How AI Can Help You Optimize Decision Making in Business

Abstract 

The primary objective of this study is to identify the most logical and practical approach to Artificial Intelligence and advanced Decision Support Systems to determine and evaluate markets for prospective products. In addition to finding these markets, Decision Support Systems will be used to predict the success of the markets identified and other products that can be introduced in these markets to make companies more successful in the formulation of product lines and marketing models. These are geared towards enhancing optimization in the business by using computer-based decision support system that is structured in a way that emulates the human reasoning and thinking activities. Artificial Intelligence method has the potential of delivering computer-based support for most of the business activities. 

A lot of literature has come up indicating the means within which Artificial Intelligence can be integrated into a business organization for optimization and decision making support. It is important to note that AI has been in application for decades now and in almost all business units across the world. The AI systems are also present in each individual’s aspect of life following the rapid technological enhancement that has occurred in the last few decades. In business, AI systems are used to run business operations efficiently, and also to guide the business owners on how to improve business performance based on the systems’ output results. However, this traditional way of evaluating AI’s efficiency does not deliver the most optimal means of applying the AI system. It is therefore crucial that better ways of evaluating the AI systems adopted to optimize decision making. This paper looks into the techniques that are currently in application, and how these systems have helped to optimize decision making in business. 

Introduction 

Decision making in a business entity is a complex activity involving different aspects of the business ("How Artificial Intelligence Will Change Decision-Making For Businesses", 2018). In most cases, decision making cannot be based on a single aspect of the business and thus require consideration of the different business aspects. The owners and the stakeholders of the business have to evaluate every aspect of the business before embarking on a venture, including costs, environmental impacts, and regulatory requirements among others. The goal of this is to enhance optimization and ensure the business meets its obligations as well as the core for which it was formed, for example, making profits for the investors. 

Decision making in any business can be very complex and may require the application of sophisticated algorithms that the human mind might not be able to compute. Decisions made by a business organization can be either qualitative or quantitative. In some instances, the decision needs to be made in haste, and thus the human mind might not be able to match the urgency and accuracy. An error in making a hasty decision could result in tougher repercussions for the company, including loss of investment. 

For effective decision making, businesses across the world have embraced Artificial Intelligence to enhance the effectiveness of the decisions that they make. Artificial Intelligence refers to a computerized ability to replicate cognitive human being functions such as decision making. It involves the ability to learn and resolve simple and complex problems ("How Artificial Intelligence Will Change Decision-Making For Businesses", 2018). Artificial Intelligence systems are used to replicate human actions, for example in the development of video games; the players are created in specific and similar characteristics to the real competitors. Artificial Intelligence has gained popularity in almost every aspect of human lives. Businesses make use of artificial intelligence to guide their decision making for optimization purposes. The returns on any Artificial Intelligence investment in any business is measured on matters such as the return on investment, new opportunities arising in the new markets identified and the additional positive returns that a business earns as a result of the business decision guided by the AI system applied. 

The application of Artificial Intelligence in decision making involves the use of Decision Support Systems. Decision Support System refers to an information system that provides support to a business entity in its decision-making activities (Ali, Kwon, Lee, Kim & Kim, 2015). They are used to provide support to the management, business operations and guiding planning activities for the business. It allows the stakeholders to the business to make informed decisions regarding the business activities, including the rapidly changing business environments. Decision Support Systems are also used to guide additional business venture that business is venturing into such as entering into new markets, new product lines and also predicting future business performance based on the current status of activities. DSS also include knowledge-based systems that include an interactive software-based system meant to help the decision makers to compile essential information from a combination of raw data, personal knowledge, documents or business models to help resolve problems and make decisions. 

Following the rapid growth in technology in the past few decades, AI has gained popularity in business decision making. AI is used by almost all business lines and structures to develop models that guide the business operations. This has helped replace the use of inconsistent and incomplete data to make decisions by the business executives with data-based models and simulations that provide a better presentation of the business aspects. According to PWC’s Rao, AI systems have continuously seen limitless outcome modeling breakthrough ("How Artificial Intelligence Will Change Decision-Making For Businesses", 2018). These systems are applied in every aspect of the business and fed on a regular diet of big data. Some of the AI applications in business organizations include; 

• Making marketing decisions. Marketing decisions are quite complex. They require one to know and understand the market needs with regards to the customers’ preferences, and align the products in a way that meets these needs and desires ("How Artificial Intelligence Will Change Decision-Making For Businesses", 2018). AI systems help to understand consumer behavior in order to formulate the best marketing decisions for the business. AI modeling and simulation techniques provide reliable insights into the buyers’ personas to predict consumer behavior. 

• Customer Relationship Management. Integrating AI into Customer’s Relationship Management enables contact management, data recording, lead ranking and analysis ("How Artificial Intelligence Will Change Decision-Making For Businesses", 2018). AI’s buyer persona modeling also provides one with the ability to predict a customer’s lifetime value. 

• Recommendation System. AI systems learn the content preferences of the users and help match these preferences with products formulation to help reduce bounce rate ("How Artificial Intelligence Will Change Decision-Making For Businesses", 2018). 

AI proves to be a promising development in business. The future of any business entity depends on its ability to make decisions that will help in achieving optimality in its operations. As such, the need to ensure that AI systems adopted in any business dire and cannot be underrated (Ali, Kwon, Lee, Kim & Kim, 2015). A business’ competitiveness requires the adoption of quality decisions in its operations. 

Definition of the Topic 

This research looks into ways of optimizing decision making with the help of AI. As stated earlier in the introduction section of this paper, AI has the ability to enhance a business’ decision making to ensure optimization of the business activities (Ali, Kwon, Lee, Kim & Kim, 2015). This includes new ventures that business plans to embark on and also to optimize the existing business operations. Several AI systems have been employed in the past, with some failing to meet the objectives of their parent businesses (Ali, Kwon, Lee, Kim & Kim, 2015). The aim of this research is to identify ways in which businesses can employ AI to improve their decision-making activities. This will be achieved through an analysis of existing literature on Artificial Decision making systems and models. 

Methodology 

The research employs both conceptual and empirical research methods to draw conclusions. Conceptual research refers to the use of existing abstract ideas to develop new concepts. In this research, conceptual methods are used to formulate ideas on the desirable characteristics and solutions to be provided by the AI decision making systems. It is used to describe the phenomenon under study by observing and analyzing existing information on Decision Making Systems and Artificial Intelligence. 

Empirical research is based on scenarios observed and measured to derive knowledge from actual experiences rather than theories. It makes use of existing literature on real experiences to draw conclusions. In this research, the empirical methodology is applied to analyze experiences in the field of Artificial Intelligence and Decision Support Systems to draw viable conclusions. 

According to Hand et al., Artificial Intelligence is used in the analysis of data that is often considered too large to find unsuspected relationships and to summarize the data in novel ways that are easy to understand and useful to the data owner (Doumpos & Grigoroudis, 2013). This is referred to as statistical learning and data mining capability of the artificial intelligence systems. The AI systems’ adopts algorithmic modeling culture in which focus is shifted from data models to the characteristics and predictive performance of learning algorithms. These enhance decision making by simplifying and generating guidelines on the possible trends and results expected from specific actions in the business. A part of the AI architecture referred to as artificial neural networks help in modeling real functions of arbitrary complexity. These ANNs are highly flexible and popular in addressing complex real-world problems including business decision requirements (Doumpos & Grigoroudis, 2013). 

Application 

The use of artificial intelligence in decision making requires the decision makers to take into consideration a few aspects. There are three issues that decision makers need to understand before they handle AI at the strategic level. These are; 

i. AI needs to be aligned to the business goals. A survey by McKinsey involving 3,000 executives indicated that 41% of them were not certain of the benefits of AI. AI can help automate relatively simple tasks that are within the human capability and also dig deeper for more information that is not within the humans' capabilities. For effective use of the AI systems, the executives need to understand the organizational goals and understand the relationship between the AI outcomes the results. Aligning the goals of the organization with the AI’s capabilities helps to ensure that the decision guides provided by the AI are valuable to the organization to avoid making wrong decisions based on misguided AI information. 

ii. The organization requires being data-centric for the effective use of Artificial Intelligence. AI uses data to produce models. Most neural networks typical of AI systems learn to make better decisions with the help of vast amounts of historical data from which they produce models for use within the organization. The organization thus needs to have sufficient data storage facilities within its business IT framework for use by the AI. 

iii. The business organization needs to pursue strategic partnerships. Though AI consumes the organization’s data to produce decisions or outcomes, the data needs to be right. This, therefore, means that for efficient usage of the AI systems, the user needs to have sufficient expertise or information relating to data usage and the type of data available. 

Artificial Intelligence Model 

Earlier research on traditional artificial intelligence models used the outcome to determine the effectiveness of the AI in decision making. Most of the traditional intelligence systems researches sought to maximize return for a given level of risk that generated a bi-objective quadratic optimization model (Doumpos & Grigoroudis, 2013). This ignored the impact of the AI systems as Decision Support Systems. This research makes use of both outcome and process to determine the effectiveness of the AI systems in optimizing business decision making. 

The optimization of artificial intelligence in decision making is determined by how artificial intelligence optimizes business processes. The end result is not the only determinant of optimization but also includes how best that these results are achieved. The model for this research can is presented below: 

Y=f(p,o) 

Where Y=optimization problem 

P= process being used 

o= output of the business activity being placed under the AI support 

To assess this, the research seeks at assessing the effectiveness of one of the modern artificial intelligence systems in the application for business purposes in the name of neural networks. Neural networks are used to create models capable of transmitting information from the cause to the effect. Regression analysis is applied to determine the effectiveness of this system under study. The model for the regression analysis will be 

Y= a +b1p + b2c 

An artificial neural network replicates the biological neural network in terms of its capabilities in comparison to the brain’s capabilities (Castrounis, 2018). It is considered to be an intelligent computer-based artificial version of the biological network of neurons. Architecturally, it is modeled using layers of computational units that are able to receive inputs and generate results based on the messages being passed along (Castrounis, 2018). A simple artificial neural network model has three layers; the input layer, the hidden layer, and the output layer. These layers contain one or more neurons. As the business decision becomes more complex, the neural network model becomes more complex with increased abstraction and problem-solving capabilities (Castrounis, 2018). More hidden layers are included in the complex artificial neural network of neurons. Below is a diagrammatical representation of a complex model of an artificial neural network: 

I nputs Layer Hidden Layer Output Layer 

This diagram demonstrates the connection between the desired output and the factors that lead to achieving this output (Castrounis, 2018). The inputs layer represents the business constraints or activities, the hidden layer represents the processes while the output layer represents the expected results of the operations. 

Experimental Results 

The results of the study indicate using artificial intelligence to optimize business operations has a huge impact on the decision making optimization goal. The type of neural network under study is referred to as Multi-Layer Perceptron (MLP), a feed-forward neural network model where input variables are feed-forwarded until they become the effect. The MLP, therefore, estimated the relationship between sets of input data (cause or process) and a set of appropriate output (effect) (Marwala, 2014). It is based on a standard linear perceptron and makes use of three or more layers of neurons with non-linear activation functions that are more powerful than the perceptron. This is because it has the ability to distinguish decision making data that is not linearly separable, or separable using a hyperplane. 

The results of the study show that the neural network helps to rank a set of alternatives as well as learning a relational multi-criteria model based on pair-wise comparisons among the alternatives (Marwala, 2014). The neural network was capable of modeling and processing nonlinear relationships existing between inputs and outputs together. The system learned the data observed including the process aspects such as costs and links this to the desired output results with the help of a learning algorithm. 

With regard to optimizing decision making, neural networks in artificial intelligence help to make optimal decisions in any business. This is because the different layers available provide different outputs and results based on different processes and inputs applied (Marwala, 2014). Thus, the decision-making function of the business can compare the different outcomes based on the results of the neural network AI. This will help to ensure that decision making is optimized by choosing the most efficient alternative that produces the most efficient and effective results. 

Future Study Recommendations 

The field of artificial intelligence is quite broad, and so is decision making. Artificial Intelligence systems help to improve decision making by providing easier and faster means of assessing situations as compared to relying on the human brain to assess and compare the different alternatives available to the business entity (Marwala, 2014). This research seeks to find means within which artificial intelligence can be used to optimize decision making. Prior researches demonstrate that tradition artificial intelligence systems placed much of their focus on the output of the artificial intelligence systems. In this research, traditional artificial intelligence systems have been evaluated to determine ways of improving decision making. More research is required on the most efficient AI systems that are much faster and convenient for use in any business. This research presents a multi-criteria artificial intelligence system as a crucial tool in optimizing decision making. To complement this finding, more research needs to be done on how well business units can use artificial intelligence to make communication and connectivity between the different business functions much faster and convenient. 

Conclusion 

Decision making is essential in each business. The type of decisions that the executives in any business make determine how well that business entity is going to perform into the future, and also its profitability (Doumpos & Grigoroudis, 2013). Relying on the human mind to generate the different possibilities might not lead to a more efficient decision for the business. It is for this reason that artificial intelligence is applied to make it easier to make decisions in the business. 

Application of artificial intelligence has resulted in improved decision making within the different business entities across the world. However, most of the AI systems that were applied in the past were centered towards output delivery to guide the decision-making process (Doumpos & Grigoroudis, 2013). This ignored the process that takes place within the business and thus resulting in decisions that could have been better if more considerations were made on the AI system application. In other words, the traditional AI systems do not fully optimize decision making. This research shows that modern AI systems have been improved to consider the processes aspect of the business before delivering output that will be used for decision making. One of these systems is referred to as neural networks which apply different layers that are interconnected and produce different sets of alternatives that are at the business exposure. The different layers connect the output to the inputs. It provides a different set of input alternatives that produce different sets of outputs. This means that a more multi-criteria artificial intelligence system will be essential to guide the decision making organ of the business entity, thus optimizing decision making (Doumpos & Grigoroudis, 2013). The decision making organ of the business can, therefore, make decisions based on the most optimal set of alternatives based on the output provided by the AI system. 

References 

Ali, M., Kwon, Y., Lee, C., Kim, J., & Kim, Y. (2015). Current Approaches in Applied Artificial Intelligence. Cham: Springer International Publishing. 

Castrounis, A. (2018). Artificial Intelligence, Deep Learning, and Neural Networks, Explained. Retrieved from https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html 

Doumpos, M., & Grigoroudis, E. (2013). Multicriteria decision aid and artificial intelligence. West Sussex, U.K.: Wiley. 

How Artificial Intelligence Will Change Decision-Making For Businesses. (2018). Retrieved from https://becominghuman.ai/how-artificial-intelligence-will-change-decision-making-for-businesses-96d47cde98df 

The Use of Artificial Intelligence in Decision-Making. (2018). Retrieved from https://hortonworks.com/blog/three-things-ceos-should-know-about-the-use-of-artificial-intelligence-in-decision-making/