Why did some previous attempts to use artificial technologies fail?
Some previous attempts to use artificial intelligence technologies fail because many early artificial intelligence applications were just solutions to problems. These early artificial intelligence systems contributed little in improving organizational performance. The systems could therefore not fulfill the expectations of people who first thought that they would relieve managers and professionals of the need to make certain types of decisions.
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People thought that computers would employ automated mechanisms that would analyze data and make sound judgments like configuring complex computer or diagnosing and treating a patient. Although the artificial intelligent has developed tremendously, these expectations could not be realized as soon as it was first thought.
There are certain differences between the modern and ancient artificial intelligence systems. The new systems are easier to create and manage than the older systems. The earlier ones leaned heavily on the expertise of knowledge engineers. The new applications do not require anyone to identify the problems or to initiate the analysis. The new systems are also designed to translate decisions into action quickly, accurately and efficiently.
Currently Saudi Arabia continues to enjoy benefits accrued from improved technology that relates to artificial intelligence. Industry professionals have been able to gain knowledge and understanding of independent forecasts and the level of competency in the business and socio political aspects. The benefits of artificial intelligence are mainly focused on forecasting the future, preparation for the future and putting up necessary measures that include remediation, control and innovative measures. For instance, artificial intelligence can project the future in form of graphical extrapolation. The trend of the business performance can therefore be checked for better performance as it has been done in the Saudi Arabian markets (Business Monitor International 3).
What types of decisions are best suited for automated decision making?
There are particular types of decisions that are best suited for automated decision making. It is important to note that today’s managers still need to be involved in reviewing and confirming decisions and in exceptional cases, in making the actual decisions. This is because even the most automated systems rely on experts and managers to create and maintain rules and monitor the results. The automated decision systems are best suited for decisions that must be made more frequently and rapidly, using information that is available electronically. The decision criteria used in such systems need to be highly structured, and the factors that must be taken into account must be well understood.
Decision making done on a large scale business organization such as large financial institutions cannot just be automated blindly. This is because of the risks involved by automating such decisions. The artificial intelligence need to be of high quality and tested for performance. Artificial intelligence has also gained usage on a large scale in several industries. They include the transport industry whereby the airlines employ these services to optimize seat prizing.
This decision making has also been used in other areas in the industry such as flight scheduling and crew and airport staff scheduling. Harrah’s entertainment, which is the largest casino operator in the world, is a good example of decision making in yield management programs. This operator makes a lot of money by optimizing room rates at its hotels and offering different rates to members of its loyalty programs. Another good example of this application is the DeepGreen created. This internet based system makes credit decisions within minutes by skimming off the customers with the best credit, enabling just eight employees to process up to 400 applications in a day.
What role do humans play in automated decision-making aplications?
There are a number of roles human beings play in automated decision making applications. For instance, knowledge engineers work hand in hand with experts to capture the factual knowledge that they posses. They also build the knowledge base and perform the roles similar to that of a system’s analyst especially in the conventional information systems development (O’Brien and Marakas 54). Human beings are also directly involved in the intelligence system.
For instance, managers are still involved in reviewing and confirming decisions and also, in exceptional cases, in making the actual decisions. Even the most sophisticated automated systems rely on human beings to create and maintain rules and monitor the results.
Some of the challenges faced by managers where automated decision making systems are being used include those that arise from lack of defining limits. The consequences of not defining limits can be huge. For instance, Cisco systems found out that it was relying too heavily on its automated ordering and supply chain systems. The management realized that most of the orders that had been entered on the books were not as firm as they assumed and would never be shipped.
This led to the company writing off more than $2 billion in excess inventory. Also, the artificial intelligent systems rarely manage exceptions. Some situation may arise whereby the computer may have inadequate data on which to make a decision. The solutions to these challenges lay squarely with the artificial intelligent systems and the managers concerned. The managers need to develop processes for managing exceptions. They also need to determine in advance what happens when the computer has too little data on which to make a decision. If this happens in the Saudi Arabian industries, business will continue to do better.
Business Monitor International. Information Technology Reports. Saudi Arabia Information Technology Report. 2011. Web.
O’Brien, James, and Marakas, George. Introduction to Information Systems. Chapter 9: Decision Support Systems. New York: McGraw- Hill, 2010. Print.