bhusal2Prepared byDeepak BhusalCWID50259419To P

bhusal2
Prepared by
Deepak Bhusal
CWID:50259419
To Professor: Dr. R. Daniel Creider
Table of Contents
Abstract 3
Introduction 4
Literature Review 5
AI for Justice 6
AI in Medical Teaching 8
Artificial Intelligence in human resource management 9
AI in Marketing 10
Artificial Intelligence in Real Estate 13
Real Estate Agent Selection 14
Artificial Intelligence in CRM 16
Artificial Intelligence in Banking 18
AI based Chatbots in Financial Institutions 19
Customization of Products 19
References 24
Artificial Intelligence: Formalizing Human CapabilitiesAbstract
Artificial Intelligence cannot replace three human abilities, in

which human beings present an insurmountable advantage
today, and they are empathy, leadership, and creativity. AI can
quickly take over essential verbal and visual communication
services, such as digital assistant-based customer service.
However, our ability to empathize with the client and to carry
out non-verbal communication based on emotions gives us an
advantage that Artificial Intelligence can never replace. These
qualities can make the difference between a misunderstood and
dissatisfied customer versus an understood and loyal customer.
Gajane & Pechenizkiy (2017) stated that it is undeniable that AI
will replace workers in essential economic-financial
management, logistics, materials, human resources, and
projects. Still, people have more advanced management
capabilities that AI cannot return. The following two skills play
a crucial role:
First is the ability to manage the growth of human groups. This
is the ability to help members of the organization develop their
skills and grow professionally through our innate leadership
ability to set goals, motivate, lead by example, evaluate,
delegate, and transmit experience.
Secondly, there is the ability to carry out the organization
members' recovery management when they suffer problems
derived from interpersonal relationships or other emotional
reasons. It is based on the skills of understanding, counseling,
care, and protection.
Yampolskiy (2019) found that AI can never replace the vision,
invention, and original proposal of innovative and disruptive
designs, not only applied to the individual as a genius but also
the ability to carry out collective intelligence management
focused on innovation, facilitating the appearance of new
knowledge and wisdom. Besides, even more, difficult it will be
able to replace the ability to implement new ideas in the
organization, communicating attractively, persuading, and
making the organization move smoothly to implement
innovative ideas.
Keywords

Artificial Intelligence, Marketing, Human Resource
Management, Medical Sciences, Nursing, Introduction
The possibility of thought in machines is a concern that has
been raised for a long time; science fiction, as well as
engineering and philosophy, have sought to provide an answer
to the question "Can machines think?" Famous exponents of
both affirmative answers, given by Turing or Kurzweil, and
negative responses can be found in the bibliography, Searle or
Penrose being notable examples. Although each of these
represents a different position, there is a common characteristic
to the other proposals. In all of them, "thinking" is defined by
the purely human experience of doing it. By presenting the
Game of Imitation as a criterion for determining whether
machines think, one is declaring that they will only do so when
they can carry out behavior’s characteristic of the human way of
thinking.
Montani & Striani (2019) understand that by denying that a
machine can think because, if we were in its position, there
would be characteristics of our intelligence -such as semantic
content and demonstration of mathematical truths so we would
not have access, we are requesting that for a machine to "think,"
it can carry out all the functions of human thought. The
proceeding is natural since our experience as thinking subjects
is the one, we can most easily access. However, this seems to
bring with it an unfair judgment for other possible mental
forms.
Suppose that our first-person experience was equally important
in establishing what it means, for example, "to breathe." In that
case, we would have a fundamental component of the definition
of the possession of lungs and a structure similar to the human
one. Under this definition, fish or cells would never be
considered exponents of subjects that breathe, leaving aside
cellular or gill respiration phenomena. In the same way that we
would leave out many other breathing subjects by exploring
how the phenomenon presents itself exclusively in humans, I
also believe that it is possible to leave out other minds by

exploring the concept of thinking solely from our perspective.
Suppose it is considered that it is essential to understand forms
of thought that go beyond the human. In that case, it will be
necessary to build a tool that allows the configuration of the
concept so that there is more information that configures it and
our experience.
Montani & Striani (2019) understand that what is sought when
using refractive equilibrium to define what it means to think is
to change the weight that human experience has for definition.
The Studies in artificial intelligence indicate that we take our
expertise from subjects considering as the only criterion to
define the characteristics that anything must meet to be
classified as thinking. By producing a definition through the
refractive equilibrium of According to the monograph's body's
proposals, it will seek that now this experience is just one more
tool for characterization. Our experience human will guide the
report, but the way we extract information from it be careful to
give other minds a chance to be taken into account despite their
material realization. This is not to say that the refractive
equilibrium process has to grant due to the existence of minds
other than human; in fact, a result of the process may be that,
according to the information available at the time, only we have
examples of thought in us. However, they would find the
difference in why we exclude other minds; currentl y seems to be
that the main reason is of perspective. Through balance, it will
find representative characteristics (and investigable in more
subjects than ourselves) provide a definition. In this way, we
would go from asking ourselves, "Can other objects think as
humans do?” a "How to do the other processes of thought?"
without assuming that they think, since the equilibrium process
will allow that the investigation of the how modifies what the
concept meansLiterature Review
Before resolving any doubts about the capabilities of a machine,
it would seem natural first to determine the limits of what you
are referring to by "machine." In the traditional problem of
artificial intelligence, research is found focused on the study of

digital computers presented by this definition proposed by
Turing is given in an open enough way to include within them a
great variety of later developments in computational
engineering, this perspective will carry some problems that it
will expose later. The historical moment in which CMI is
written is one in which computer personnel did not yet have a
significant presence in the world. Most machines computational
systems belonged to research institutes and were out of the
reach of the public generally.
Bogachov, et al, (2020) stated that to imply what a digital
computer, a digital computer, understands, Turing defines them
by an analogy with a human-machine (the processes that he
could carry out a person under a closed system of instructions);
from which the capabilities and limits the machine will
encounter. This is an application of those strict definition
presented in his famous "Computable Numbers with an
Application in which devices are presented as a tool to solve a
problem of mathematics. Digital computers will be composed of
three main parts (i) storage, (ii) execution unit, and (iii) control,
which make up a functional object. The storage represents the
space in which the data is presented. The information to be
manipulated and the results are there; the execution unit is in
charge of carrying out the processes, and the control is the fixed
rules, presented as tables that the system must follow.AI for
Justice
The first proposition or premise states that all law students are
shortsighted. Therefore, all law students are included in the
universe of people who have myopia. The second proposition
informs us that some myopic people are intolerant to contact
lenses. That is, within the universe of myopic people, a group
does not tolerate contact lenses. If these are our two premises,
the conclusion is not correct because those two statements are
compatible with a situation in which the group of myopic people
who do not tolerate contact lenses does not include any of the
law students - all of them myopic, as we know -. If true, the two
premises are compatible with states of the world where all law

students are intolerant of contact lenses, only some law students
are intolerant of contact lenses, or even no law student is
intolerant of contact lenses.
Bogachov, et al, (2020) stated that the alleged conclusion does
not necessarily follow from the premises. It is a false syllogism
or "paralogous." Something that looks like a syllogism but is
not. A subtle trap is committed when the second premise - the
one that contains the subject of the conclusion - is placed first,
while the first premise - the one that includes the predicate of
the decision - is presented in second place. In this way, when
we find ourselves in the first place with a universal type
statement - all law students are ... -the reasoning looks more
like a syllogism. If we rearrange the two premises (some
nearsighted people do not tolerate contact lenses; all law
students are nearsighted; then…), the jump or logical
inconsistency is perhaps more easily detected.
Heer (2019) thought that to see it more clearly, we can try a
graphic representation of the matter. We draw a huge circle that
represents all myopic people in the world. With a smaller
process, we mean all law students in the world. We must
necessarily draw this smaller circle in its entirety within the
myopic circle (because our reasoning starts from the premise
that all law students are myopic). Finally, we must draw a third
circle - which is the one that interests us the most - that
represents all subjects intolerant to contact lenses. Where and
how do we draw this circle? Well, there are a few possibilities.
It must necessarily be drying to some extent with respect to
myopic circle because some myopic have the property of
intolerance to contact lenses. However, fulfilling that
requirement, we have, as I say, several possibilities. I invite the
reader to play set logic for himself, using paper and pencil and
draw all the possible combinations of circles compatible with
the two premises.
Bonacina (2017) mentioned that there is still another way to
deal with the problem: simplify or "formalize" our language.
Thus, we replace the expression "all law students are myopic"

by the following: "all A is B"; the phrase "some myopic are
intolerant to contact lenses," for "some B are C"; and our
conclusion would become "then some A is C". We could use a
more sophisticated notation like "for all x if x is A then x is B"
(∀ x A (x) → B (x)), But the important thing is that we have
replaced some terms that had a specific meaning. Therefore, it
referred to certain sets or classes of entities or realities existing
in the world (law students, myopic, intolerant of contact lenses)
by symbols (A, B, C) with which you can refer to any set of
entities or realities. We could say that we have eliminated all
the "semantics" from our reasoning. We are left only with the
"syntax": with the position of subject or predicate that each of
the terms (A, B, C) occupy in each of the propositions and with
some "quantifiers" that model the scope of these terms (all,
some). Reduced reasoning to its syntax - or in other words, to
its formal structure - a purely ceremonial "calculation" is
possible and comfortable, allowing us to verify the correctness
of the reasoning.
AI in Medical Teaching
The ability of a system to correctly interpret external data, learn
from such data, and use that knowledge to achieve more specific
tasks and goals through flexible adaptation." One of its uses for
decades was the application of game theory to defeat the best
human players.
Kaplan & Haenlein (2019) stated that it is structured from
different areas of knowledge such as computer science, logic,
mathematics, philosophy, and experience to develop
computational models capable of carrying out human activities,
based on two fundamental characteristics: reasoning and
behavior. In teaching, it is summarized from a pedagogical
solution to the problem. It is presented as a discipline that is
responsible for studying and building "intelligent agents," that
is, systems capable of perceiving their environment and acting
on it to achieve the proposed objectives, where each “agent” is

implemented through a function, which establishes a
correspondence between his or her perceptions and their
actions.
Kaplan & Haenlein (2019) stated that Artificial intelligence
gives machines the ability to "reason and learn." Two
capabilities are very useful in clinical diagnosis. For example, a
computer program can analyze the photo of a spot on the skin
and comparing it with its database, establish the probabili ties
that it is a melanoma. Similar applications are being developed
for many other diseases, although for now, AI complements and
strengthens the diagnosis of doctors.
A robotic nurse? It seems that it will be one of the keys to
assisting the elderly and dependent patients in the future. So
far, robotic pets have been developed for therapeutic purposes
to help Alzheimer's patients. Robotic pets stimulate brain
functions in patients by delaying cognitive problems that
improve quality of life and reduce dependency on social
services.Ease the burden on doctors
Wang (2019) found that analysis tests, X-rays, CT scans, data
entry, and other mundane tasks can be performed faster and
more accurately if carried out by robots. Cardiology and
radiology are two examples of disciplines where the amount of
data to analyze can be overwhelming.
Perhaps in the future, simple cases will be left exclusively in
the hands of AI, and human doctors will only deal with the most
complicated ones.Drug development
Wang (2019) found that getting effective new drugs over
clinical trials can take more than a period and cost too much.
Thus, streamlining the process by using AI could transform the
globe. In the modern Ebola calamity, they used an AI-powered
software to examine current drugs that it could reform to fight
the ailment or disease. The program found two medications that
can lower Ebola contagion in one day when such study mostly
takes months or years - a change that maybe saved many lives
or more.Artificial Intelligence in human resource management

They are the virtual assistant's AI, Chabot that respond to
requests of different difficulty. A Chabot is a software tool
designed to help users carry out other tasks by implementing
machine learning and artificial intelligence (AI). As their name
indicates, they can assist us in carrying out internal processes
related to the administrative part of Human Resources; they can
provide information on policies and procedures or filter
curriculum vitae, as we said. These Chabot’s are capable of
learning, so we are working on communicating with them
because we do not know how far their understanding could go,
both intellectual and sensory. We know that they can read
emotions on people's faces, which would bring their level of
understanding closer to ours, and that they can process levels of
information in such a way that they can transform it into
knowledge.
Kolski & Vanderdonckt (2020) stated that the question is at
what level of trust you would place in a "machine" and if you
would leave such human issues as talent management and
professional development in their hands. Can a robot evaluate,
and plan areas related to our performance? It is predicted that
after developing the appropriate algorithms, an algorithm and
therefore a robot could learn from human behavior and its
needs. It could plan solutions to needs related to the human
essence and that is so much needed in organizations, such as
motivation and learning. Many things are said about the future
that is to come. Still, it is difficult, at least for me, to believe
that a robot will be able to intervene in such subtle matters
without the intervention of a person who supervises the process.
Let us look at the paradigm shift that new technologies are
generating and, from there, in the latest models of the
relationship between people that are being established. We can
discover new models of Human Resource Management that are
more effective and empowering. Suppose we start from the
initial consensus on the concept that "organizations achieve
results through the behaviors manifested by the people who
compose them". In that case, an effective people management

model will be one that maximizes their performance and
productivity, guaranteeing the best results that it can expect
from them.
Kormalev, et al, (2018) realized that for this, technology will
allow us to put integral tools at people's service that generate a
collaborative and interrelated context that connects processes
and people, adding value to the business chain and maximizing
results. Let us take an example; a phone has no value on its own
if it has no other phone to communicate with. In this way, two
telephones will double in value once they can contact each
other. However, this valued relationship will be reduced to
exchanging information between the two terminals unless a
third or other terminals can connect simultaneously to the same
conversation. In this way, each telephone that joins the
discussion will add exponential value to it in terms of time and
communication efficiency.
AI in Marketing
The marketing world discovered it a long time ago, and for that
reason, it makes the client/consumer the protagonist of the
experience with the company. The question would be if we were
prepared to create a people management model based on
empowering employees to become the real protagonists of
business activity. The times of control ended since we
discovered that the power exercised over others is inversely
proportional to the trust that is generated in the other.
Acay, Sonenberg & Tidhar (2019) found that the times have
come to empower, to listen to others, to enhance autonomy and
risk, to assume responsibilities and decisions, to favor the
establishment of links and networks, to share knowledge, to
learn from others, to awaken interest in the search for
information, to filter and select the relevant information, to
maximize performance and productivity.
In short, we are in times of transparency and information
available to anyone, in times of collaboration and handing over
the power of action to the real protagonists, people, and talent.

If this is the new paradigm for managing people and human
resources in the future companies, how can technology help us
manage this model?
Perez, et al, (2018) stated that Artificial Intelligence systems
differ, among other things, from traditional Data Warehouse
systems in that the latter generate indicators calculated from the
analysis of data from the source systems (transactional
systems). The former can establish predictive hypotheses based
on the same data, follow up the selected predictions, confirm or
correct these hypotheses with the temporal monitoring of the
evolution of the data and the results. In this way, as time passes
and the system handles a larger and more complete sample of
data, it learns to be more and more efficient in its prediction
models.
The result of the predictions will be more reliable and accurate.
The more inputs are collected within the system and the longer
it takes to establish and follow (to confirm or readjust) its
predictive hypotheses. To do this, technological systems allow
us to collect and integrate all kinds of day-to-day data from
each employee, their social interaction with other people and
their results through their manifest behaviors in the workplace.
Hassabis, et al, (2017) told that companies need dynamic
structures aimed at effective project management. Knowledge is
quickly transferred from one person to another, where
productivity is maximized, talent is enhanced, and unlimited
collaboration between people in different positions is favored.
The relevant information flows through fast and accessible
channels sharing acceptable practices, experiences, or concerns,
where emotional bonds are generated capable of retaining the
best and supporting and empowering those who need it most,
where communication between the employee and their company
is transparent, efficient, and reliable and find multiple channels
of channeling.
The rise of AI in sectors such as hospitality or medicine, to
name two examples, is worth studying, but there are still
challenges to be met soon. Most of them are related to the main

handicaps that this technological innovation has:
Offer care with greater empathy. The idea is that it is the
corresponding system that responds or attends to a potential
client without the participation of a worker. The chatbots can
hold a conversation, but her coldness can cause alterations in
engagement with the company. One of the challenges is,
therefore, to get more empathetic applications that are not
limited to offering answers, but also to interact with each client.
Townsend & Hunt (2019) stated that facilitate access to this
technology. The more production and innovation, the easier it
will be to find options at an affordable price. The improvement
of the applications in the market has the final objective of
generalizing their use.
Manage to avoid rejection of potential customers. It is
undoubtedly the most important objective, since in not a few
cases it is quite common to find a frontal rejection of this
alternative. Meeting the challenges of the two previous sections
will surely contribute to normalizing the use of AI in any
marketing campaign.
Now, as we anticipated at the beginning, you are surely
wondering how to take advantage of this resource in your
company. Marketing that incorporates Artificial Intelligence is
based on the premise of using the latest technologies for the
benefit of consumers.
Chan & Yuan (2019) found that it is also one of the main ways
that advertisers can ensure the Return on Investment (ROI) of
ad campaigns. With these tools, you can use customer data and
machine learning to get the most out of your advertising
investments.
Varlamov, et al (2019) noted that the main objective that AI
will allow you to achieve is the creation of appropriate content
for target audience, in addition to process optimization.
Considering AI is essential if you want to take your business to
the next level and create more effective and far-reaching digital
marketing strategies.Benefits of integrating Artificial

Intelligence in Marketing Strategy
So far, we have a brief overview of what the incorporation of
Artificial Intelligence can do in your business. However, here
we show you in detail some of the specific benefits that it will
offer you.
Sales Forecasts
Varlamov, et al (2019) noted that using Artificial Intelligence in
your marketing strategy will allow you to combine and cross
records of all the operations you carry out. AI can collect data
from multiple channels like emails, phone calls, and even face-
to-face meetings. With all this information, this type of
technology can predict the performance of current campaigns
and forecast future sales. Data analysis is a key strategy to learn
more about the performance of your business and, in this way,
prepare projections from now on.
The reports that the AI obtains on the behavior of users and
your interactions with them will also allow you to carry out
much more precise segmentations and even personalize the
messages you send to your customers. This will help you offer
products or services that they really need or that are interesting
to them. Similarly, it greatly facilitates the sales process and
automates routine processes such as receiving data.
Gajane & Pechenizkiy (2017) stated that by having more
information about your audience, you will be able to understand
their behaviors and their particular characteristics to interact
with them in the most effective way. This way you can evaluate
if your strategies are suitable for users, if they are working
optimally or if they need some adjustment.
All these procedures, when performed by a machine, are done
faster and at a lower cost, which will leave more human
resources free to be allocated to the most important part: the
direct and face-to-face relationship with customers.
Yampolskiy (2019) found that Artificial Intelligence will also
allow you a deep analysis of the activity and actions of your
competition. Through this technology, you will be able to have
a detailed approach to their methods and their audiences, to

compare them with your work and propose new positioning
tactics.
Artificial Intelligence in Real Estate
The artificial intelligence has allowed something as simple as
what we now are much habituated, like housing search by
attributes and the fact is that the development of internet
listings in the real estate sector has allowed buyers to search for
homes by location, price, square meters or number of rooms.
However, even after narrowing your search based on these
attributes, it can return results for hundreds or thousands of
properties. However, thanks to artificial intelligence, search
engines learn what behavior patterns of each customer and
restrict even more the answer, in addition to focusing it more
precisely.
Montani & Striani (2019) understand that several companies
have developed artificial intelligence applications that serve as
conversational interfaces with customers to answer customer
questions.
AI technology also offers a powerful tool to help agents
discover their potential customers, screening, for example,
those who are only looking for a home for entertainment, but
for whom buying a home is a distant reality, from those who
truly they are prepared to acquire a property.
De Oliveira, Sanin & Szczerbicki (2019) found that these
systems also use natural language processing (NLP) to analyze
the conversations of the potential client with the real estate
agent and assess the level of commitment of the potential buyer.
In the near future, an agent will be able to use a robot to
schedule customer appointments by phone, in any language, by
using a CRM capable of managing customers in a multilingual
format.Real Estate Agent Selection
Artificial intelligence can also be used in a real estate agency to
select the work team more efficiently, for example, eliminating
personal bias when interviewing candidates or providing

information, based on an in-depth market study, on where it is
necessary to hire more personal because the market has
untapped potential. Obviously, an artificial intelligence system
does not replace the work of an expert in the real estate sector,
but it does provide you with in-depth information to make more
precise and accurate decisions.
Bogachov, et al, (2020) stated that by combining the use of a
CRM and analysis of market data, artificial intelligence can
help real estate agents better predict the future value of a home
in a specific market, as the system can synthesize information
from a very wide variety from sources, including about
transportation, security, services, and about market activity and,
since most buyers view a new home as an investment, having a
more reliable forecast of its value can help increase the interest
of potential buyers. In addition, artificial intelligence applied
in the real estate sector will continue to evolve to facilitate
numerous processes, such as offering faster closing times,
smarter mobile applications, more detailed sector reports,
virtual visits to clients, among many others.
Shook, et al, (2019) mentioned that artificial intelligence to
determine, depending on the actual preferences of buyers if the
visit or purchase generates no. Thus, eliminating a high number
of unnecessary meetings or in which there is no real purchase
interest. This type of predictive analysis is carried out thanks to
artificial intelligence, allowing a forecast of the homes that can
best be adjusted to the profile of the interested buyer.
Heer (2019) thought that with block chain systems, it will be
possible to streamline and provide greater security to all
financial operations carried out digitally. Perhaps in the not-
too-distant future it will also be possible to use this technology
to provide greater security for buying and selling operations or
mortgages. Customer service is essential for a correct
relationship with the customer and for the brand image. More
and more, companies are betting on improving customer service
and promoting a more personalized service. Real estate
companies have already begun to introduce some questions to

clients in their systems that seek to identify what that client is
looking for in order to direct the call to the specific agent who
can help them. With artificial intelligence, the computer is
being given the ability to think and process information in such
a way that it gives meaning to the data and seeks a solution.
Bonacina (2017) mentioned that technology has evolved in an
incredible way and many of these processes have been
automated. Visits are reduced, paperwork is minimized and
results improve because we increase productivity thanks to the
new machines and processes that we are integrating.
The volume of data that we handle now is exponentially greater
than a few years ago, Big Data is already in charge of it, but it
is precisely this enormous volume of information that forces us
to trust Artificial Intelligence tools.
Kaplan & Haenlein (2019) stated that the Knowledge is power,
but only the AI can allow us to select the information necessary
and proper for us to take those decisions. The key is no longer
to access the data that the Internet offers us, but to have the
tools that analyze it for us in the way that best suits us. It is no
longer a question of spending hours in front of a computer
looking at properties but rather of telling an application to
search for us all over the Internet for what we need.
Dignum (2018) realized that the future of the real estate sector
goes through AI because Big Data is already available in the
network of networks. Now it is essential to have tools that find
the specific properties that we are looking for among all that
tangle of webs and that select them based on some guidelines
that we tell you, such as the city, neighborhood, price, square
meters, public transport, etc. With AI, the real estate sector will
be able to respond to all the needs of its clients in a faster, more
efficient and effective way and will reduce the weight that the
old commercials had in the sector.Artificial Intelligence in
CRM
Customer Relationship Management or better known as CRM is
the software that has been helping companies for years to

improve their relationship with their customers. According to
the results obtained by a study by Forrester and published by
the digital Merca2.0, these solutions increase the productivity
of the teams by 50%, increase sales by 5% and the cost of
customer service is reduced by 40%.
Dignum (2018) realized that the information extracted by these
tools can be further benefited if we integrate Artificial
Intelligence in them, resulting in three great advantages of
applying AI to CRM. Reduces the time of the most mechanical
tasks. Thanks to the incorporation of Artificial Intelligence in
CRM systems, workers are freed from the routine efforts such
as entering data in databases, answering emails, managing
invoices, or scheduling meetings, among others. According to
Salesforce estimates published in the CRM Today portal, up to
four hours a week can be saved in repetitive tasks.
Kokina & Davenport (2017) realized that AI is the best virtual
assistant. In line with routine tasks that take time and effort
from workers, Artificial Intelligence is much faster and more
efficient when it comes to generating immediate and automatic
responses, emails, functions related to data collection and
monitoring thanks to its ability to obtain customer information
such as behavior on the site or demographic data. In addition to
the speed in obtaining customer data, the AI applied to CRM
collects quality and very specific information regarding gender,
geographic location, and purchase history, behavior on the web
or other sections that allow companies to develop content very
specific to develop personalized messages that have a better
impact. Which translates into a higher percentage of success of
the sales strategy and a closer loyalty of the customers.
Kokina & Davenport (2017) realized that with a unified
customer success platform, Salesforce is the heart of key
customer data for most businesses. Bringing Salesforce
Einstein, an intelligence layer, to the platform that can also
easily integrate additional data from third-party systems avoids
the problem of customer data silos. To eliminate the need to
build artificial intelligence models from scratch, Salesforce

Einstein brings sophisticated data analytics and pre-built
predictive models to business process and application creators,
without complexities or cumbersome processes. The Salesforce
CRM and its AI capabilities foster the ideal environment for
creating diverse applications and experiences, not just for
customers, but also for everyone in the organization. Using the
same data model, business logic, and experience layer that
powers CRM, combined with the simple point-and-click way to
build applications, both IT and business users can leverage an
intelligence boost to provide experiences with artificial
intelligence and a 360-degree view of the customer to anyone.
Kormalev, et al, (2018) realized that using a consumer shopping
experience as an example, the AI models embedded in the
Salesforce CRM system's personalization engine take into
account the catalog that a given buyer sees and the context of
how the merchant interacts with that buyer, and then classifies
each product for that buyer in terms of relevance to search
results, achieving the most specific and personalized results of
all time. This is done at scale, constantly refining consumer
recommendations based on learning.
Acay, Sonenberg & Tidhar (2019) found that the data for such
recommendations includes real-time and historical data on
clicks from various sources. By selecting a challenging
champion model algorithm, each experience becomes more
personalized and intelligent because the algorithm "learns" from
past data and can produce results with greater precision. The
bottom line of accurate recommendations is that customer
conversions skyrocket and your inventory value increase.
Perez, et al, (2018) stated that these advanced artificial
intelligence algorithms can also improve both employee and
customer experiences using tools such as speech recognition,
sentiment analysis, intent, content summarization through
natural language processing, and data table-based responses.
The key to the intelligence layer built into Salesforce CRM is
the point-and-click capabilities to enable AI for any application,
without the need for a team of data scientists on standby.

Everyone in the organization, from developers to business
users, can collaborate to create the right customer experience
using rapid, iterative prototyping.
Hassabis, et al, (2017) told that the low-cost code-free
programming environment of the Salesforce platform brings
traditionally non-tech teams on board, such as customer service
and marketing teams that can be actively involved in creating a
truly intelligent customer experience. This enables IT to take a
coaching role to empower teams across the organization to
create their own artificial intelligence solutions.Artificial
Intelligence in Banking
Without a doubt, artificial intelligence in the banking and
finance sector is one of the most interested in the development
of this technology, due to the enormous volume of data it holds.
Not surprisingly, it is the world's leading investor in artificial
intelligence.
Hassabis, et al, (2017) told that figures from the European
Commission reflect a strong investment imbalance between
Europe and its main competitors. Private investment in artificial
intelligence in Asia and North America exceeds 6,500 and
12,000 million euros respectively. This contrasts with the 3,500
million euros that the European financial sector allocates to the
development of this technology. The aforementioned Bank of
Spain report indicates that Asian and American companies have
the advantage of having the infrastructure and the volume of
data necessary for the development of artificial intelligence in
the banking and finance sector.
Chan & Yuan (2019) found that according to a report published
by the World Economic Forum and Deloitte, artificial
intelligence will have a disruptive nature in the financial sector
“weakening the ties that have held together the components of
existing financial institutions.”
Varlamov, et al (2019) noted that the new operating models will
favor those institutions focused on the sophistication and scale
of the data, rather than on capital. It is expected that the entities
are going to turn to the relationship with their clients, advisory

services, and collaborative solutions.
On the other hand, a new battlefield is presented, in which the
rules of the traditional financial and banking sector will be
relegated by automated systems.AI based Chatbots in Financial
Institutions
Wamba-Taguimdje, et al (2020) noted that these virtual
assistants can respond to a multitude of requests from users.
This allows us to provide an automated customer service
available 24 hours a day. Likewise, it also makes it possible to
collect in an automated way the interactions carried out by the
users themselves, to later improve the quality of the service.
This automation process will not only benefit the execution of
customer relationships, but also the execution of internal
processes. Consequently, it is expected that the sector will see
its productivity increase significantly in the coming years.
Customization of Products
Thanks to the information gathered about each client, it will
also be possible to create more personalized experiences , as
well as to adjust the conditions and characteristics of the
products and services offered to each client.Credit Rating,
Fraud Detection and Risk Management
Gajane & Pechenizkiy (2017) stated that artificial intelligence
applied to predictive analytics is going to make a considerable
change in credit rating and risk management systems. On the
one hand, risk analysis will gain in reliability, which allows the
conditions for granting credit or making the investment to be
adjusted more precisely.
In addition, the analysis of unstructured data opens the door to
granting credit to previously ineligible clients.
in the financial sector is extremely effective in preventing fraud
in the use of credit cards. This problem has been increasing
over the last few years; following the evolution of electronic
commerce. Analytical systems can anticipate a change in user
behavior patterns, and issue alerts or even carry out preventive

measures, to avoid fraud.
On the other hand, these same systems can also be used to
detect practices that may be involving money laundering.
Finally, artificial intelligence algorithms are already being used
for the execution of investment strategies. This technology is
especially valued in those strategies that require high
refinement, together with a high speed of execution.
Montani & Striani (2019) understand that decision-making is
thus automated, under the parameters included in the algorithm,
which is in charge of executing the operations. Ultimately,
artificial intelligence is completely changing the rules of the
game within the banking and finance sector.Present and Future
of AI in Financial Sector
AI can drive efficiency of operations in many areas of finance
like trading or risk management. While some uses are pertinent
to certain zones within monetary services, or they can easily
exploit by anyone.Risk Management
AI has proven its worth when it comes for fraud detection and
security. Common fraud detection ways like analyze the
structured data with set of rules is one example. Let`s say a
company set threshold for wire transfer of $20,0000, then any
transaction that goes above or exceeds this threshold may
flagged for investigation.
Montani & Striani (2019) understand that this type of tests often
makes false positive results and needs much effort. Perchance,
it needs more enhanced techniques that cybercrime scammers
change frequently. In this way, systems must made smarter.
Bogachov, et al, (2020) stated that with new learning methods
for machine to understand algorithms such as deep learning or
reinforcement learning, new features can help systems to
become dynamic and self-aware in some way. With help of
cognitive analytics, fraud detection models can become smart,
accurate and robust.
Shook, et al, (2019) mentioned that if an intelligent system
removes somewhat that it fixes as possible scam, and a human
being controls that it is not scam due to X, Y, and Z, the

computer acquires from perception of humans, and it will not
send any same detection.
Since years, management of investment companies have found
that machine can also handle trading such has 1,443 hedge
funds that represent 10% of overall frauds rely on big statically
models that are made by scientists who are PhD in Mathematics.
On the other hand, these models use data, which is historical, or
static that require human attention. They often do no perform
well in the market changes. Therefore, funds are made for
intelligent models, which can analyze large data in huge volume
but keep improving themselves.
Heer (2019) thought that new technologies are uses complex
models like Deep Learning, which is form of AI ML, model
called “Bayesian Network”. This model is inspired by genetics.
Software, which are AI based, can handle large chunks of data
to learn about predictions for financial markets. These AI based
trading software can absorb everything from tweets, reports,
news reports, international financial policies from different
sources. To make this clear, there is much difference in High
Frequency Trading, which allow traders to start millions of
orders and scan different markets in seconds.
Bonacina (2017) mentioned that the AI-powered programs talk
over are looking for the top long-term trading plans which AI
machines can make strategy, not humans.
Finance managers are still using portfolio allocation since start
of 2000 and their advisors have to rely on these techs. Many
robo-advisors allow customers to directly take the services and
they manage or monitor the markets non-stop 24/7. Robo-
advisors allow investors to save up to 75% cost savings and less
participation in the trading.
Kaplan & Haenlein (2019) stated that robo-advisor help more in
repetitive tasks like transferring assets or opening new

accounts. These tasks just need answering of simple questions
about liquidity factors or risk appetite that robo-advisor can
translate into investment topics.
Robo-advisors today allow clients to manage ETF portfolios as
per their preferences. In future, these capabilities will become
more advance such as automatic asset swap and more asset
coverage in finance world. Robo-advisor have good impact on
the personal finance and management of wealth sectors. Robo-
advisor`s current asset under total asset management represents
only $10 trillion of wealth, while management industry is 5
trillion.
References
Gajane, P., & Pechenizkiy, M. (2017). On formalizing fairness
in prediction with machine learning. arXiv preprint
arXiv:1710.03184.
Yampolskiy, R. V. (2019). Unexplainability and
incomprehensibility of artificial intelligence. arXiv preprint
arXiv:1907.03869.
Montani, S., & Striani, M. (2019). Artificial intelligence in
clinical decision support: a focused literature survey. Yearbook
of medical informatics, 28(1), 120.
de Oliveira, C. S., Sanin, C., & Szczerbicki, E. (2019, April).

Towards knowledge formalization and sharing in a cognitive
vision platform for hazard control (CVP-HC). In Asian
Conference on Intelligent Information and Database
Systems (pp. 53-61). Springer, Cham.
Bogachov, S., Kwilinski, A., Miethlich, B., Bartosova, V., &
Gurnak, A. (2020). Artificial intelligence components and fuzzy
regulators in entrepreneurship development. Entrepreneurship
and Sustainability Issues, 8(2), 487.
Shook, E., Bowlick, F. J., Kemp, K. K., Ahlqvist, O.,
Carbajeles-Dale, P., DiBiase, D., ... & Wang, S. (2019). Cyber
literacy for GIScience: Toward formalizing geospatial
computing education. The professional geographer, 71(2), 221-
238.
Heer, J. (2019). Agency plus automation: Designing artificial
intelligence into interactive systems. Proceedings of the
National Academy of Sciences, 116(6), 1844-1850.
Bonacina, M. P. (2017, November). Automated Reasoning for
Explainable Artificial Intelligence. In [email protected]
CADE (pp. 24-28).
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand:
Who’s the fairest in the land? On the interpretations,
illustrations, and implications of artificial intelligence. Business
Horizons, 62(1), 15-25.
Dignum, F. (2018). Interactions as social practices: towards a
formalization. arXiv preprint arXiv:1809.08751.
Wang, P. (2019). On defining artificial intelligence. Journal of
Artificial General Intelligence, 10(2), 1-37.
Kokina, J., & Davenport, T. H. (2017). The emergence of
artificial intelligence: How automation is changing
auditing. Journal of emerging technologies in accounting, 14(1),
115-122.
Kolski, C., Boy, G. A., Melançon, G., Ochs, M., &
Vanderdonckt, J. (2020). Cross-fertilisation between human-
computer interaction and artificial intelligence. In A guided tour
of artificial intelligence research (pp. 365-388). Springer,
Cham.

Kormalev, D. A., Kurshev, E. P., Vinogradov, A. N., Belov, S.
A., & Paramonov, S. V. (2018). Interactive visualization of non-
formalized data extracted from news feeds: Approaches and
Perspectives. In Proceedings of the Scientific-Practical
Conference" Research and Development-2016" (pp. 93-100).
Springer, Cham.
Acay, D. L., Sonenberg, L., & Tidhar, G. (2019). Formalizing
tool use in intelligent environments. Journal of Ambient
Intelligence and Humanized Computing, 10(4), 1597-1610.
Perez, J. A., Deligianni, F., Ravi, D., & Yang, G. Z. (2018).
Artificial intelligence and robotics. arXiv preprint
arXiv:1803.10813.
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M.
(2017). Neuroscience-inspired artificial
intelligence. Neuron, 95(2), 245-258.
Townsend, D. M., & Hunt, R. A. (2019). Entrepreneurial action,
creativity, & judgment in the age of artificial
intelligence. Journal of Business Venturing Insights, 11,
e00126.
Chan, H. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S.
(2019). Advancing drug discovery via artificial
intelligence. Trends in pharmacological sciences, 40(8), 592-
604.
Varlamov, O. O., Chuvikov, D. A., Aladin, D. V., Adamova, L.
E., & Osipov, V. G. (2019, May). Logical artificial intelligence
Mivar technologies for autonomous road vehicles. In IOP
Conference Series: Materials Science and Engineering (Vol.
534, No. 1, p. 012015). IOP Publishing.
Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., &
Wanko, C. E. T. (2020). Influence of artificial intelligence (AI)
on firm performance: the business value of AI-based
transformation projects. Business Process Management Journal.
bhusal1

Prepared by Comment by Daniel Creider: No title page
Remove headers from paper
All references must have a URL which is a hyperlink. None of
your references have a URL and they are not acceptable without
the URL.
Deepak Bhusal
CWID:50259419
To Professor: Dr. R. Daniel Creider
Table of Contents
Abstract 3
Introduction 4
Literature Review 5
AI for Justice 6
AI in Medical Teaching 8
Artificial Intelligence in human resource management 9
AI in Marketing 10
Artificial Intelligence in Real Estate 13
Real Estate Agent Selection 14
Artificial Intelligence in CRM 16
Artificial Intelligence in Banking 18
AI based Chatbots in Financial Institutions 19
Customization of Products 19
Artificial Intelligence: Formalizing Human Capabilities
Comment by Daniel Creider: You are missing the required
outline of the paper.

See week 5 for first 2-3 lines of the paper. Title font is too big.
What is the meaning of the title?
How is it related to the contents of the paper?
This paper appears to be too broad.Abstract Comment by
Daniel Creider: This abstract seems too long.
Artificial Intelligence cannot replace three human abilities, in
which human beings present an insurmountable advantage
today, and they are empathy, leadership, and creativity. AI can
quickly take over essential verbal and visual communication
services, such as digital assistant-based customer service.
However, our ability to empathize with the client and to carry
out non-verbal communication based on emotions gives us an
advantage that Artificial Intelligence can never replace. These
qualities can make the difference between a misunderstood and
dissatisfied customer versus an understood and loyal customer.
Comment by Daniel Creider: Spacing between lines must
be 1 not 1.5
Gajane & Pechenizkiy (2017) stated that it is undeniable that AI
will replace workers in essential economic-financial
management, logistics, materials, human resources, and
projects. Still, people have more advanced management
capabilities that AI cannot return. The following two skills play
a crucial role:
First is the ability to manage the growth of human groups. This
is the ability to help members of the organization develop their
skills and grow professionally through our innate leadership
ability to set goals, motivate, lead by example, evaluate,
delegate, and transmit experience.
Secondly, there is the ability to carry out the organization
members' recovery management when they suffer problems
derived from interpersonal relationships or other emotional
reasons. It is based on the skills of understanding, counseling,
care, and protection.

Yampolskiy (2019) found that AI can never replace the vision,
invention, and original proposal of innovative and disruptive
designs, not only applied to the individual as a genius but also
the ability to carry out collective intelligence management
focused on innovation, facilitating the appearance of new
knowledge and wisdom. Besides, even more, difficult it will be
able to replace the ability to implement new ideas in the
organization, communicating attractively, persuading, and
making the organization move smoothly to implement
innovative ideas.
Keywords
Artificial Intelligence, Marketing, Human Resource
Management, Medical Sciences, Nursing, Introduction
The possibility of thought in machines is a concern that has
been raised for a long time; science fiction, as well as
engineering and philosophy, have sought to provide an answer
to the question "Can machines think?" Famous exponents of
both affirmative answers, given by Turing or Kurzweil, and
negative responses can be found in the bibliography, Sear le or
Penrose being notable examples. Although each of these
represents a different position, there is a common characteristic
to the other proposals. In all of them, "thinking" is defined by
the purely human experience of doing it. By presenting the
Game of Imitation as a criterion for determining whether
machines think, one is declaring that they will only do so when
they can carry out behavior’s characteristic of the human way of
thinking.
Montani & Striani (2019) understand that by denying that a
machine can think because, if we were in its position, there
would be characteristics of our intelligence -such as semantic
content and demonstration of mathematical truths so we would
not have access, we are requesting that for a machine to "think,"
it can carry out all the functions of human thought. The
proceeding is natural since our experience as thinking subjects
is the one, we can most easily access. However, this seems to
bring with it an unfair judgment for other possible mental

forms.
Suppose that our first-person experience was equally important
in establishing what it means, for example, "to breathe." In that
case, we would have a fundamental component of the definition
of the possession of lungs and a structure similar to the human
one. Under this definition, fish or cells would never be
considered exponents of subjects that breathe, leaving aside
cellular or gill respiration phenomena. In the same way that we
would leave out many other breathing subjects by exploring
how the phenomenon presents itself exclusively in humans, I
also believe that it is possible to leave out other minds by
exploring the concept of thinking solely from our perspective.
Suppose it is considered that it is essential to understand forms
of thought that go beyond the human. In that case, it will be
necessary to build a tool that allows the configuration of the
concept so that there is more information that configures it and
our experience.
Montani & Striani (2019) understand that what is sought when
using refractive equilibrium to define what it means to think is
to change the weight that human experience has for definition.
The Studies in artificial intelligence indicate that we take our
expertise from subjects considering as the only criterion to
define the characteristics that anything must meet to be
classified as thinking. By producing a definition through the
refractive equilibrium of According to the monograph's body's
proposals, it will seek that now this experience is just one more
tool for characterization. Our experience human will guide the
report, but the way we extract information from it be careful to
give other minds a chance to be taken into account despite their
material realization. This is not to say that the refractive
equilibrium process has to grant due to the existence of minds
other than human; in fact, a result of the process may be that,
according to the information available at the time, only we have
examples of thought in us. However, they would find the
difference in why we exclude other minds; currently seems to be
that the main reason is of perspective. Through balance, it will

find representative characteristics (and investigable in more
subjects than ourselves) provide a definition. In this way, we
would go from asking ourselves, "Can other objects think as
humans do?” a "How to do the other processes of thought?"
without assuming that they think, since the equilibrium process
will allow that the investigation of the how modifies what the
concept means Comment by Daniel Creider: This text looks like
you have just copied the words from the reference rather than
paraphrasing what the article was about. This is plagiarism.
Any text that is a direct quote must be quoted but you cannot
have too many quotes in the paper.Literature Review
Comment by Daniel Creider: The text above looks like
literature review more than an introduction.
Before resolving any doubts about the capabilities of a machine,
it would seem natural first to determine the limits of what you
are referring to by "machine." In the traditional problem of
artificial intelligence, research is found focused on the study of
digital computers presented by this definition proposed by
Turing is given in an open enough way to include within them a
great variety of later developments in computational
engineering, this perspective will carry some problems that it
will expose later. The historical moment in which CMI is
written is one in which computer personnel did not yet have a
significant presence in the world. Most machines computational
systems belonged to research institutes and were out of the
reach of the public generally.
Bogachov, et al, (2020) stated that to imply what a digital
computer, a digital computer, understands, Turing defines them
by an analogy with a human-machine (the processes that he
could carry out a person under a closed system of instructions);
from which the capabilities and limits the machine will
encounter. This is an application of those strict definition
presented in his famous "Computable Numbers with an
Application in which devices are presented as a tool to solve a
problem of mathematics. Digital computers will be composed of
three main parts (i) storage, (ii) execution unit, and (iii) control,

which make up a functional object. The storage represents the
space in which the data is presented. The information to be
manipulated and the results are there; the execution unit is in
charge of carrying out the processes, and the control is the fixed
rules, presented as tables that the system must follow.AI for
Justice Comment by Daniel Creider: Wrong font for heading
The first proposition or premise states that all law students are
shortsighted. Therefore, all law students are included in the
universe of people who have myopia. The second proposition
informs us that some myopic people are intolerant to contact
lenses. That is, within the universe of myopic people, a group
does not tolerate contact lenses. If these are our two premises,
the conclusion is not correct because those two statements are
compatible with a situation in which the group of myopic people
who do not tolerate contact lenses does not include any of the
law students - all of them myopic, as we know -. If true, the two
premises are compatible with states of the world where all law
students are intolerant of contact lenses, only some law students
are intolerant of contact lenses, or even no law student is
intolerant of contact lenses.
Bogachov, et al, (2020) stated that the alleged conclusion does
not necessarily follow from the premises. It is a false syllogism
or "paralogous." Something that looks like a syllogism but is
not. A subtle trap is committed when the second premise - the
one that contains the subject of the conclusion - is placed first,
while the first premise - the one that includes the predicate of
the decision - is presented in second place. In this way, when
we find ourselves in the first place with a universal type
statement - all law students are ... -the reasoning looks more
like a syllogism. If we rearrange the two premises (some
nearsighted people do not tolerate contact lenses; all law
students are nearsighted; then…), the jump or logical
inconsistency is perhaps more easily detected.
Heer (2019) thought that to see it more clearly, we can try a
graphic representation of the matter. We draw a huge circle that
represents all myopic people in the world. With a smaller

process, we mean all law students in the world. We must
necessarily draw this smaller circle in its entirety within the
myopic circle (because our reasoning starts from the premise
that all law students are myopic). Finally, we must draw a third
circle - which is the one that interests us the most - that
represents all subjects intolerant to contact lenses. Where and
how do we draw this circle? Well, there are a few possibilities.
It must necessarily be drying to some extent with respect to
myopic circle because some myopic have the property of
intolerance to contact lenses. However, fulfilling that
requirement, we have, as I say, several possibilities. I invite the
reader to play set logic for himself, using paper and pencil and
draw all the possible combinations of circles compatible with
the two premises. Comment by Daniel Creider: Starting most
paragraphs with an author’s name makes the paper less
interesting to read.
Bonacina (2017) mentioned that there is still another way to
deal with the problem: simplify or "formalize" our language.
Thus, we replace the expression "all law students are myopic"
by the following: "all A is B"; the phrase "some myopic are
intolerant to contact lenses," for "some B are C"; and our
conclusion would become "then some A is C". We could use a
more sophisticated notation like "for all x if x is A then x is B"
(∀ x A (x) → B (x)), But the important thing is that we have
replaced some terms that had a specific meaning. Therefore, it
referred to certain sets or classes of entities or realities existing
in the world (law students, myopic, intolerant of contact lenses)
by symbols (A, B, C) with which you can refer to any set of
entities or realities. We could say that we have eliminated all
the "semantics" from our reasoning. We are left only with the
"syntax": with the position of subject or predicate that each of
the terms (A, B, C) occupy in each of the propositions and with
some "quantifiers" that model the scope of these terms (all,
some). Reduced reasoning to its syntax - or in other words, to
its formal structure - a purely ceremonial "calculation" is
possible and comfortable, allowing us to verify the correctness

of the reasoning.
AI in Medical Teaching
The ability of a system to correctly interpret external data, learn
from such data, and use that knowledge to achieve more specific
tasks and goals through flexible adaptation." One of its uses for
decades was the application of game theory to defeat the best
human players.
Kaplan & Haenlein (2019) stated that it is structured from
different areas of knowledge such as computer science, logic,
mathematics, philosophy, and experience to develop
computational models capable of carrying out human activities,
based on two fundamental characteristics: reasoning and
behavior. In teaching, it is summarized from a pedagogical
solution to the problem. It is presented as a discipline that is
responsible for studying and building "intelligent agents," that
is, systems capable of perceiving their environment and acting
on it to achieve the proposed objectives, where each “agent” is
implemented through a function, which establishes a
correspondence between his or her perceptions and their
actions.
Kaplan & Haenlein (2019) stated that Artificial intelligence
gives machines the ability to "reason and learn." Two
capabilities are very useful in clinical diagnosis. For example, a
computer program can analyze the photo of a spot on the skin
and comparing it with its database, establish the probabilities
that it is a melanoma. Similar applications are being developed
for many other diseases, although for now, AI complements and
strengthens the diagnosis of doctors.
A robotic nurse? It seems that it will be one of the keys to
assisting the elderly and dependent patients in the future. So
far, robotic pets have been developed for therapeutic purposes
to help Alzheimer's patients. Robotic pets stimulate brain
functions in patients by delaying cognitive problems that
improve quality of life and reduce dependency on social

services.Ease the burden on doctors Comment by Daniel
Creider: Wong font and does not need to be italicized.
Wang (2019) found that analysis tests, X-rays, CT scans, data
entry, and other mundane tasks can be performed faster and
more accurately if carried out by robots. Cardiology and
radiology are two examples of disciplines where the amount of
data to analyze can be overwhelming.
Perhaps in the future, simple cases will be left exclusively in
the hands of AI, and human doctors will only deal with the most
complicated ones. Comment by Daniel Creider: Why do you
have a one sentence paragraph?Drug development
Wang (2019) found that getting effective new drugs over
clinical trials can take more than a period and cost too much.
Thus, streamlining the process by using AI could transform the
globe. In the modern Ebola calamity, they used an AI-powered
software to examine current drugs that it could reform to fight
the ailment or disease. The program found two medications that
can lower Ebola contagion in one day when such study mostly
takes months or years - a change that maybe saved many lives
or more.Artificial Intelligence in human resource management
They are the virtual assistant's AI, Chabot that respond to
requests of different difficulty. A Chabot is a software tool
designed to help users carry out other tasks by implementing
machine learning and artificial intelligence (AI). As their name
indicates, they can assist us in carrying out internal processes
related to the administrative part of Human Resources; they can
provide information on policies and procedures or filter
curriculum vitae, as we said. These Chabot’s are capable of
learning, so we are working on communicating with them
because we do not know how far their understanding could go,
both intellectual and sensory. We know that they can read
emotions on people's faces, which would bring their level of
understanding closer to ours, and that they can process levels of
information in such a way that they can transform it into
knowledge.

Kolski & Vanderdonckt (2020) stated that the question is at
what level of trust you would place in a "machine" and if you
would leave such human issues as talent management and
professional development in their hands. Can a robot evaluate,
and plan areas related to our performance? It is predicted that
after developing the appropriate algorithms, an algorithm and
therefore a robot could learn from human behavior and its
needs. It could plan solutions to needs related to the human
essence and that is so much needed in organizations, such as
motivation and learning. Many things are said about the future
that is to come. Still, it is difficult, at least for me, to believe
that a robot will be able to intervene in such subtle matters
without the intervention of a person who supervises the process.
Let us look at the paradigm shift that new technologies are
generating and, from there, in the latest models of the
relationship between people that are being established. We can
discover new models of Human Resource Management that are
more effective and empowering. Suppose we start from the
initial consensus on the concept that "organizations achieve
results through the behaviors manifested by the people who
compose them". In that case, an effective people management
model will be one that maximizes their performance and
productivity, guaranteeing the best results that it can expect
from them.
Kormalev, et al, (2018) realized that for this, technology will
allow us to put integral tools at people's service that generate a
collaborative and interrelated context that connects processes
and people, adding value to the business chain and maximizing
results. Let us take an example; a phone has no value on its own
if it has no other phone to communicate with. In this way, two
telephones will double in value once they can contact each
other. However, this valued relationship will be reduced to
exchanging information between the two terminals unless a
third or other terminals can connect simultaneously to the same
conversation. In this way, each telephone that joins the
discussion will add exponential value to it in terms of time and

communication efficiency.
AI in Marketing
The marketing world discovered it a long time ago, and for that
reason, it makes the client/consumer the protagonist of the
experience with the company. The question would be if we were
prepared to create a people management model based on
empowering employees to become the real protagonists of
business activity. The times of control ended since we
discovered that the power exercised over others is inversely
proportional to the trust that is generated in the other.
Acay, Sonenberg & Tidhar (2019) found that the times have
come to empower, to listen to others, to enhance autonomy and
risk, to assume responsibilities and decisions, to favor the
establishment of links and networks, to share knowledge, to
learn from others, to awaken interest in the search for
information, to filter and select the relevant information, to
maximize performance and productivity.
In short, we are in times of transparency and information
available to anyone, in times of collaboration and handing over
the power of action to the real protagonists, people, and talent.
If this is the new paradigm for managing people and human
resources in the future companies, how can technology help us
manage this model?
Perez, et al, (2018) stated that Artificial Intelligence systems
differ, among other things, from traditional Data Warehouse
systems in that the latter generate indicators calculated from the
analysis of data from the source systems (transactional
systems). The former can establish predictive hypotheses based
on the same data, follow up the selected predictions, confirm or
correct these hypotheses with the temporal monitoring of the
evolution of the data and the results. In this way, as time passes
and the system handles a larger and more complete sample of
data, it learns to be more and more efficient in its prediction
models.
The result of the predictions will be more reliable and accurate.

The more inputs are collected within the system and the longer
it takes to establish and follow (to confirm or readjust) its
predictive hypotheses. To do this, technological systems allow
us to collect and integrate all kinds of day-to-day data from
each employee, their social interaction with other people and
their results through their manifest behaviors in the workplace.
Hassabis, et al, (2017) told that companies need dynamic
structures aimed at effective project management. Knowledge is
quickly transferred from one person to another, where
productivity is maximized, talent is enhanced, and unlimited
collaboration between people in different positions is favored.
The relevant information flows through fast and accessible
channels sharing acceptable practices, experiences, or concerns,
where emotional bonds are generated capable of retaining the
best and supporting and empowering those who need it most,
where communication between the employee and their company
is transparent, efficient, and reliable and find multiple channels
of channeling.
The rise of AI in sectors such as hospitality or medicine, to
name two examples, is worth studying, but there are still
challenges to be met soon. Most of them are related to the main
handicaps that this technological innovation has:
Offer care with greater empathy. The idea is that it is the
corresponding system that responds or attends to a potential
client without the participation of a worker. The chatbots can
hold a conversation, but her coldness can cause alterations in
engagement with the company. One of the challenges is,
therefore, to get more empathetic applications that are not
limited to offering answers, but also to interact with each client.
Townsend & Hunt (2019) stated that facilitate access to this
technology. The more production and innovation, the easier it
will be to find options at an affordable price. The improvement
of the applications in the market has the final objective of
generalizing their use.
Manage to avoid rejection of potential customers. It is

undoubtedly the most important objective, since in not a few
cases it is quite common to find a frontal rejection of this
alternative. Meeting the challenges of the two previous sections
will surely contribute to normalizi ng the use of AI in any
marketing campaign.
Now, as we anticipated at the beginning, you are surely
wondering how to take advantage of this resource in your
company. Marketing that incorporates Artificial Intelligence is
based on the premise of using the latest technologies for the
benefit of consumers.
Chan & Yuan (2019) found that it is also one of the main ways
that advertisers can ensure the Return on Investment (ROI) of
ad campaigns. With these tools, you can use customer data and
machine learning to get the most out of your advertising
investments.
Varlamov, et al (2019) noted that the main objective that AI
will allow you to achieve is the creation of appropriate content
for target audience, in addition to process optimization.
Considering AI is essential if you want to take your business to
the next level and create more effective and far-reaching digital
marketing strategies.Benefits of integrating Artificial
Intelligence in Marketing Strategy
So far, we have a brief overview of what the incorporation of
Artificial Intelligence can do in your business. However, here
we show you in detail some of the specific benefits that it will
offer you.
Sales Forecasts
Varlamov, et al (2019) noted that using Artificial Intelligence in
your marketing strategy will allow you to combine and cross
records of all the operations you carry out. AI can collect data
from multiple channels like emails, phone calls, and even face-
to-face meetings. With all this information, this type of
technology can predict the performance of current campaigns
and forecast future sales. Data analysis is a key strategy to learn
more about the performance of your business and, in this way,
prepare projections from now on.

The reports that the AI obtains on the behavior of users and
your interactions with them will also allow you to carry out
much more precise segmentations and even personalize the
messages you send to your customers. This will help you offer
products or services that they really need or that are interesting
to them. Similarly, it greatly facilitates the sales process and
automates routine processes such as receiving data.
Gajane & Pechenizkiy (2017) stated that by having more
information about your audience, you will be able to understand
their behaviors and their particular characteristics to interact
with them in the most effective way. This way you can evaluate
if your strategies are suitable for users, if they are working
optimally or if they need some adjustment.
All these procedures, when performed by a machine, are done
faster and at a lower cost, which will leave more human
resources free to be allocated to the most important part: the
direct and face-to-face relationship with customers.
Yampolskiy (2019) found that Artificial Intelligence will also
allow you a deep analysis of the activity and actions of your
competition. Through this technology, you will be able to have
a detailed approach to their methods and their audiences, to
compare them with your work and propose new positioning
tactics.
Artificial Intelligence in Real Estate
The artificial intelligence has allowed something as simple as
what we now are much habituated, like housing search by
attributes and the fact is that the development of internet
listings in the real estate sector has allowed buyers to sear ch for
homes by location, price, square meters or number of rooms.
However, even after narrowing your search based on these
attributes, it can return results for hundreds or thousands of
properties. However, thanks to artificial intelligence, search
engines learn what behavior patterns of each customer and
restrict even more the answer, in addition to focusing it more

precisely.
Montani & Striani (2019) understand that several companies
have developed artificial intelligence applications that serve as
conversational interfaces with customers to answer customer
questions.
AI technology also offers a powerful tool to help agents
discover their potential customers, screening, for example,
those who are only looking for a home for entertainment, but
for whom buying a home is a distant reality, from those who
truly they are prepared to acquire a property.
De Oliveira, Sanin & Szczerbicki (2019) found that these
systems also use natural language processing (NLP) to analyze
the conversations of the potential client with the real estate
agent and assess the level of commitment of the potential buyer.
In the near future, an agent will be able to use a robot to
schedule customer appointments by phone, in any language, by
using a CRM capable of managing customers in a multilingual
format.Real Estate Agent Selection
Artificial intelligence can also be used in a real estate agency to
select the work team more efficiently, for example, eliminating
personal bias when interviewing candidates or providing
information, based on an in-depth market study, on where it is
necessary to hire more personal because the market has
untapped potential. Obviously, an artificial intelligence system
does not replace the work of an expert in the real estate sector,
but it does provide you with in-depth information to make more
precise and accurate decisions.
Bogachov, et al, (2020) stated that by combining the use of a
CRM and analysis of market data, artificial intelligence can
help real estate agents better predict the future value of a home
in a specific market, as the system can synthesize information
from a very wide variety from sources, including about
transportation, security, services, and about market activity and,
since most buyers view a new home as an investment, having a
more reliable forecast of its value can help increase the interest
of potential buyers. In addition, artificial intelligence applied

in the real estate sector will continue to evolve to facilitate
numerous processes, such as offering faster closing times,
smarter mobile applications, more detailed sector reports,
virtual visits to clients, among many others.
Shook, et al, (2019) mentioned that artificial intelligence to
determine, depending on the actual preferences of buyers if the
visit or purchase generates no. Thus eliminating a high number
of unnecessary meetings or in which there is no real purchase
interest. This type of predictive analysis is carried out thanks to
artificial intelligence, allowing a forecast of the homes that can
best be adjusted to the profile of the interested buyer.
Heer (2019) thought that with block chain systems, it will be
possible to streamline and provide greater security to all
financial operations carried out digitally. Perhaps in the not too
distant future it will also be possible to use this technology to
provide greater security for buying and selling operations or
mortgages. Customer service is essential for a correct
relationship with the customer and for the brand image. More
and more, companies are betting on improving customer service
and promoting a more personalized service. Real estate
companies have already begun to introduce some questions to
clients in their systems that seek to identify what that client is
looking for in order to direct the call to the specific agent who
can help them. With artificial intelligence, the computer is
being given the ability to think and process information in such
a way that it gives meaning to the data and seeks a solution.
Bonacina (2017) mentioned that technology has evolved in an
incredible way and many of these processes have been
automated. Visits are reduced, paperwork is minimized and
results improve because we increase productivity thanks to the
new machines and processes that we are integrating.
The volume of data that we handle now is exponentially greater
than a few years ago, Big Data is already in charge of it, but it
is precisely this enormous volume of information that forces us
to trust Artificial Intelligence tools.

Kaplan & Haenlein (2019) stated that the Knowledge is power,
but only the AI can allow us to select the information necessary
and proper for us to take those decisions. The key is no longer
to access the data that the Internet offers us, but to have the
tools that analyze it for us in the way that best suits us. It is no
longer a question of spending hours in front of a computer
looking at properties but rather of telling an application to
search for us all over the Internet for what we need.
Dignum (2018) realized that the future of the real estate sector
goes through AI because Big Data is already available in the
network of networks. Now it is essential to have tools that find
the specific properties that we are looking for among all that
tangle of webs and that select them based on some guidelines
that we tell you, such as the city, neighborhood, price, square
meters, public transport, etc. With AI, the real estate sector will
be able to respond to all the needs of its clients in a faster, more
efficient and effective way and will reduce the weight that the
old commercials had in the sector.Artificial Intelligence in
CRM
Customer Relationship Management or better known as CRM is
the software that has been helping companies for years to
improve their relationship with their customers. According to
the results obtained by a study by Forrester and published by
the digital Merca2.0, these solutions increase the productivity
of the teams by 50%, increase sales by 5% and the cost of
customer service is reduced by 40%.
Dignum (2018) realized that the information extracted by these
tools can be further benefited if we integrate Artificial
Intelligence in them, resulting in three great advantages of
applying AI to CRM. Reduces the time of the most mechanical
tasks. Thanks to the incorporation of Artificial Intelligence in
CRM systems, workers are freed from the routine efforts such
as entering data in databases, answering emails, managing
invoices or scheduling meetings, among others. According to
Salesforce estimates published in the CRM Today portal, up to
four hours a week can be saved in repetitive tasks.

Kokina & Davenport (2017) realized that AI is the best virtual
assistant. In line with routine tasks that take time and effort
from workers, Artificial Intelligence is much faster and more
efficient when it comes to generating immediate and automatic
responses, emails, functions related to data collection and
monitoring thanks to its ability to obtain customer information
such as behavior on the site or demographic data. In addition to
the speed in obtaining customer data, the AI applied to CRM
collects quality and very specific information regarding gender,
geographic location, and purchase history, behavior on the web
or other sections that allow companies to develop content very
specific to develop personalized messages that have a better
impact. Which translates into a higher percentage of success of
the sales strategy and a closer loyalty of the customers.
Kokina & Davenport (2017) realized that with a unified
customer success platform, Salesforce is the heart of key
customer data for most businesses. Bringing Salesforce
Einstein, an intelligence layer, to the platform that can also
easily integrate additional data from third-party systems avoids
the problem of customer data silos. To eliminate the need to
build artificial intelligence models from scratch, Salesforce
Einstein brings sophisticated data analytics and pre-built
predictive models to business process and application creators,
without complexities or cumbersome processes. The Salesforce
CRM and its AI capabilities foster the ideal environment for
creating diverse applications and experiences, not just for
customers, but also for everyone in the organization. Using the
same data model, business logic, and experience layer that
powers CRM, combined with the simple point-and-click way to
build applications, both IT and business users can leverage an
intelligence boost to provide experiences with artificial
intelligence and a 360-degree view of the customer to anyone.
Kormalev, et al, (2018) realized that using a consumer shopping
experience as an example, the AI models embedded in the
Salesforce CRM system's personalization engine take into
account the catalog that a given buyer sees and the context of

how the merchant interacts with that buyer, and then classifies
each product for that buyer in terms of relevance to search
results, achieving the most specific and personalized results of
all time. This is done at scale, constantly refining consumer
recommendations based on learning.
Acay, Sonenberg & Tidhar (2019) found that the data for such
recommendations includes real-time and historical data on
clicks from various sources. By selecting a challenging
champion model algorithm, each experience becomes more
personalized and intelligent because the algorithm "learns" from
past data and can produce results with greater precision. The
bottom line of accurate recommendations is that customer
conversions skyrocket and your inventory value increase.
Perez, et al, (2018) stated that these advanced artificial
intelligence algorithms can also improve both employee and
customer experiences using tools such as speech recognition,
sentiment analysis, intent, content summarization through
natural language processing, and data table-based responses.
The key to the intelligence layer built into Salesforce CRM is
the point-and-click capabilities to enable AI for any application,
without the need for a team of data scientists on standby.
Everyone in the organization, from developers to business
users, can collaborate to create the right customer experience
using rapid, iterative prototyping.
Hassabis, et al, (2017) told that the low-cost code-free
programming environment of the Salesforce platform brings
traditionally non-tech teams on board, such as customer service
and marketing teams that can be actively involved in creating a
truly intelligent customer experience. This enables IT to take a
coaching role to empower teams across the organization to
create their own artificial intelligence solutions.Artificial
Intelligence in Banking
Without a doubt, artificial intelligence in the banking and
finance sector is one of the most interested in the development
of this technology, due to the enormous volume of data it holds.
Not surprisingly, it is the world's leading investor in artificial

intelligence.
Hassabis, et al, (2017) told that figures from the European
Commission reflect a strong investment imbalance between
Europe and its main competitors. Private investment in artificial
intelligence in Asia and North America exceeds 6,500 and
12,000 million euros respectively. This contrasts with the 3,500
million euros that the European financial sector allocates to the
development of this technology. The aforementioned Bank of
Spain report indicates that Asian and American companies have
the advantage of having the infrastructure and the volume of
data necessary for the development of artificial intelligence in
the banking and finance sector.
Chan & Yuan (2019) found that according to a report published
by the World Economic Forum and Deloitte, artificial
intelligence will have a disruptive nature in the financial sector
“weakening the ties that have held together the components of
existing financial institutions.”
Varlamov, et al (2019) noted that the new operating models will
favor those institutions focused on the sophistication and scale
of the data, rather than on capital. It is expected that the entities
are going to turn to the relationship with their clients, advisory
services and collaborative solutions.
On the other hand, a new battlefield is presented, in which the
rules of the traditional financial and banking sector will be
relegated by automated systems.AI based Chatbots in Financial
Institutions
Wamba-Taguimdje, et al (2020) noted that these virtual
assistants can respond to a multitude of requests from users.
This allows us to provide an automated customer service
available 24 hours a day. Likewise, it also makes it possible to
collect in an automated way the interactions carried out by the
users themselves, to later improve the quality of the service.
This automation process will not only benefit the execution of
customer relationships, but also the execution of internal
processes. Consequently, it is expected that the sector will see
its productivity increase significantly in the coming years.

Customization of Products
Thanks to the information gathered about each client, it will
also be possible to create more personalized experiences, as
well as to adjust the conditions and characteristics of the
products and services offered to each client.Credit Rating,
Fraud Detection and Risk Management
applied to predictive analytics is going to make a considerable
change in credit rating and risk management systems. On the
one hand, risk analysis will gain in reliability, which allows the
conditions for granting credit or making the investment to be
adjusted more precisely.
In addition, the analysis of unstructured data opens the door to
granting credit to previously ineligible clients.
in the financial sector is extremely effective in preventing fraud
in the use of credit cards. This problem has been increasing
over the last few years; following the evolution of electronic
commerce. Analytical systems can anticipate a change in user
behavior patterns, and issue alerts or even carry out preventive
measures, to avoid fraud.
On the other hand, these same systems can also be used to
detect practices that may be involving money laundering.
Finally, artificial intelligence algorithms are already being used
for the execution of investment strategies. This technology is
especially valued in those strategies that require high
refinement, together with a high speed of execution.
Montani & Striani (2019) understand that decision-making is
thus automated, under the parameters included in the algorithm,
which is in charge of executing the operations. Ultimately,
artificial intelligence is completely changing the rules of the
game within the banking and finance sector.Present and Future
of AI in Financial Sector
AI can drive efficiency of operations in many area of finance
like trading or risk management. While some uses are pertinent

to certain zones within monetary services or they can easily
exploit by anyone.Risk Management
AI has proven its worth when it comes for fraud detection and
security. Common fraud detection ways like analyze the
structured data with set of rules is one example. Let`s say a
company set threshold for wire transfer of $20,0000, then any
transaction that goes above or exceeds this threshold may
flagged for investigation.
Montani & Striani (2019) understand that this type of tests often
makes false positive results and needs much effort. Perchance,
it needs more enhanced techniques that cybercrime scammers
change frequently. In this way, systems must made smarter.
Bogachov, et al, (2020) stated that with new learning methods
for machine to understand algorithms such as deep learning or
reinforcement learning, new features can help systems to
become dynamic and self-aware in some way. With help of
cognitive analytics, fraud detection models can become smart,
accurate and robust.
Shook, et al, (2019) mentioned that if an intelligent system
removes somewhat that it fixes as possible scam, and a human
being controls that it is not scam due to X, Y, and Z, the
computer acquires from perception of humans, and it will not
send any same detection.

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The references are not acceptable without a URL which is a
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