of necessity from their being so. A famous illustration of syllogism is shown below.
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All humans are mortal (stated symbol)
All Greeks are humans (stated symbol)
All Greeks are mortal (result symbol)
“ All humans are mortal” is a statement as well as “All Greeks are humans”. “All Greeks are mortal” is a result. It is a fact that all humans are mortal. It is also a fact that all Greeks are human. So, if a Greek is human, it is true to assume that by the fact that they are human they are also mortal. The syllogism is Aristotle's early contribution to the field of AI. In his reasoning, we can state that all B's are A's and that all C's are B's leading us to the result that All C's are A's (Nilsson, 2010). In a simple AI system, two stated correlated symbols need to be present to come up with a particular result symbol. Correlation between the symbols is key for the result to make sense. Take for instance,
All humans are mortal (stated symbol)
All Greeks are mortal (stated symbol)
? (result symbol)
The result symbol is unknown because symbol 1 and 2 are purely independent stated symbols. Also, the result symbol does not necessarily have to be true unless the stated symbols are all true. In a particular field like accounting or medicine, the stated symbols are in relation to the field for the result to be of the same field. This is how Aristotle understood the initial aspects of automated reasoning and is still applied in a complex manner today for AI to make sense.
The AI field was formerly initiated in 1956 at a conference at Dartmouth College, Hanover, New Hampshire. This is the beginning of the post-modern field of AI. It also has not been an easy task for the AI to progress during this period. Developed countries were in a race to be the first among others to figure out AI similar to the races to the moon or discovering life on Mars. However, several reports emerged criticizing the non-progress of the AI field. It led to the withdrawal of government funding leading to the AI Winter of 1947-1980. The British government later revived funding into the AI field in the 1980s to compete with efforts made by the Japanese. Another AI winter occurred between 1987 and 1993 coinciding with the collapse of some early computer markets as well as a reduction in government funding. Progress in the AI field grew in 1997 when Deep Blue, an IBM’s computer beat legendary chess champion Garry Kasparov in a game of chess (Lewis, 2014). Inroads into the AI field is now possible because of crowdfunding. Figure 1 below represents funding statistics for AI startups from the year 2014 to 2019.
Figure 1
$4 billion was raised in 2014 then preceded by $5 billion in 2015. $26 billion raised in 2019 proves that inroads have been made in the AI field and more interest is growing. The interest attributed to new business trends that stipulate that AI intervention in businesses offers a competitive edge (Rana, 2018). Presently AI systems have been integrated into numerous fields like security, medicine, physics, engineering and business. The objective of AI is to offer speedy solutions in the somewhat problem-solving process that would have taken longer using human resources. However, it is difficult to talk about AI in the modern era without mentioning Machine learning.
Machine Learning
In AI, technicians seek to automate the process of intelligent reasoning through some form of coding but this means that the user has to key in some parameters for AI process to yield results. Machine learning is a set of rules followed in calculations that generate more calculative rules also known as algorithms. These rules are a sequence of instruction used to solve a problem. Computers use algorithms to organize huge sums of data into useful information. Machine learning is the aspect of giving computers commands that permit it to study from data without the necessary step of giving it commands every time you expect it to generate a solution. Machine learning introduces the aspect of autonomy into AI. Full autonomy causes fear to users who are of the assumption that autonomy would render the human aspect invalid. There are different guidelines upon which learning can be exercised. Machine learning can employ a mix of different techniques which are generally categorized into three. 1. Supervised learning where the learning set of rules is given characterized information and the desired data. For instance, images of cats characterized as cat will help the set of rules identify the rules to classify pictures of cats. 2. Unsupervised learning where the algorithm is not labelled and it is instructed to classify patterns in the raw data. For example, a retail e-commerce recommendation system coming up with patterns on what retail items are frequently purchased together. 3. Reinforcement learning where the set of rules interacts with a wider setting that delivers response in terms of rewards and penalties. For instance, an autonomous self-driving car being rewarded to stay on the road (Internet Society, 2017). In summary, Artificial Intelligence is the reasoning system in a pool of data while Machine learning is the part that gives that system autonomy.
Artificial Intelligence in the Accounting Sector
Artificial Intelligence continues to extend its capabilities of computing such as systems making predictions and making changes in accordance with the predictions. AI is set to revolutionize the business world according to an MIT-Boston Consulting Group survey conducted on over 3,000 executives. 85% of the executives decreed that Ai would give them a competitive advantage. 79% of the executives believe that AI as part of technology would increase productivity in the organization (Snyder, 2017). AI is being used in accounting firms to take on administrative tasks such as high-speed analysis of large volumes of data, something that an accountant would take longer to achieve. Using AI in the accounting industry is likely to streamline operations that will improve productivity and accuracy at a minimal cost. Data supervision and dispensation becomes fully computerized and is, therefore, one of the key benefits of AI. With AI, data is easily recognized and categorized and available for the right departmental head (Rana, 2018). The ultimate benefit of using AI in any business setup is improving cost management by the organizations using it. Understanding the impact of AI on the accounting sector calls on reviewing some of the functions AI can undertake.
1.Periodic Close Procedure – Accounting information in terms of data is normally used to give periodic close information and takes time using human resources. AI can provide information from different sources, consolidate, and merge it. This process is done in the shortest time and information produced is accurate. Having less time in generating such information gives an organization more time to strategize on what they can do about the information generated.
2. Procurement – Procurement is a business task that involves piles of paperwork and tracking processes. In most instances’ procurement practices vary with different organizations making compatibility a hectic issue. The world of AI with API machines help overcome such challenges. Once integrated, amorphous data can be managed making the procurement procedure paperless and efficient. Aspects like charge variations among numerous dealers can be easily be traced.
3. Accounts payable and Accounts Receivable – Existing accounting systems already have automated invoice systems but could use AI systems to automate and streamline the process.
4. Audit- Under normal auditing circumstances, auditors require to look through physical files in cabinets and track who has done what. Digital working makes auditing easier as all information is available in the system. Auditors can easily ascertain who accessed what file or who filed what records. AI increases the accuracy and efficiency of an audit process.
5. Expense Management – Expenses need approvals in line with company policies. Ascertaining approvals manually while going through a checklist whether the expense complies to company expenses is a tall order. Introduction of an AI expense approval system streamlines expense management.
6. Chatbots – Accounting firms are entities in the service industry who sometimes get clients with common simple queries. An AI chatbot is capable of answering common queries giving the response team time to handle more serious queries.
Impact of Artificial Intelligence in the Accounting Sector
Advancement of technology through automation has led to the loss of jobs in different industries like the automotive industry. Areas, where people were used for assembly of cars, are now using robotic machinery. Automation led to layoffs and increased efficiency in industries that introduced it. Even if there were gains there were losses in terms of human capital. However, the loss was nothing compared to the 24-hour shifts they could pull off. Machines are maintained at a low cost and have no emotional inhibitors. The AI path will automate accounting systems soliciting fear among many in the profession. Fear that their jobs will become extinct and responsibilities handed over to an AI system. But this is not entirely true. We cannot forecast the jobs of the future but instead horn the fact that we can believe in the notion that jobs will continue to be created in the accounting sector. Rather than lose a job, it would change to more strategic initiatives like cost improvement, process improvement, capital optimization. Automation would focus on more repetitive tasks leaving professionals more time to undertake more serious undertakings. The future guarantees disruptive changes throughout the world of AI that will profoundly impact the employment landscape (Griffin, 2019). It means the skills required in future will be different from the ones required presently. But it does not mean that new graduates and individuals in the industry will lose. They can learn new skills and fit right back in.
Only a few working professionals believe that AI and machine learning will have a profound effect in the labor industry. Another few believe that the two technologies will make it possible to automate knowledge-worker tasks. Tasks that have been regarded as impossible to automate in the previous work periods. But since this will be possible shortly, we should expect the caseload for judgement work to increase and made possible by having more data available. More data is saved by organizations in cloud computing, already a product of AI. Having loads of data is not a problem but using that data to make guided decisions has been the challenge. Human intervention on multitudes of data is time-consuming and sometimes impossible. Professionals have to pick a sample of data to try and make decisions. AI can work the load, therefore, use entire databases to make informed decisions. These decisions will be accurate as no aspect of human error is given chance by an AI system. The process of getting to a decision will also be fast and timely.
Auditing is one of the key functions practiced in the accounting sector. This is a process that takes a lot of time, especially for large organizations. Auditing can run into months delaying important decision-making timelines. For instance, an organization may need to finish its auditing process before going public. Delays in the process lead to a delay in going public. This can give a competitor going through the same process an edge in getting to the public market before the first one. AI will increase and improve auditing as the system would allow asking more questions and process more data that is humanly possible. Such a system should also be able to guide auditors into making informed recommendations after the process. Since AI is a machine, auditing is fast and accurate giving the organization more time to focus on the recommendations made.
Prospects of AI on Accounting
Prospects on the impact of AI on accounting seems to be positive. More accounting data can be handled and used to make more definite decisions for business operations. Process time will be cut to offer more time for businesses to focus on strategizing. Jobs will not be affected but skills for the accounting profession will change to cater to the AI aspect. AI is going to make accounting services cheaper as well as introduce other accounting services because organizations will have more time and tools in their hands (Smith, 2020).
References
Nilsson, N. J. (2010). The Quest for artificial intelligence: A history of ideas and achievements . Cambridge: Cambridge University Press.
Lewis, T. (2014, December 4). A Brief History of Artificial Intelligence. https://www.livescience.com/49007-history-of-artificial-intelligence.html
Rana, R. (2018, November 9). How Artificial Intelligence Will Impact the Accounting Industry? https://www.acecloudhosting.com/blog/artificial-intelligence-impact-accounting/
Internet Society. (2017, April 18). Artificial Intelligence & Machine Learning: Policy Paper. https://www.internetsociety.org/resources/doc/2017/artificial-intelligence-and-machine-learning-policy-paper/?gclid=Cj0KCQjw6sHzBRCbARIsAF8FMpXgJta5NMjegxzOn5MqEnfI-256SJS7xT4raKZMhpvjPLEKCqSdCfIaAowVEALw_wcB
Snyder, A. (2017, December 15). Executives say AI will change business, but aren't doing much about it.
https://www.axios.com/executives-say-ai-will-change-business-but-arent-doing-much-about-it-1513305300-e041775d-a56b-4db7-be1f-47d69b060427.html
Griffin, O. (2019). How artificial intelligence will impact accounting.
https://www.icaew.com/technical/technology/artificial-intelligence/artificial-intelligence-articles/how-artificial-intelligence-will-impact-accounting
Smith, S. (2020). Blockchain, Artificial Intelligence and Financial Services . Springer International Publishing.
