Artificial Intelligence on Modern Day Business

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Artificial Intelligence on Modern Day Business
Artificial Intelligence on Modern Day Business

Referencing style- Harvard and intext referencing, all scholar articles 1000 -1200 words, I want HD so please maintain the quality, use scholar articles from my uni library

Artificial Intelligence on Modern Day Business Sample Answer

Artificial intelligence on business

According to Chan and Ip (2011), artificial intelligence research during the previous two decades has fundamentally improved performance in the service and manufacturing systems. The need to understand more about artificial intelligence cannot be ignored, considering its essence in the modern day businesses. In this literature review, there is a report of the current standing on artificial intelligence using a concise, elegantly distilled, and integrated manner so that the field’s experiences can be highlighted. Particularly, the field’s recent developments in business are reviewed broadly, as well as the present applications. Such a move is necessary for the sake of new entrants in this field. Moreover, such a literature review can help in reminding the experienced researchers on the known and prevalent issues.

Li and Li (2010) indicated that in this 21st century, AI (artificial intelligence) has become a very significant area in nearly all the fields, including medicine, accounting, business, education, science, engineering, law, and stock market among others. AI poses some challenges in relation to the growing and rising nature of information technology globally. Wierenga (2010) indicated the need for research in the business area so that the new entrants can have a basic understanding of what to expect and deal with. Broadly, the AI areas are categorized into sixteen classes. These are a theory of computation, constraint satisfaction, theorem proving, neural networks, natural language understanding, machine learning, knowledge representation, systems, genetic algorithms, expert systems, distributed AI, data mining, belief revision, artificial life, programming, and reasoning.

Wierenga (2010) asserts that the practice and theory of AI reasoning have extensive documentation. Researchers have done extensive research on axioms’ development which gives complete axiomatization and sound for the reasoning’s logic. The reasoning concept in AI has been discussed using some general areas like reasoning about the minimal belief, complexity of reasoning, sampling algorithm, efficient methods, fuzzy description logics, independence, diagnosis, fusion, and filtering among others.

When creating the intelligence system, a number of challenges can be faced. Chan and Ip (2011) noted that when trying to create the AI, there ought to be some condition on the likely reactions based on the anticipated stimuli. Arguably, this is not intelligence as imitating the true intelligence needs a detailed comprehension of the manner in which the output and input relate. At the same time, there should be huge interdisciplinary efforts among a majority of the subfields, together with linguistics and psychology.

Wierenga (2010) added that when it comes to AI, many of the complications related to human-machine interaction based on human interaction complexity. Many of the communications that take place between people might never be coded facts which machines can simply recite. In addition to this, Moreno (2009) indicated that there are very many ways through which humans are able to communicate with others, and which affects communication. These were noted to be body language, emotions, intonations invoices, popular culture facts, responses to different stimuli, as well as slang and all these have an effect on the manner in which people may communicate. This simply means that AI has not been that efficient in businesses. The non-verbal communications are difficult to model in machines, and more so when the principal common sense model has not been installed for making inferences. Lin, Wang, and Chin (2009) added that Fuzzy Logic that is modeled after people’s excellent ability to make approximations in the absence of real values also poses immense complications. By the mere definition, computations need numbers as opposed to concepts and words. When using AI in business, challenges are also experienced when human commonsense and intuition is being imitated. Humans take a lot of background information for granted, and replicating on machines is equally difficult.

Li and Li (2009) were keen to note that the other challenge arises when efforts are being made to imitate human emotions. This is based on how subjective and complex the emotions can be, particularly when expressing multiple emotions. When the Machine Learning approach is being used, the system can process conversations that humans have labeled. However, the labels are never consistent all the times. The use of AI in businesses also poses a challenge with image processing. Various locations from photos are hard to recognize on the internet based on the images’ variability. Modeling the world using internet photos is equally hard since the average photo on the internet varies a lot.

Martínez-López and Casillas (2009) noted that despite the challenges being experienced in applying AI in business, it is still being applied a lot in promoting efficiency. This is based on the fact that the major of AI is developing automated valuable solutions to a problem which would need the intelligence intervention that humans normally engage in. Chan and Ip (2011) indicated that in the business contexts, there are challenges to be addressed and which need this particular characteristic. That is, human analysis and judgment to solve and assess the problems for success to be granted. Many times, these decisional situations relate to firms’ strategic issues frequently, where challenges are barely well structured. Li and Li (2010) noted the need to apply and develop ad-hoc intelligent systems, based on their particular strengths, to provide valuable information and process data. This can use either the knowledge- or data-driven approach. Moreover, it can be of great essence to managers when they are making decisions.

Regardless of the potentialities of contributing towards strategic intelligence (competitive intelligence, knowledge management, and business intelligence), AI as a research theme has experienced scarce attention in the relevant journal articles, and more so those focusing on management and business issues. A basic Scopus search (keywords, abstract, and article title) reveals that the whole number of papers which have been published on AI or intelligent systems and business in the management or business- focused journals are less than one hundred and fifty. As such, Casillas, Martínez-López and Corchado (2012) indicated the need of promoting, publishing and stimulating high-quality contributions on the applied-intelligent systems so that management of the marketing issues can be managed among businesses.

According to Casillas, Martínez-López and Corchado (2012, the interesting and particular areas where AI can be applied within a business or industrial marketing frameworks are many. Some of these include targeting and segmenting business markets, managing the relationships between the customer, B2B pricing strategies and B2B communications decisions. At the same time, it can be applied in marketing channel relationships, knowledge management, and business intelligence, managing personal selling, and supply chain management and organizational buying processes. Finally, their applications in areas such as B2B e-commerce and web intelligence applications, services management in the business markets, and creativity, innovation, and product development. Li and Li (2010) were keen to note that some of the areas have been researched on widely, but there is still a lot that needs to be done in the future research.

Casillas and Martínez-López (2010) propose an original and new method for optimizing the line of products in a company that is dependent on the continuous and discrete attributed. This requires the Particle Swarm Optimization algorithm should be designed for dealing with such attributes in the B2B context. The newness associated with this method has a benefit in that dissimilar to a majority of the product line optimization approaches that are normally developed for companies oriented to the consumer markets, the suggested methods have been designed considering the typical industrial settings. At the same time, apart from the ability to deal with the discrete variables, this method also processes variables that are set on the continuous range. As such, manufacturers are able to increase customization degree, which added value to the product lines.

In a nutshell, there is a lot that the AI is promoting in the business field, which managers can learn from. However, there are many areas that can be studied on for more productivity in the field.

Artificial Intelligence on Modern Day Business Reference List

Casillas, J., & Martínez-López, F. J. (Eds.). (2010). Marketing intelligent systems using soft computing: Managerial and research applications. Springer. Management intelligent systems.

Casillas, J., Martínez-López, F. J., & Corchado, J. M. (Eds.). (2012). First international symposium, vol. 171 of Advances in intelligent systems and computing. Springer.

Chan, S. L., & Ip, W. H. (2011). A dynamic decision support system to predict the value of the customer for new product development. Decision Support Systems, 52, 178–188.

Li, S., & Li, J. Z. (2009). Hybridizing human judgment, AHP, simulation and a fuzzy expert system for strategy formulation under uncertainty. Expert Systems with Applications, 36, 5557 – 5564.

Li, S., & Li, J. Z. (2010). Agents International: Integration of multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making. Expert Systems with Applications, 37, 2580– 2587.

Lin, P. -C., Wang, J., & Chin, S. -S. (2009). Dynamic optimization of price, warranty length, and production rate. International Journal of Systems Science, 40, 411– 420.

Martínez-López, F. J., & Casillas, J. (2009). Marketing intelligent systems for consumer behavior modeling by a descriptive induction approach based on genetic fuzzy systems. Industrial Marketing Management, 38 (7), 714– 731.

Moreno, J. (2009). Trading strategies modeling in Colombian power market using artificial intelligence techniques. Energy Policy, 37, 836 – 843.

Wierenga, B. (2010). Marketing and artificial intelligence: Great opportunities, reluctant partners. In J. Casillas, & F. J. Martínez-López (Eds.), Marketing intelligent systems using soft computing: Managerial and research applications (pp. 1–8). Springer.

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