AI Applications in Business: Expert Systems to Neural Networks

Introduction to AI in Business

Artificial intelligence (AI) represents the branch of computer science concerned with creating machines capable of simulating human intelligence. Intelligent and optimal approaches to problem-solving are needed today in all sectors, regardless of whether the issue is straightforward or complex. Developers and research scholars are constantly attempting to create increasingly more intelligent and efficient software and machines. This is where AI plays a central role — in the development of optimal and efficient search algorithm solutions and programs (Tabassum & Mathew, 2014).

Expert Systems

Individuals are valuable in the business domain because they carry out important business-related tasks. Many business tasks require expertise that is typically stored in an individual's mind — and, in many cases, that is the only place within a company where such knowledge may be obtained. AI is able to offer organizations expert systems that have the ability to capture expertise, thereby enabling its use by individuals who lack it. These expert systems — also known as knowledge-based systems — may be employed for learning problem-solving techniques or for directly solving a problem. They are AI systems that apply reasoning capabilities to arrive at a conclusion, and are an excellent tool for both prescriptive and diagnostic problems.

Prescriptive problems cover issues that require a response to the question of what must be done, corresponding to the selection stage of decision-making. Diagnostic problems, by contrast, demand a response to the question of what is wrong, corresponding to the intelligence stage of decision-making. Expert systems are generally created for a specific domain or application area (Chapter Four Outline, n.d.). Expert systems may be applied in multiple business domains, such as:

Decision support systems (DSSs) at times incorporate expert systems; however, the two are fundamentally different. DSSs are highly interactive and flexible information technology systems aimed at supporting decision-making in unstructured problem situations. A DSS represents an association between specialized IT support and the individuals making decisions. IT brings with it speed, advanced processing capabilities, and vast quantities of information, helping to create valuable inputs for decision-making (Chapter Four Outline, n.d.).

Expert systems employ IT for capturing and applying human expertise. In cases involving problems with well-defined techniques and rules, expert systems prove highly effective and are capable of offering great benefits to an organization. Specific advantages include:

Users may encounter significant challenges when creating and utilizing expert systems:

Neural Networks

Neural networks — commonly referred to as ANNs, or artificial neural networks — are AI systems capable of finding and differentiating patterns. The human brain is trained to consider numerous combined factors for recognizing and differentiating objects, and neural networks are designed similarly. They are capable of learning by example as well as adapting to new ideas and knowledge. They are commonly employed in speech and visual pattern recognition systems, and they prove valuable in a variety of situations.

In the business arena, neural networks are particularly popular in the areas of securities trading, real estate assessment, fraud detection, target marketing, and loan application evaluation. They may also be used to control machinery, detect malfunctioning equipment, and adjust temperature settings. The common element across all of these use cases is pattern recognition. These processes require classification and identification, which can subsequently help predict outcomes. Frequently, a neural network is described as a predictive system, owing to its capability of identifying patterns in enormous quantities of data. Key benefits include (Chapter Four Outline, n.d.; Khosrow-Pour, 2015):

The greatest challenge with neural networks, until recently, has been that their hidden layers remain genuinely "hidden." In simple terms, it has been difficult to understand how neural networks learn and how their neurons interact. More recent neural networks no longer conceal their middle layers. Using such systems, individuals can adjust connections or weights manually, thus gaining greater control and flexibility (Chapter Four Outline, n.d.).

Conclusion: AI and the Future of Business Forecasting

Consumer demand drives the modern business world. Unfortunately, demand patterns fluctuate significantly from one period to another, meaning that developing accurate forecasts can prove to be a huge challenge. Forecasting — the process of estimating future events — is vital to every aspect of management. Its aims are to reduce uncertainty and to provide benchmarks against which actual performance can be monitored.

Emergent AI techniques and information technologies are being utilized to improve forecast accuracy, thereby contributing positively to bottom-line improvement. AI aims at mimicking the thought processes of the human brain, including optimization and reasoning. The overall AI systems market is witnessing rapid growth, a trend expected to continue into the future. One of the primary purposes of artificial intelligence is to aid in organizing and supplying information for management decision-making, such that overall performance and efficiency improve (Hall, 2008).

References

Bazghandi, A. (2012). Techniques, advantages and problems of agent-based modeling for traffic simulation. Int J Comput Sci, 9(1), 115–119.

Castle, C. J., & Crooks, A. T. (2006). Principles and concepts of agent-based modelling for developing geospatial simulations.

Chapter Four Outline (n.d.). Retrieved 3 April 2016 from http://www.mhlearningsolutions.com/columbia_southern/007138388/ch4.pdf

Hall, O. (2008). Artificial intelligence techniques enhance business forecasts. Graziadio Business Report.

Khosrow-Pour, M. (2015). Encyclopedia of information science and technology.

Tabassum, M., & Mathew, K. (2014). A genetic algorithm analysis towards optimization solutions. International Journal of Digital Information and Wireless Communications (IJDIWC), 4(1), 124–142.