Why choose us?

We understand the dilemma that you are currently in of whether or not to place your trust on us. Allow us to show you how we can offer you the best and cheap essay writing service and essay review service.

Artificial intelligence on business

Artificial intelligence on business

Referencing style- harvard and intext referncing,all scholar articles 1000 -1200 words,I want HD so please

maintain the quality, use scholar articles from my uni library

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 21 st 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 about what to expect and deal with. Broadly, the AI areas are categorized into
sixteen classes. These are 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 has extensive
documentation. Researchers have done extensive research on axioms’ development which gives
complete axiomization 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 relates.
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 relate to
human-machine interaction based on the 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 in voices, 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
Li and Li (2009) were keen to note that the other challenge arises when efforts are being
made to imitate the 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 promote efficiency. This is based on
the fact that the major of AI is developing automated valuable solutions to 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 barley 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 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, there applications in areas such as B2B e-commerce and web intelligence
applications, services management in the business markets, and creativity, innovation, and
produce development. Li and Li (2010) was 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 of dealing
with the discrete variables, this method also process 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.


Reference List

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

All Rights Reserved, scholarpapers.com
Disclaimer: You will use the product (paper) for legal purposes only and you are not authorized to plagiarize. In addition, neither our website nor any of its affiliates and/or partners shall be liable for any unethical, inappropriate, illegal, or otherwise wrongful use of the Products and/or other written material received from the Website. This includes plagiarism, lawsuits, poor grading, expulsion, academic probation, loss of scholarships / awards / grants/ prizes / titles / positions, failure, suspension, or any other disciplinary or legal actions. Purchasers of Products from the Website are solely responsible for any and all disciplinary actions arising from the improper, unethical, and/or illegal use of such Products.