The Benefits of Artificial Intelligence(AI) for Business Management - Essay Sample

It is improbable to list comparisons between human intelligence against artificial intelligence. Both have differentiated merits, with the latter lacking breadth, generality, and flexibility, typical for the former. However, AI still proves influential in management from its capability to codify, capture, and extend organizational knowledge. There are no specific means of using AI in business, but its application is subject to the users' needs ascertained (Livio & Hodhod, 2018). The adaptive nature of AI has several applications in various tasks within an organization and can be applied differently. Such use enumerates the reason why AI has a significant buzz about its use. The technological advancement is substantial in business management in various means and can optimally prevent businesses' dissolution from preventable reasons.

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One influential component of AI that has significant potential to impact business management is expert systems. The term identifies computer systems with the capacity to model the decision-making capabilities of human experts. The expert systems reason through rules instead of conventional procedural code (Tan et al., 2016). The rules are the product of comprehensive bodies of knowledge that the AI collects and retains through tacit knowledge sourced from a limited human expertise domain. In management, expert systems are fundamental in the diagnosis and classification of problems. Expert systems perceive case-based reasoning as a collection of databases that one can refine and expand to represent more comprehensive organization knowledge.

Expert systems also influence business management by evaluating large amounts of data in very little time. The context saves managers time by completing tasks that would take longer or are improbable to achieve with human labor (LI et al., 2019). Moreover, its capacity to analyze significant amounts of data and storing the procedure used makes it most appropriate for handling repetitive tasks (Tan et al., 2016). The management can then focus on other duties so long as there is a clear definition of the parameters included in the data. The expert systems also execute tasks without any errors. The organization's merit is on saving time required to go through and correct data and information within and sent out of the organization.

Expert systems utilize the fuzzy logic software technology in their expression of knowledge as rules with either subjective or approximate values. The fuzzy logic application has been in controlling physical devices but has expanded into decision-making applications (Livio & Hodhod, 2018). Business management recognizes the significance of data as an invaluable resource. AI affects businesses by making the managers better and grater at accomplishing their tasks. One such realm of influence is in enhancing data-driven management (Livio & Hodhod, 2018). The managers spend less time sourcing and finding relevant information to make policies and address the problems. They have much more time to implement the data sourced by the AI to drive the results. The impact of the manager working in collaboration with the AI advances the options for the business.

Both the software and hardware make up the neural network, whose functioning aims to mimic the human brain processes. Notable progress of the neural networks is in their capability to recognize and adopt patterns that are highly difficult for humans to describe (LI et al., 2019). The structures are also capable of learning without programming. Moreover, AI only gets better at executing similar tasks since it has an inbuilt capability to learn and collect what it brings to know. Their most effective use in various industries, including business management, is in discriminating patterns from massive amounts of data. (LI et al., 2019) AI exhausts the opportunities within the data by facilitating and carrying out data-mining processes. AI is also critical for businesses in projecting future trends. The knowledge that the AI accrues from the large volumes of data it executes provides relevant background information that provides insight into the future (Tan at al., 2016). Several organizations rely on similar tactics in the industry through predictive analysis. Business managers could employ such tactics to determine the most practical tactics of engaging the organization's consumer market to remain competitive and relevant.

AI also adopts logic from Darwin's theory of Natural Selection when taking into account genetic algorithms. The algorithms referred to provide computer systems with potential forms of solving issues and challenges through implementing evolutionary processes, including mutation, crossover, and fitness. The computer program starts as chromosomes, similar to those in humans storing genetic information (Sidorova & Rafiee, 2019). The use of genetic algorithms is in the evaluation of multiple variables as potentially optimal solutions for problems. The resolution of the issues also exhausts the AI to eliminate the chances of errors in the information provided. Managers can apply the algorithms in operations involving monitoring systems, product design, and optimization.

AI also incorporates the use of intelligent agents. The term identifies autonomous entities with the capability of directing activities towards the stipulated objective. Their working logic requires requests from users that they accrue from sensors and can fetch data from the internet without authorization or help (Tan at al., 2016). The agents are software programs and can either be learned knowledge basis or built-in to the system. Within an organization, their use revolves around the execution of predictable, repetitive, and specific tasks (Sidorova & Rafiee, 2019). The programming of intelligent agents can also grant them the capability to go through large data sets while looking for detailed or relevant information. They may also execute commands based on the information according to the user's request.

The intelligent agents also help in organizational functioning when dealing with their clients. The information held by the smart agents provides a better and more comprehensive outlook of the customer base, which the management can minimize to workable and executable elements (Sidorova & Rafiee, 2019). The organization could use the information to better tailor its products and service to meet the established customer base's needs. By carefully considering the client needs from specified data sets, intelligent agents also improve operational efficiency. Such a dimension again proves critical for organizations seeking to expand to new regions. The smart agents could scan the relevant information for potential variations of culture and geography that may impact the business' objectives (Sidorova & Rafiee, 2019). Managers can use the same information in restructuring their operations and products to b ideal for the new customer base.

The influences of artificial intelligence in business management are limitless. The impact of the same is varied, and functions are relevant to the needs of the organization. The merits are also subjective to the business's methods to min the most from implementing the AI and its various resources. The general aspects of AI and its integration into various organizational operations make the technology a highly invaluable resource for management.

References

Li, E., Zeng, L., Zhou, Z., & Chen, X. (2019). Edge AI: On-demand accelerating deep neural network inference via edge computing. IEEE Transactions on Wireless Communications, 19(1), 447-457.

Livio, J., & Hodhod, R. (2018). AI Cupper: A fuzzy expert system for sensory evaluation of coffee bean attributes to derive quality scoring. IEEE Transactions on Fuzzy Systems, 26(6), 3418-3427.

Sidorova, A., & Rafiee, D. (2019, January). AI Agency Risks and their Mitigation through Business Process Management: a Conceptual Framework. In Proceedings of the 52nd Hawaii International Conference on System Sciences.

Tan, C. F., Wahidin, L. S., Khalil, S. N., Tamaldin, N., Hu, J., & Rauterberg, G. W. M. (2016). The application of expert system: A review of research and applications. ARPN Journal of Engineering and Applied Sciences, 11(4), 2448-2453.