Business Analytics Report: AI and ML Applications and Analysis

This report from the Department of Business Analytics delves into the applications of Artificial Intelligence (AI) and Machine Learning (ML) within the realm of business and management. The initial sections of the report discuss the subfields of AI and ML that are most pertinent to business applications, including robotics, expert systems, and deep learning, along with their potential extensions. The report then provides background information on the data used, focusing on the labor dependency ratio of the UK and Switzerland from 1991 to 2020. The core of the analysis involves descriptive statistics, graphical representations, and statistical analysis of the data to identify trends in the labor dependency ratio. The research question addressed is the trend of labor dependency ratio in UK and Switzerland from 1991 to 2020. The report concludes with a summary of findings and their implications for business decision-making.

DEPARTMENT OF
BUSINESS ANALYTICS

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
Subfields of AI and ML that is suitable for business and management......................................3
Synthesising the existing literature dealing with application of the AI and ML to business and
management................................................................................................................................5
Potential outlook for extension of existing AI and ML application............................................6
PART 2............................................................................................................................................6
Brief background information of data.........................................................................................6
Focus of analysis and research question.....................................................................................6
Key descriptive statistics.............................................................................................................7
Graphical analysis.......................................................................................................................9
Statistical analysis.......................................................................................................................9
Conclusion................................................................................................................................11
CONCLUSION..............................................................................................................................11
REFERENCES..............................................................................................................................13

INTRODUCTION
Business analytics is being defined as the process through which business use different
statistical methods and latest technology for the analysis of data to provide some insight and
assist in decision making. For any business to run successfully it is essential that they undertake
business analytics because no decision can be taken without an analysis of the data. The present
study will start by discussing the different technologies like artificial intelligence and machine
learning. The report will highlight the subfields of AI and ml along with the extension of
application of the existing AI and machine language. In addition to this the present study will
also highlight the analysis of data by using different tools for stop this will include statistical
analysis and interpret the results in order to take effective decisions for the betterment of the
business.
PART 1
Subfields of AI and ML that is suitable for business and management
The current modern business world involves many different technologies which can
assist business and taking effective decisions (Di Vaio and et.al., 2020). In this highly
technologically advanced world there are many technology been developed in order to assist
stick and carrying on their operations effectively. This includes two major technologies that is
artificial intelligence and machine learning. For the effective business and its management both
this technique are very helpful. The artificial intelligence is a part of computer science which is
being concerned with designing of the intelligent computer systems. These computer system
associate with the intelligence in the human behaviour. In simple words the artificial intelligence
requires many features like human behaviour such as understanding the language effective
reasoning solving problem and many other features. Use of fields of AI which are assistive and
suitable for effective business and its management. There are many different subsets of artificial
intelligence and machine learning is also a major type of subset of artificial intelligence. Other
than machine learning the major subfields of AI are as follows-
Robotics- The major subfield of AI assisted to businesses and management is the robotics
(Lavinia-Mihaela, 2019). This field of robotics emerged as a sizzling development within the
field of AI. Robotics is a type of interdisciplinary field related to science and engineering which

mixed with mechanical and electrical engineering all stop the robotic determines the designing
and operation and usage of robots. This is a computer system for the control and having
intelligent outcomes. These robots are effectively used in order to order them and make them
work as a human performs. Many of the tasks are being done with help of these robots and this is
very assistive as the person just has to direct the robot and they will do the work (Fanti,
Guarascio and Moggi, 2020). These robots are also programmed machine that can perform a
series of actions automatically as the human being performs. The artificial intelligence can be
applied to robotics in order to make them intelligent so that they can perform the task as per the
order of the human beings by them. For the effective working of the robot algorithms are very
necessary and this will assist them in performing more complex tasks easily. In the present days
both AI and machine learning is being applied over the robots in order to make the robot
intelligent so that they can socially interact just like human beings do.
Expert system- In addition to robotics another field of AI is expert system and this was
considered as the first successful model of software. The expert system is defined as a computer
system which mimics the decision making intelligence of the human being. This intelligence is
being copied by driving knowledge from the knowledge base and by implementing reasoning
and insights over the decision and then decision is being undertaken. Effectiveness of expert
system is completely based on the knowledge accumulated within the knowledge base for stop if
the more information is collected in the knowledge base then the expert will be more effective in
taking decisions in enhancing efficiency. For instance the expert system provides suggestion of
spelling errors within the Google search engine and this is an example of expert system. This
subfield of AI is built in order to deliver complex problem with help of effective reasoning and
with help of body of proficiencies which is expressed in the "if- then" rule. This is a traditional
rule which states that if this will happen then what will be the result. The key feature of this
expert system under AI is that it is reliable, easily understandable and extremely responsive.
Machine learning- Machine learning is being defined as the application of AI which assist the
system in automatically learns and improves by the experience. The major focus of ml is on
development of computer programs which can have access to data and use it effectively by
themselves. Machine learning has only a single subfield which is deep learning (Roselli,
Matthews and Talagala, 2019).

Deep learning- Deep learning is a subset of machine learning which AIMS at providing the
ability to the machine in order to perform task in the manner of humans perform. Deep learning
provides a direction to the agent in order to copy the human brain. This concept can be both
supervised and unsupervised in order to train the AI agent. This deep learning is being
implemented with the help of neutral network architectures which is also called the deep neural
network. This is the primary technology which is behind the self driving cars image recognition
and other concepts (Armour and Sako, 2020). These deep learning algorithms work over the
deep neural network which is called deep learning. In this the first layer is called the input layer
and the last one is the output. Between these two layers all the layers are called the hidden layers
for stop in this deep neural network there are many different multiple hidden layers and every
layer is composed of some neurones these neurones are connected with the each layer. The
working of deep learning starts with the input layer which receives the data and then the
neurones transfer these data to the hidden layers. For the digital layer perform the mathematical
operations over the data and then the output is being forwarded to the output layer. This output
layer is the data which is being used by the user as the output.
Synthesising the existing literature dealing with application of the AI and ML to business and
management
In accordance with the views of Di Vaio and et.al., (2020) AI is applied in every type of
the business and management. This is pertaining to the fact that AI is useful for the business in
effective and efficient manner. In the field of banking and finance as well the AI is being used.
The banks and other financial institution make use of AI in order to detect the fraudulent
activities of the business. The AI is trained to determine that whether the transaction is based on
the data or not. in addition to this Fanti, Guarascio and Moggi (2020) criticizes the fact that the
retail sector companies also makes use of the AI in their effective working. Here the retail stores
and many website of retail companies offer the option of ‘chat’ function where the consumer can
talk with the customer support representative. In most of the cases it is the AI chat bots who are
capable of understanding the human conversation and assist the customers.
In contrast to this the machine learning is also has a good and effective application within
the business and management. As per the views of Lavinia-Mihaela (2019) the concept of
machine learning is being used by the doctors and other medical people in order to more
accurately diagnosis the patient and treats them in effective and better manner. In addition to this

ML is being used by the retail companies in order to personalizing the customer experience.
With help of ML the companies can have deep data mining, continuous learning algorithms and
natural language processing. All these will assist the retail companies in effectively manage the
data of consumer and keep them happy and satisfied.
Potential outlook for extension of existing AI and ML application
The potential outlook for the application of the existing AI and ML is that the companies
can effectively undertake the use of these in the hiring process as well. This is necessary because
of the reason that when the companies will have the record of the past applicant then they will
effectively be in position to contact them in case of vacancy. Along with this many companies
can effectively use this ML and AI in order to detect the frauds being undertaken within the
company. This will be assistive to the company as this will improve the working condition of the
company and will increase its profitability. In addition to this ML can also be applied in the IT
operations of the company (5 practical business applications for machine learning, 2021). This
is pertaining to the fact that when the IT is an important part of the company and if this will be
using effective ML and AI then this will result in better and effective operations of the company.
PART 2
Brief background information of data
The present data is based on ILO that International Labour Organization which is a
United Nation agency which is mandate to social and economic justice with help of setting
international labour standards. This was founded in the year 1919 and is the first and oldest
agency of UN and is headquartered in Geneva in Switzerland. The major function of ILO is to
develop and promote the standard relating to the national legislation in order to protect and
improve the working condition and way of living of labours. The present data involves the data
relating to labour dependency ratio which is an age- population ratio of those people who are
typically not in the labour force and those who are in the labour force. This ratio is used to
measure the pressure over the productive population. The current study will involve the analysis
of the labour dependency ratio of UK and Switzerland.
Focus of analysis and research question
For the research to be successful the most essential aspect of the study is to have some
base. In order to make the study effective the most essential aspect is to decide for some research

question or objectives relating to the study. This is pertaining to the fact that when the this
question or the objective will provide a base to the research and the study will be completed in
effective and efficient manner. The research question for the present study is
What is the trend of labour dependency ratio in UK and Switzerland since 1991 to 2020?
Key descriptive statistics
Data
Year Switzerland UK
1991 0.8 1.17
1992 0.82 1.22
1993 0.84 1.26
1994 0.87 1.25
1995 0.87 1.24
1996 0.87 1.22
1997 0.88 1.19
1998 0.85 1.17
1999 0.84 1.15
2000 0.85 1.13
2001 0.83 1.12
2002 0.83 1.11
2003 0.85 1.1
2004 0.86 1.09
2005 0.86 1.07
2006 0.84 1.07
2007 0.82 1.07
2008 0.79 1.07
2009 0.8 1.11
2010 0.85 1.12
2011 0.82 1.13
2012 0.82 1.12
2013 0.82 1.11
2014 0.81 1.07
2015 0.81 1.05
2016 0.8 1.04
2017 0.8 1.03
2018 0.8 1.02
2019 0.81 1
2020 0.83 1.02

Particular Switzerland UK
Mean 0.833 1.112
Standard Error 0.005 0.013
Median 0.83 1.11
Mode 0.85 1.07
Standard
Deviation 0.026 0.071
Sample Variance 0.001 0.005
Kurtosis -1.065 -0.227
Skewness 0.131 0.623
Range 0.09 0.26
Minimum 0.79 1
Maximum 0.88 1.26
Sum 23.32 31.13
Count 28 28
With the help of the above data and the descriptive analysis of the data it is clear that the mean or
the average labour dependency ratio of Switzerland is 0.833 whereas of UK is 1.112. This
dependency ratio reflects the population breakdown of the country that is how many dependents
is there over the working age people. It is very important for the country to analyse this ratio as if
the dependent will be more as compared to the working group of the company then this will
affect the working condition of the people. In addition to this if the country will be having more
the dependency ratio then this will affect the country and its development. With the above
average it is clear that Switzerland is having low dependency ratio and this is good for the
country. On the other hand, the average dependency ratio of UK is more as compared to
Switzerland for the year from 1991- 2020.

Graphical analysis
From the above graphical presentation it is clear that the labour dependency ratio of UK is more
as compared to Switzerland. This is evident that the labour dependency ratio of Switzerland is on
an average near to 0.8. Whereas in the case of UK the labour dependency ratio is on an average
near to 1.2. In case of Switzerland the trend of labour dependency ratio is not much fluctuating.
On the other hand, in contrast to this for UK it is a little fluctuating. This can be visible in the
graph that from 1991 it was a little higher then after 1995 it decreased to a little extent.
Thereafter, again this ratio increased a little bit. Furthermore, there was a decrease but on an
average the ratio was between the range of 1- 1.2.
Statistical analysis
Statistical analysis is being defined as the application of different types of statistical tools and
analysing the data with help of these tools. In addition to this the application of the statistical tool
is very essential for the effective working and successful completion of the research. The
statistical tools applied in the analysis of the data relating to the labour dependency ratio are the
use of correlation and regression.
Regression
Regression
Statistics
Multiple R 0.57
R Square 0.33

Adjusted R
Square 0.31
Standard Error 0.02
Observations 30
ANOVA
df SS MS F
Significance
F
Regression 1 0.006180801 0.0061808 13.772423 0.001
Residual 28 0.012565866 0.0004488
Total 29 0.018746667
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 0.6042 0.0613 9.8530 0.0000 0.4786 0.7298 0.4786 0.7298
X Variable 1 0.2033 0.0548 3.7111 0.0009 0.0911 0.3155 0.0911 0.3155
From the help of the above regression analysis it is clear that there is correlation between both
the variables of 0.57 of 57 %. This implies that the correlation among the two variables is
moderate. In addition to this the R square was 0.33 and this implies that any changes within the
independent factors will cause a change of 33 % in the dependent factor as well. Further with
help of the ANNOVA table it is clear that significance value is 0.001 which is less than then
standard value that is p= 0.05 then the alternate hypothesis is being selected. Hence, in the
present case of labour tendency ratio it was evaluated there is a significant relation between the
ratio of Switzerland and UK. With the analysis of the significance value it is clear that the
alternate hypothesis is being selected rejecting the null hypothesis. The use of regression analysis
is being undertaken because of the reason that it is a reliable method in identifying the variables
which may have impact over the topic of interest. This is used because of the reason that it is a
great tool for predictive analytics and forecasting research. Hence, this is a reliable method for
the effective analysis of the data and draw relevant conclusion from it.
Correlation
Column 1 Column 2
Column 1 1
Column 2 0.574 1

Interpretation- the above table depicts the correlation among the labour dependency ratio in
Switzerland and in UK. The above table evaluated that correlation among both the variables is
0.574 or 57.4 %. With this it can be evaluated that the correlation among both these variable is
moderate. There might be correlation between this data as if the labour dependency ratio of
Switzerland will decrease then there are possibilities that by applying those strategies UK can
also try to decrease the labour dependency rate with help of application of those strategies. The
method of correlation is being applied within the research as this will assist researcher in
identifying the relationship between the test scores and other measures. This is a tool which
assists the company in identifying the linear association between both the variables.
Conclusion
In the end from the above part it is clear that analysis of the data is very important and this will
assist the company in taking proper decision. This data analysis is also useful for the countries as
well as this will assist them in identifying the current trends and compare it with other countries
and then take decision for the betterment of the country. With this it was evaluated that statistical
tools are very essential for the effective analysis of the collected data. This is pertaining to the
fact that if the data will not be analysed in proper manner then the research outcome will not be
analysed. Thus, it is essential to undertake the appropriate statistical tools so that research
objectives or the question can be answered appropriately.
CONCLUSION
The above report summarised that the business analytics is being defined as the
undertaking the data in order to evaluate and analyse the data. This analysis is being undertaken
with the intention of analysing the present situation and take effective decision for the business.
The report started by a detailed discussion relating to use of AI and ML in the business and
management. With the analysis it was evaluated that subfield of AI are robotics, expert system
and for ML is the deep learning. Further it was analysed that the application of AI and ML is in
almost all the business and industries like retail, banking and others. Further the next part
evaluated and analysed the data relating to the ILO which emphasised over labour dependency
ratio of Switzerland and UK with help of statistical tools like regression, correlation and others.

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