White Paper to External Audience: Business Application for Artificial Intelligence

White Paper to External Audience: Business Application for Artificial Intelligence

Executive Summary

The primary purpose of this white paper is to explore and expand the business application of artificial intelligence.  It provides a comprehensive analysis of the various form of artificial intelligence in functional areas such as fraud detection, transfers and cross-referencing of data, customer service through chatbots, or telephones, among other uses. The analysis presents the benefits of integrating artificial intelligence in business operations and recommend implementation framework for businesspeople.  The target audience for this paper is mainly business people who are own large, small, and startup companies. The secondary audience is technology experts and developers such as Khosla Ventures, DeepMind, Google Cloud, and many others in the technology industry.

Introduction

Currently, the technology industry is very dynamic. It comes with changes every day that influences several aspects of human life, and business is not an exception. Artificial intelligence is one of the most current technologies in the world (Das, 2013). It involves the development of computer systems that can perform tasks and duties that need human intelligence or ability. Some of the most performed actions with artificial intelligence are speech recognition, language translation, visual perception, and decision making. It entails the development of intelligent computer programs that can understand human intelligence but do not conform to biological methods that are observable. Artificial intelligence has applications in almost all dimensions of human life. However, in this paper, I will stick to its business application, especially for decision making. Today, several industries have adopted the use of Al to increase effectiveness and efficiency in their operations. It has also improved the company’s innovative capabilities, return on investment, opening new market opportunities, and automating business services.

Of interest is the ability of artificial intelligence to help business managers forecast in the future and make the right decisions for business success. Since the development of artificial intelligence, business people have used it in various functions. Auditors and accountants have used artificial intelligence to detect fraud and cases of embezzlement of funds in companies.  Studies show that business companies that have adopted a total use of artificial intelligence have significantly reduced corruption cases. It has also provided auditors and accountants with easy time, efficiency, and accuracy while counterchecking the financial records. Consumer behavior forecasting and product recommendations are effective and productive with the integration of artificial intelligence in businesses. With Al, it is possible to predict consumer behavior and buying patterns. The field of advertisement is not exempted from the application of artificial intelligence. Personalized advertising and marketing messages have been broadly used to increase sales volume for the firm’s products and services. This has significantly improved marketing services bridging the gap between sales representatives and final consumers. Further, artificial intelligence has helped in customer relationship management by enhancing customer services via telephone and chatbots.  All these applications are essential in business; however, most area of interest today the use of artificial intelligence in decision-making processes.

Previous Approaches

In the past, the business has employed analytic-driven decision making. Further, business organizations have used three decision-making models. The rational, administrative, and political models (Morcol, 2006).

• The rational model is based on the logic of the optional choice. In this case, it is believed that the manager would make a decision that maximizes the value of the firm. The decision-maker is faced with several known alternative courses of action and based on his or her managerial characteristics, and the right decision is made. This model is complex for the manager to have to struggle with limited shared resources to maximize organizational value.
• The administrative model focuses on a more realistic decision making with variations. In this model, the managers are believed to have several motivating factors influenced by demand. However, they have little time for decision making hence opt for shortcuts in search of acceptable solutions.
• The political model does not focus on a single subject instead of analyzes several organizational problems that align with personal objectives. This method does not apply or consider operational procedures or routines.

Despite the application of the above models either in a single form or integrated form, business organizations and managers still make economically undesirable decisions. In this paper, I present what is termed as the “Al-decision making model” as an emerging framework for decision making for business organizations.

New Findings

As technology moves with a speed of light, artificial intelligence is driving decision making in the business world today. With immense breakthroughs in areas such as deep learning and machine learning. Through artificial intelligence, machines not only process but also analyze business information in unimaginable ways. Automation of cognitive and physical tasks is possible, and this makes the decision making the process much faster and convenient (Turban, Sharda & Delen, 2010). The integration of computer power and ever-increasing data storage provides a better framework for decision making. Artificial intelligence has combined human intelligence with computer power to enable smarter decision-making. The integration of computer power and human intelligence in decision-making has eliminated huge costs of wrong decisions, human biases, and turned up the rate of the decision making process.

The new approaches decision making approaches in business enhanced by artificial intelligence are in the six essential areas (Vondrák, 2007).

• Marketing decision-making

With customer-driven market complexities, marketing decisions are becoming challenging every day. Understanding customer requirements, tastes, need, and preferences and producing satisfactory products and services requires data-analysis. Al simulation and modeling techniques give reliable and sustentative consumer data that can be used for decision making. With real-time data collection, quantitative and qualitative analysis, and forecasting, Al can predict consumer behavior and help managers make appropriate future decisions.

• Customer Relations Management

Al’s buyer persona modeling has help business organizations to know consumer’s lifetime value. These assist firms manage multiple inputs. Al has been noted to control and efficiently manage different factors simultaneously. Further, artificial intelligence can source and analyze massive data within a few seconds, giving valuable information. One of the most merits of Al is that it is not subject to decision fatigue as human beings. Algorithms have no limitation as human beings, which makes them more reliable and easy to use in a complex decision-making process.

• Recommender system

One of the most current technology in Al is the recommender engine that recommends products and services to consumers. Previously recommender engines were used for music content; however, today, that is used to provide consumer explicit and implicit feedback. This helps reduce bounce rate and craft better hence encouraging customer specificity.

• Problem-solving

Through Al, an expert system has been developed that can help business organizations solve complex problems.  The system can replicate knowledge and reason to provide a solution in certain conditions. The decision making is based on gathered and analysis of massive data, therefore, producing right and swift decisions. Expert systems have mushroomed in the technology and various business industries due to its convenience and accuracy. It relieves human beings of too much thinking and stressful situations. Further, expert systems are more reliable since the decisions are made based on synthesized data from previous instances. References to the previous cases make the outcomes more unique and accurate.

• Opinion mining and Augmented analytics

Al decision-makers have been able to get insight into situations before coming with the final course of action (Risse, 2018). Al has provided invaluable information concerning customers that promotes constant communication between the customers and the company. It has also enabled retailers to forecast demand and supply and respond appropriately. Opinion mining has allowed businesses to know how customers feel and reasons why they think that way. In most cases, one customer’s feelings about a product or company reflect other customer’s feelings. A simple analysis of a customer’s data provides insight into the entire market.

Garter news currently reported that Augmented Analytics would be the next big trend for massive transformation in analytics advancement and sharing. It transforms decision making from only relying on data and recommendations to the individual performance of teams, which increases the competitive edge of business organizations. An integration of both human and artificial intelligence in business decision making has helped many businesses make significant and profitable steps. It has enabled firms to reach both short-term and long-term goals within the set timeline. Al, in all, its aspects means progress, and every business organization needs to implement it for faster economic growth and revenue increase.

Conclusion

In a nutshell, integration of artificial intelligence is recommended for both large and small businesses to increase performance. For businessmen and women, it is essential to recognize that the business world is dynamic. Technology is here with us and undoubtedly imparts notable changes in the business environment. In order to stay competitive and maintain a large market share, objective business people should adopt artificial intelligence for decision making, among other benefits in business.

References

Das, S. (2013). Computational business analytics. Chapman and Hall/CRC.

Morcol, G. (2006). Punctuated equilibrium models in organizational decision making. In Handbook of decision making (pp. 156-173). Routledge.

Risse, M. (2018). Human rights and artificial intelligence.

Turban, E., Sharda, R., & Delen, D. (2010). Decision support and business intelligence systems (required). Google Scholar.

Vondrák, I. (2007). Business process modeling. Frontiers in Artificial Intelligence and Applications, 154, 223.