Artificial Intelligence Applications in Business AI or artificial intelligence represents the part of the computer science field that deals with creation of machines, which can simulate human intelligence. Intelligent and optimal approaches to problem-solving are needed today in all sectors, irrespective 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, and here's where AI plays a part, in the development of optimal and efficient search algorithm solutions/programs (Tabassum & Mathew, 2014).

Expert systems

Individuals are valuable in the business domain, as they carry out important business-related tasks. A number of business tasks necessitate expertise, which is typically found in an individual's brain; also, this is the lone place in a company where it may be obtained. AI is able to offer organizations expert systems having the ability to capture expertise, thereby enabling its utilization by individuals who lack expertise. These expert systems (known also as knowledge-based systems) may be employed for learning problem-solving techniques or directly solving a problem, and are AI systems that apply reasoning capabilities for arriving at a conclusion. They are an excellent tool for prescriptive and diagnostic problems. The former category of problems covers issues, which require a response to the situation of what must be done; it corresponds to decision-making's selection stage. Meanwhile, the latter category of problems demands for a response to the issue of what's wrong; it corresponds to decision-making's intelligence stage. Expert systems are generally created for some specific domain or application area (Chapter Four Outline, n.d). Expert systems may be applied in the multiple business domains, such as:

1. Accounting -- to aid with auditing, management consulting, training, and tax planning.

1. Financial management -- for identifying accounts likely to turn delinquent, in banks' loan departments.

1. Human resource (HR) management -- for assisting personnel managers in determining whether or not they are complying with the wide range of national/federal employment regulations.

1. Production -- for guiding the manufacturing of all kinds of products (e.g. airplane parts).

Decision support systems (DSSs), at times, incorporate expert systems; however, the latter basically differs from DSSs. DSSs represent highly interactive and flexible information technology systems aimed at supporting decision-making in case of unstructured problems. A DSS represents an association between specialized IT support and the individuals making a decision. IT brings with it speed, advanced processing capabilities, and vast quantities of information, for helping create valuable information for decision-making (Chapter Four Outline, n.d).

Advantages

Expert systems employ IT for capturing and applying human expertise. In case of problems having well-defined techniques and rules, expert systems will prove highly effectual and are capable of offering great benefits to an organization. Expert systems are capable of:

Handling massive quantities of information

Reducing errors

Aggregating data from multiple sources

Improving customer service

Providing decision-making consistency

Providing new information

Reducing cost

Decreasing personnel time devoted to tasks (Chapter Four Outline, n.d)

Disadvantages

Users may run into issues when creating and utilizing expert systems. User challenges may include:

1. Transfer of expertise on any domain to expert systems may occasionally prove difficult, owing to the fact that domain experts can't invariably explain exactly how they possess certain knowledge and expertise. Frequently, they have no knowledge of their overall reasoning processes. They claim that they simply know, and experience grants them intuition when it comes to solving problems.

1. Even if domain experts were capable of explaining the entire process of reasoning, its automation might not be possible. The process might be overly complex, or overly imprecise or vague, and may be bound by a disproportionate quantity of rules. In employing expert systems, one must bear in mind the fact that they may only be utilized to effectively resolve respective issues for which they were created. They cannot address inconsistency or any new problem situation. Expert systems are incapable of learning from prior experience, and of applying previously-gained expertise to novel issues like the human brain.

1. Expert systems lack judgment or common sense. One early expert system integrated into a fighter aircraft -- F-16 -- enabled its pilot to withdraw its landing gear even when the aircraft whilst on the ground as well as jettison bombs when flying inverted (Chapter Four Outline, n.d).

Neural networks

Neural networks (generally known as ANNs or artificial neural networks) refer to AI systems that can find and differentiate patterns. The human brain is trained in considering numerous combined factors for recognizing and differentiating objects. Neural networks are designed similarly. They are capable of learning by example as well as adapting to novel ideas and knowledge. They are commonly employed in case of speech and visual pattern recognition systems. They prove valuable in various situations. In...

Neural networks may be utilized for the purpose of controlling machinery, detecting malfunctioning machinery, and adjusting temperature settings. The common element in every aforementioned situation is pattern recognition. All these processes necessitate classification and identification, which can subsequently help predict any outcome or finding. Frequently, a neural network is labeled a predictive system, owing to its capability of seeing patterns in enormous quantities of information. It has numerous benefits (Chapter Four Outline, n.d; Khosrow-Pour, 2015), some of which include:
Learning and adjustment to novel circumstances by itself.

Lending itself to immense parallel processing.

Functioning without well-structured or complete information.

Handling huge quantities of information having numerous dependent variables.

Analyzing nonlinear relationships within data (neural networks are also considered fancy systems for performing regression analysis) (Chapter Four Outline, n.d).

The greatest challenge, up to now, with regard to neural networks is that its hidden layers, in fact, remain "hidden." To put it simply, one cannot understand how neural networks learn and how their neurons interact. Latest neural networks do not hide their middle layers any longer. Using such systems, individuals can adjust connections or weights manually, thus gaining more control and flexibility (Chapter Four Outline, n.d).

Genetic Algorithms

Genetic algorithms refer to AI systems that mirror the survival-of-the-fittest evolutionary process for generating increasingly improved solutions to any given issue. That is, genetic algorithms are optimizing systems that find a blend of inputs imparting best outputs. They are most effective with decision-making settings wherein thousands, or even millions, of potential solutions exist. These systems can identify and appraise solutions intelligently, getting through countless possibilities more meticulously and quicker than any human ever can (Chapter Four Outline, n.d).

Advantages

As is expected, a business is faced with decision-making scenarios for all kinds of problems such as computer graphics, engineering design, game playing strategies, and, in fact, everything requiring optimization techniques.

A genetic algorithm may be employed by business leaders for aiding them in deciding the combination of ventures their organization must invest in, considering complex tax considerations.

It may be employed by investment firms for facilitating trading decisions and choices (Chapter Four Outline, n.d)

A genetic algorithm will prove effective for such problems because of its application of the techniques of selection, mutation, and crossover for exploring myriad solutions and their respective worth (Chapter Four Outline, n.d).

The key disadvantage of genetic algorithms is: fine tuning every parameter, such as mutation rate, crossover parameters, fitness selection/normalization parameters, elitism percentage, etc., as typically occurs through trial and error. Moreover, it requires a fairly large population and numerous generations, prior to generating sound results. Further, with heavy simulation, users typically have to wait for days to attain a solution to their problem (Chapter Four Outline, n.d).

Agent-based Technologies

ABM or agent-based modeling is one means of achieving human organization simulation through the use of several intelligent agents, all following a simple collection of rules, and capable of adapting to evolving conditions. ABM systems are currently being employed for modeling fluctuations in the stock market, predicting escape routes sought by individuals in burning apartment/office buildings, anticipating how changing market conditions impact supply chains, and estimating interest rates' impacts on customers with varying kinds of debt (Chapter Four Outline, n.d).

Advantages

ABM has the following advantages over other techniques of modeling: (i) It can capture emerging phenomena; (ii) it offers natural system descriptions; (iii) it is a time- and cost- efficient approach; and (iv) it is flexible. However, clearly, ABM's capacity of handling emergent phenomena drives its remaining advantages (Castle & Crooks, 2006; Bazghandi, 2012).

Disadvantages

The enthusiasm linked to ABM adoption in geospatial modeling gets curtailed by a few limitations. One issue, though common to every modeling method, is connected with the model's purpose; models are only as beneficial as the object they are created for. Character of the modeled system is a second consideration. For instance, a system centered on humanity will comprise of agents characterized by potentially irrational conduct, complex psychology, and subjective choices. The above factors are hard to calibrate, quantify, and even justify, at times, and this complicates model development and application, in addition to simulation output interpretation (Castle & Crooks, 2006).

Conclusion

Consumer demand drives the modern business world. Unfortunately, demand patterns fluctuate significantly from one period to another. Hence, developing accurate forecasts can prove to be a huge challenge. Forecasting refers to a process of guessing at future events; it is vital to every management…