the rise of tificial intelligence

Journal of Engineering Research and Reports
Volume 25, Issue 3, Page 85-103, 2023; Article no.JERR.100807 ISSN: 2582-2926
Rise of Artificial Intelligence in Business and Industry
Jasmin Praful Bharadiya a*, Reji Kurien Thomas b and Farhan Ahmed c
a University of the Cumberlands, United States.
b Swiss School of Business and Management, Geneva, Switzerland.
c Mid Sweden University, Sweden.
Authors’ contributions
This work was carried out in collaboration among all authors. All authors read and approved the final manuscript.
Article Information
DOI: 10.9734/JERR/2023/v25i3893
Open Peer Review History:
This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers,
peer review comments, different versions of the manuscript, comments of the editors, etc are available here:
https://www.sdiarticle5.com/review-history/100807
Received: 27/03/2023
Accepted: 29/05/2023 Review Article
Published: 05/06/2023 ABSTRACT
The ongoing development of business and the most recent advances in artificial intelligence (AI)
allow for the many business practices to be improved by the capacity to establish new forms of
collaboration, which is a significant competitive advantage. This rapidly developing technology
enables to offer brand services and even some new forms of business interactions with consumers
and personnel. The digitalization of AI concurrently emphasized for businesses that they need
concentrate on their present strategies while also routinely and early pursuing new chances in the
market. Not only in business but also in different industry sectors, Al techniques are being used and
revolutionized different industry sectors. This review focuses on the application of AI techniques in
business and different industries.
Keywords: Artificial intelligence; business; healthcare; pharmaceutical; industry; machine learning ML.
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*Corresponding author: Email: jasminbharadiya92@gmail.com;
J. Eng. Res. Rep., vol. 25, no. 3, pp. 85-103, 2023
Bharadiya et al.; J. Eng. Res. Rep., vol. 25, no. 3, pp. 85-103, 2023; Article no.JERR.100807 1. INTRODUCTION
The market for cognitive and AI systems has
been progressively developing over the course of
Artificial intelligence (AI) is causing a revolution
the previous several years, as seen by the global
in business as well as the economy and society
expenditure of $24.0 billion on such systems in
as a whole by changing the interactions and
2018. According to IDC's 2019 projections, this
relationships that exist between stakeholders and
investment will reach 77.6 billion dollars in 2022
individuals. The roots of artificial intelligence may
[7]. Researchers and practitioners of AI will
be traced back to mythology from ancient benefit from having a comprehensive
Chinese, Greek, and other civilizations, where it
understanding of what has been explored and
was believed that automatons have true brains
used in a variety of business domains (for
and were able to think and experience emotion.
example, manufacturing to services) and in a
This is where the concept of artificial intelligence
variety of disciplinary subjects, such as
first emerged. However, according to Nilsson, the
marketing, tourism, management, sociology,
term "artificial intelligence" was first used in
psychology, and so on. This is due to the fact
public during a workshop held in 1956 at
that research on commercial applications of AI
Dartmouth College in the United States [1]. Since
typically requires a perspective that draws from then, researchers from a variety of oth er d is
ciplines. With the use of such in-depth
academic fields have made contributions to the
knowledge, researchers will be able to prioritize
field of artificial intelligence. Researchers in
their study topics, and practitioners will be able to
business management have been looking at how
make prudent investments in essential business- AI affects customers, businesses, and related AI aspects [8].
stakeholders in an increasingly automated and
interconnected business world [2]. Computer
It is interesting to note that several academics
scientists have created sophisticated deep
have made an attempt to carry out a
learning algorithms [1,3]. Social scientists have
comprehensive literature review on the use of AI
been debating the ethical and legal ramifications
in business. For example, Côrte-Real, Ruivo, and
of AI. Despite this, most of these advancements
Oliveira [9] carry out a methodical mapping of the
in AI research have been carried out in isolated
diffusion stages of business intelligence and
enclaves with relatively little collaboration
analytics (BI&A) deployment and make a
between different fields of study. In the same
recommendation for more research in the post-
vein, it has not been easy to settle on a single
adoption stages, which were mostly disregarded
definition of AI that is generally recognized by
at the time of the study's original publication.
most people. The many concepts of AI systems
Moro, Cortez, and Rita [10] did a literature study
have been organized by Russell and Norvig [4]
that focused on business intelligence in banking
into four categories along two dimensions: the
between the years 2002 and 2013. Business
human performance-rationality dimension and
intelligence uses AI algorithms for predictive
the reasoning-behavior dimension. Systems that
analysis. Tká and Verner [11,12] noticed,
act like people, systems that think rationally,
throughout the course of their analysis of 20 systems that think like humans, and y e ars'
worth of research on the use of artificial
systems that think like humans are all examples
neural networks in business, that the majority of of these types of artificial intelligence. the
pa pers they looked at mentioned expert Natural language processing, knowledge
systems with applications. This was one of the
representation, automated reasoning, the use of
findings from their investigation. As a final step,
the stored information to answer questions and
Duan, Edwards, and Dwivedi [13] conduct an
draw new conclusions and machine learning
analysis of relevant papers from the International
should all be included in AI systems [4-6].
Journal of Information Management. Their goal is Natural language processing, knowledge
to identify issues and challenges connected to
representation, automated reasoning, and the
the application of artificial intelligence (AI) for
use of the stored information to answer questions
decision-making in the era of big data, as well as
and draw new conclusions should also be
to provide theoretical advancement and AI
included. Because of these skills, AI systems will
implementation. While these projects do provide
soon be able to engage in natural language with
useful information on the latest advancements in
people and other machines. The global spread of
business research and artificial intelligence, the
research into fresh applications of AI, on the
majority of their focus is on certain applications
other hand, has not been hindered by the
(such as BI&A or decision support systems) or
absence of a definition that is generally
domains. The purpose of this study is to address recognized.
this vacuum by providing an overview of the 86
Bharadiya et al.; J. Eng. Res. Rep., vol. 25, no. 3, pp. 85-103, 2023; Article no.JERR.100807
present research on artificial intelligence (AI) in
amounts of data, which can be difficult for
business. This will be accomplished by doing a
humans to comprehend without the assistance of
comprehensive analysis of the history and state-
machine learning [18]. For instance, if you are
of-the-art research on AI, as well as by projecting
the manager of a manufacturing company, the
future trends in order to make useful ideas for
equipment in your facility is probably connected
further research on the subject.
to the network. A centralized location receives a
steady flow of information regarding the
1.1 Artificial Intelligence
functionality, production, and other aspects of an
organization's connected devices. Unfortunately,
It is necessary to provide a definition of the word
there is far too much information for a person to
"artificial intelligence" before analysing the ways
ever be able to sort through all of it, and even if
in which AI technologies are influencing the
they did, it is probable that they would overlook business sector. The phrase "artificial
the majority of the patterns [19]. Machine
intelligence" is a generic one that may be used to
learning can detect trends and abnormalities in
describe any variety of computer software that
real time. A machine-learning system can alert
participates in tasks that are analogous to those
decision-makers that a manufacturing plant
performed by humans, such as learning, machine is underperforming and needs
planning, and problem-solving. To refer to certain
preventative maintenance [20,21]. Machine
applications as "artificial intelligence" is like to
learning is vast. Deep learning emerged from
referring to a car as a "vehicle"; while this is
artificial neural networks, a network of artificial
technically accurate, it does not encompass any intelligence "nodes" [22].
of the nuances of the topic. We need to delve
further if we want to learn which kind of artificial 1.1.2 Deep learning
intelligence is most commonly used in business [14, 15].
Deep learning uses neural networks for nonlinear
thinking. Fraud detection requires deep learning. 1.1.1 Machine learning
It does this by analyzing many factors [23]. Self-
driving automobiles must concurrently identify,
One of the most prevalent forms of artificial
analyze, and respond to multiple elements. Deep
intelligence (AI) currently being developed for
learning algorithms assist self-driving cars
use in business is known as machine learning.
contextualize sensor data including object
The primary goal of machine learning is to
distance, speed, and 5-10-second predictions. A
process vast volumes of data in a relatively short self-driving automobile calculates all this
amount of time. The algorithms that make up
information at once to decide whether to change
these sorts of artificial intelligences give the
lanes [24]. Deep learning is promising in
impression that they "learn" over time [16,17]. If
business and may be utilized increasingly. Deep
you give an algorithm for machine learning
learning models advance with additional data,
additional information, the modeling it produces
but older machine-learning algorithms plateau.
should get better. The Internet of Things and
Deep learning models are more scalable,
linked gadgets are producing ever-increasing
detailed, and independent [25]. Machine learning Artificial Intelligence Deep learning
Fig. 1. Artificial intelligence 87
Bharadiya et al.; J. Eng. Res. Rep., vol. 25, no. 3, pp. 85-103, 2023; Article no.JERR.100807 1.2 AI in Business
contextualize the data for your company's
decision-makers to better comprehend energy
Artificial intelligence supports human intelligence
use and maintenance needs [31].
and inventiveness rather than replacing it. AI can
process and analyze large amounts of data 2.2 Cyber Security
quicker than a human brain, but it struggles with
basic tasks. Then, AI software may offer
Husain said artificial intelligence is essential for
synthesized actions to humans. Thus, AI can
finding computer network security weaknesses.
help us predict the outcomes of each action and
AI systems can detect cyber-attacks and other
simplify decision-making [26,27]. Amir Husain,
cyber threats by analyzing data trends. It may
creator and CEO of Spark Cognition, called
pinpoint the source of a danger in your data and
artificial intelligence the second coming of
prevent future threats. Your infrastructure will
software. “It’s software that makes its own benefit from AI's vigilant and constant eyes
decisions and acts in situations the programmers [32].“Because of scale and increasing
didn’t anticipate. AI can make more decisions complexity, you real y can’t have enough cyber
than traditional software” [28]. AI is useful in
security experts to look at these problems,”
numerous sectors, from helping visitors and staff
Husain said. “AI is becoming more important
navigate a corporate site to monitoring a wind here” [33].
turbine to forecast maintenance [29]. 2.3 Managing Customers
2. AI APPLICATIONS IN BUSINESS
AI is also revolutionizing CRM systems. Sales
There are several uses for artificial intelligence in
force and Zoho need frequent human updates. AI
business, but most of them are focused on
turns a CRM system into a self-updating, auto-
fostering expansion. Companies are discovering
correcting relationship management system [34].
novel methods to improve company performance
by integrating artificial intelligence (AI) and  Increasing Productivity Through the
machine learning (ML) [30]. The following is a list Automation of Processes
of some of the commercial benefits of AI.
 Improving either the rate at which service is provided or its consistency. 2.1 Machine Learning
 Utilizing information gleaned from customers
as a basis for decision-making
Massive data systems employ machine learning. 
Smart energy management systems take data
Discovering new markets for existing and
from asset sensors. Machine-learning algorithms
potential products and services % of AL in business 45% 40% 35% 30% 25% 20% % of AL in business 15% 10% 5% 0% Management Quality Customer care Cybersecurity Fruad controll Detection Fig. 2. AL in business 88