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An Analytical review on the Impact of Artificial Intelligence on the Business Industry: Applications, Trends, and Challenges

March 2024
IEEE Engineering Management Review

DOI:10.1109/EMR.2024.3355973

Authors:

Kuldeep Gurjar

Kangwon National University

Hasnan Baber

American University of Sharjah

Maidul Islam

Keimyung University

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The integration of artificial intelligence (AI) in business processes has revolutionized many industries by automating tasks, improving decision-making processes, and enhancing customer experiences. This review paper examines the impact of AI in business areas, including its applications, challenges, limitations, current trends, and future work. The paper begins with defining the importance of AI in business, followed by an overview of its applications in various sectors such as customer service, marketing, finance, healthcare, manufacturing, logistics, and human resources. The advantages and benefits of AI implementation are explored, along with examples of successful AI implementation and changes in business processes. The challenges and limitations of AI technology, such as ethical concerns, data privacy and security issues, technical expertise and knowledge, and high implementation costs, are explained. Current trends in AI integration, such as the integration of AI with other technologies, the growing demand for AI skills, and the development of more advanced and sophisticated AI algorithms, are presented.

Flow chart for screening and selection of articles

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Cost decrease and revenue increase from AI adoption Source: McKinsey and company, 2021

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An Analytical review on the Impact of Artificial Intelligence on

the Business Industry: Applications, Trends, and Challenges

Kuldeep Gurjar

1Department of Computer Science, Kangwon National University, Chuncheon, Republic of Korea

2The University of Suwon, Hwaseong, Republic of Korea

Email: dkekuldeep@gmail.com

Anshika Jangra

Department of Research and Clinical Coordination,

CureTherapeutics Inc., Suwon, Republic of Korea

Email: anshika.jangra@curetherapeutics.com

Hasnan Baber*

School of Business Administration,

American University of Sharjah, Sharjah, United Arab Emirates

Email: hbaber@aus.edu

https://orcid.org/0000-0002-8951-3501

*Corresponding Author

Maidul Islam

Department of International Business, Keimyung Adams College (KAC),

Keimyung University, Daegu, Republic of Korea

Email: maidul@kmu.ac.kr

Shabnam Abdulkasem Sheikh

International College, The University of Suwon,

Gyeonggi-do, Hwaseong-Si, Republic of Korea

Email: shabnam@suwon.ac.kr

Abstract

The integration of artificial intelligence (AI) in business processes has revolutionized many

industries by automating tasks, improving decision-making processes, and enhancing customer

experiences. This review paper examines the impact of AI in business areas, including its

applications, challenges, limitations, current trends, and future work. The paper begins with

defining the importance of AI in business, followed by an overview of its applications in various

sectors such as customer service, marketing, finance, healthcare, manufacturing, logistics, and

human resources. The advantages and benefits of AI implementation are explored, along with

examples of successful AI implementation and changes in business processes. The challenges and

limitations of AI technology, such as ethical concerns, data privacy and security issues, technical

expertise and knowledge, and high implementation costs, are explained. Current trends in AI

integration, such as the integration of AI with other technologies, the growing demand for AI

skills, and the development of more advanced and sophisticated AI algorithms, are presented.

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

K. Gurjar, A. Jangra, H. Baber, M. Islam and S. A. Sheikh, (2024). An Analytical Review on the Impact of

Artificial Intelligence on the Business Industry: Applications, Trends, and Challenges. IEEE Engineering

Management Review. https://doi.org/10.1109/EMR.2024.3355973

The review concludes with recommendations and predictions for the future of AI in business

areas and proposes strategies for successful AI implementation while reflecting on ethical and

social considerations. Our analysis in this paper points to various open research areas and

improves the understanding of AI in the business field.

Keywords: Artificial Intelligence; Business; AI; AI and marketing; AI and healthcare.

1. Introduction

AI has become an integral part of business

operations, providing new opportunities for

growth, innovation, and improved decision-

making. The use of AI in business areas has

grown rapidly in recent years, and its

potential to transform industries is now

becoming a reality. The ability of AI to

automate tasks, analyze vast amounts of

data, and provide insights has made it an

essential tool in various sectors, such as

customer service, marketing, finance,

healthcare, manufacturing, logistics, and

human resources [1]. As AI technology

continues to evolve, it is becoming

increasingly important for businesses to

understand the opportunities and challenges

associated with its implementation.

The impact of AI in business has been the

subject of extensive research in recent years.

A growing body of literature has focused on

the various applications of AI in business,

including automation, predictive analytics,

and natural language processing [2].

Furthermore, scholars have explored the

impact of AI on business strategy,

organizational structure, and the labor

market. Strategy emerges primarily from

enabling technologies or general-purpose

technologies. Therefore, strategy is

essentially derived from the underlying AI

structure [3] While some studies have

shown that AI can increase efficiency and

productivity [4-9], others have highlighted

potential issues [10]. Various theoretical

models and frameworks have been put forth

to guide the application of Artificial

Intelligence (AI) in the business sector.

Kitsios (2021) underscores the importance of

aligning AI tools strategically with

organizational goals [11]. Mikalef (2019)

introduces the concept of AI capability,

encompassing dimensions like data,

technology, and human resources [12].

Sestino (2022) provides a systematic

classification of AI research, emphasizing

implications, applications, and methods [2].

However, there is a gap in the existing

research within the business domain,

particularly in offering holistic analysis

strategies for the effective implementation of

AI in business.

The integration of AI in business areas has

the potential to revolutionize various

industries and transform the way businesses

operate. This review paper provides a

comprehensive overview of the impact of AI

in business areas and serves as a useful

resource for understanding the

opportunities and challenges associated

with AI implementation. This review paper

also serves as an important resource for

scholars, researchers, and business

practitioners who seek to gain a deeper

understanding of the current state of

knowledge on the impact of AI in the

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

business industry. Moreover, we provide a

comprehensive overview of the existing

literature on this topic, analyzing the

different applications of AI in business

domains, the background of AI in business,

current trends, and potential challenges

associated with AI adoption. The importance

of this review paper lies in its ability to

synthesize and critically analyze the findings

of various research papers, providing a

broad perspective on the impact of AI on the

business industry. The paper is also valuable

as it identifies gaps and areas for further

research, providing a roadmap for future

studies in this area.

The rest of the paper is organized as follows:

Section 2 presents a literature review,

explaining the background and recent

research works. Section 3 presents various

application areas of AI in business, and

Section 4 explains the impact of AI in

business. In sections 5, 6, and 7, we explain

the current trends, limitations, and

challenges of AI in business and future

works and recommendations, respectively.

And finally, we conclude the paper in

Section 8.

2. Literature Review

This literature review aims to provide an

overview of the impact of AI on business,

with a focus on two main areas: the

background of AI in the business industry

and recent research works. The first section

will provide a comprehensive overview of

the history and development of AI in the

business industry, including its evolution

over time, the current state of the technology,

and the different applications of AI in

various business domains. The second

section will focus on recent research works

that have explored the impact of AI on

business.

2.1 Background

AI has a long and rich history of

development, dating back to the 1950s. Early

AI research focused on rule-based systems

and symbolic reasoning, which involved

programming computers to reason based on

a set of predefined rules. In the 1980s, the

field of AI shifted towards machine learning,

which involved training computer

algorithms to learn from data and make

predictions [4]. Since then, the development

of AI has accelerated, with the advent of

deep learning and neural networks, which

have enabled computers to analyze vast

amounts of data and recognize patterns with

high accuracy These advancements in AI

have paved the way for its integration into

business operations, offering new

opportunities for growth, innovation, and

improved decision-making. In the business

world, AI is transforming the way

companies operate, interact with customers,

and make decisions. The impact of AI is

visible in various industries, including

marketing [5], finance, healthcare [6],

manufacturing [7], logistics [8], Inventory

[13], and human resources [9].

While the potential of AI is vast, there are

also challenges and limitations associated

with its implementation, such as ethical

concerns around the use of AI [10], data

privacy and security issues [14], lack of

technical expertise and knowledge [1], high

implementation costs [15], and limitations in

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

AI technology and its applications [16].

2.2 Recent Research Works

Recent research has focused on the impact of

AI in business areas and its potential for

transforming various industries. Studies

have found that AI implementation can

improve customer experiences, increase

efficiency, and reduce costs [17-19]. In the

area of marketing and advertising, research

has explored the use of AI in predicting

consumer behavior, optimizing advertising

campaigns, and improving customer

segmentation [20, 21]. Artificial intelligence

(AI) has become an increasingly important

technology for businesses across a variety of

industries [22, 23]. The adoption of AI in the

business industry is not without its

challenges. One of the biggest challenges is

the need for significant investment in

infrastructure and talent [24]. As highlighted

by Shamim et al. (2019), firms need to make

a significant investment in hardware,

software, and data management

infrastructure to take full advantage of AI

capabilities [25]. In addition, the adoption of

AI requires a significant investment in talent

acquisition and training, as highlighted by

Pillai et al. (2020), Faqihi et al. (2023), and

Mariani et al. (2023) [26-28]. These challenges

are further compounded by the need to

address ethical concerns around the use of

AI in business [29, 30].

Despite these challenges, the adoption of AI

in the business industry is growing rapidly.

Several studies have highlighted the key

trends in AI adoption in this context. For

example, Pettey (2017) predicts that by 2021,

40% of all enterprise applications will

include AI features [31], while IDC forecasts

that global spending on AI will reach $110

billion by 2024 [32]. Another trend

highlighted by Sampson et al. (2021) is the

increasing use of AI to automate routine

tasks, allowing employees to focus on more

creative and strategic work [33]. AI has a

broad range of applications in the business

industry, including marketing, operations,

finance, and customer service. For example,

in marketing, AI is increasingly used for

personalized marketing and customer

segmentation [34, 35]. In operations, AI can

help optimize supply chain management

[36], while in finance, it can be used for fraud

detection and risk management [37, 38]. AI is

also increasingly being used in customer

service, for example, through chatbots and

virtual assistants [39-41]. Other studies have

explored the impact of AI on specific

industries, such as healthcare [42-44],

education [45-47], and retail [48, 49]. In

healthcare, AI is being used for diagnosis

and personalized treatment planning, while

in education, it is being used for

personalized learning and adaptive testing.

In retail, AI is being used to improve the

customer experience, for example, through

personalized recommendations and product

search. In next section, we discuss the

methodology of this study.

3. Methodology

Our literature review followed a systematic

and structured approach to ensure the

validity and reliability of the findings. We

acknowledge the importance of a rigorous

methodology in conducting a literature

review. Our study employed a systematic

approach, including research question

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

formulation, comprehensive data collection,

inclusion and exclusion criteria, data

analysis, critical evaluation of sources, and

synthesis of findings. By following these

methodological steps, we aimed to minimize

biases and ensure the scientific rigor of our

review. Systematic literature review method

was used over the multivocal literature

review and other review methods for three

reasons- a) high objectivity than other

narrative methods b) results are more

conclusive and replicable with a clear

process of selecting literature and c) helps to

identify research gaps. Therefore, SLR helps

to advance a research field with more clarity

of past studies and projecting future research

agendas [50]. We believe that our approach

provides a robust foundation for the analysis

presented in the manuscript. The key steps

of our methodology are as follows:

Research Question Formulation: The first step

in our literature review process was to

formulate a clear research question to guide

our study. The research question focused on

understanding the impact of artificial

intelligence (AI) on the business industry,

including its applications, trends, challenges,

and potential future developments.

Comprehensive Data Collection: To ensure

inclusivity and comprehensiveness, we

conducted an extensive search of reputable

academic databases, such as PubMed, IEEE

Xplore, Google Scholar, and Scopus. The

search was limited to peer-reviewed articles

between 2015 and 2023, conference papers,

and reports published between 2017 and

2023 to capture the most recent and relevant

literature.

Inclusion and Exclusion Criteria: We

established specific inclusion and exclusion

criteria to select relevant publications for our

review. Inclusion criteria involved

publications directly related to the impact of

AI in business, while exclusion criteria

excluded articles that were in non-English

languages and did not meet the thematic

focus or lacked academic rigor.

Data Analysis and Synthesis: Following data

collection, we systematically analyzed the

selected articles to identify key themes,

trends, and findings. The analysis involved

coding and categorizing the literature based

on the different aspects of AI in business,

such as applications, challenges, and trends.

Critical Evaluation and Quality Assessment: To

ensure the quality of the literature included

in the review, we critically evaluated each

selected publication. We assessed the

credibility of the sources, the validity of

research methodologies used, and the

relevance of the findings to our research

question as shown in figure 1..

Synthesis and Dissemination of Findings: The

findings from the reviewed literature were

synthesized and organized into a coherent

and logical narrative. We provided a

comprehensive overview of the impact of AI

in business, addressing its various

applications, challenges, current trends, and

potential future developments. The results

were presented in a structured manner in the

manuscript. We discuss the application areas

of AI in business, in section 4.

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

Figure 1: Flow chart for screening and selection of articles

4. Applications of AI in Business

According to a review article by Haleem et al.

[51], AI is gaining popularity in the business

industry due to its potential to transform

various areas such as customer service,

marketing, finance, healthcare,

manufacturing, logistics, and human

resources. AI can automate tasks, analyze

data, and provide insights, enabling

businesses to improve decision-making,

increase efficiency, and enhance the

customer experience [51, 52]. AI is

transforming customer service by enabling

chatbots and virtual assistants to handle

routine customer queries and provide

personalized recommendations to customers

[53, 54]. AI-powered sentiment analysis can

also help businesses understand customer

feedback and respond to complaints in real-

time. AI can be used to optimize marketing

campaigns, personalize content, and predict

consumer behavior [55, 56]. AI algorithms

can analyze consumer data to create targeted

marketing campaigns that deliver the right

message to the right audience at the right

time [35, 57]. Moreover, AI-powered

chatbots can engage with customers in real-

time, helping businesses improve the

customer experience.

AI can automate repetitive tasks, such as

data entry and reconciliation, enabling

finance and accounting professionals to

focus on higher-value tasks [58]. AI

algorithms can also analyze financial data to

identify fraud and prevent financial crimes.

Moreover, AI-powered portfolio

management and algorithmic trading can

help financial institutions make better

investment decisions. AI is revolutionizing

healthcare by enabling personalized

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

medicine, drug discovery, and patient

monitoring [59, 60]. AI algorithms can

analyze vast amounts of medical data to

identify patterns and make predictions

about patient health. Moreover, AI-powered

diagnosis and treatment can help healthcare

professionals make more accurate and

timely diagnoses and improve patient

outcomes [61].

AI can improve efficiency and productivity

in manufacturing by enabling predictive

maintenance, quality control, and supply

chain optimization [62, 63]. AI algorithms

can analyze sensor data to predict machine

failure, enabling manufacturers to perform

maintenance before breakdowns occur [64].

Moreover, AI-powered autonomous robots

can help with repetitive tasks, enabling

manufacturers to free up human resources

for more complex tasks. AI can optimize

logistics and supply chains by enabling real-

time tracking and delivery prediction, as

well as inventory management and demand

forecasting [36, 65]. AI algorithms can

analyze data from multiple sources to

predict demand and optimize delivery

routes. Moreover, AI-powered chatbots can

provide customers with real-time updates on

their deliveries, improving customer

experience.

AI can help businesses with human

resources tasks such as candidate screening,

employee engagement, performance

management, and talent management [66,

67]. AI algorithms can analyze employee

data to identify patterns and make

predictions about employee performance

and attrition. Moreover, AI-powered

chatbots can help with routine HR queries,

freeing up HR professionals for more

complex tasks.

Table 1 shows some of the reviewed articles

on the different functions of business, where

AI has the potential to be integrated and

provide ease of service and convenience to

the stakeholders. In summary, AI is

transforming business across various

domains, from customer service to

healthcare. While challenges and limitations

exist, businesses that embrace AI can gain a

competitive advantage and drive growth.

Ongoing research and development in AI

will continue to shape the future of

businesses across various sectors.

Table 1: Summary of the articles reviewed of the following business areas of AI.

Business Area

Objectives of the study

References

Marketing and Sales

Personalized recommendations, predictive analytics, chatbots, and

sentiment analysis

[35, 68]

Marketing automation, neuromarketing, viral marketing, voice

recognition, and conversion optimization,

[20, 69, 70]

Efficiency improvements, accuracy improvements, better decision-

making, customer relationship improvements, sales increases, cost

reductions, and risk reductions

[5, 34, 71]

Predict customer behavior, one-to-one digital marketing, enhance sales,

and increase customer satisfaction.

[48, 49, 72]

Consumers’ perception and voice-assisted AI centers

[51, 73]

Knowledge creation and knowledge management in B2B sales

[74]

Social media and digital advertising

[21, 57, 75]

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

Finance

Fraud detection, risk assessment, portfolio management, and algorithmic

trading

[37, 38, 76]

Lending Risk, security in processing payments, automating compliance-

related work, and improving customer targeting.

[58, 77]

Digital financial inclusion, risk detection, information asymmetry,

availing customer support and helpdesk through chatbots, and fraud

detection

[78]

Digital servitization

[79]

Ethical implications

[80]

Stable functioning and sustainable growth

[81]

Portfolio diversification.

[82]

Healthcare

Diagnosis and treatment, drug discovery, personalized medicine, patient

monitoring

[6, 59, 61, 83]

Ethical risks presented by AI in health

[84]

Data infrastructure

[85]

Diagnosis, patient morbidity or mortality risk assessment, disease

outbreak prediction and surveillance, and health policy and planning.

[86]

Ethical challenges: informed consent to use, safety and transparency,

algorithmic fairness and biases, and data privacy.

[61, 87]

Diagnosis and treatment recommendations, patient engagement and

adherence, and administrative activities.

[61, 88]

Medical, legal, ethical, and societal questions

[89]

Challenges for AI startups-

clinical limitations, credibility, model explainability, non-clinical

stakeholder, management, technical depth

[90]

Manufacturing

Predictive maintenance, quality control, supply chain optimization, and

autonomous robots

[36, 62, 65, 91]

Camera calibration, detection, tracking, camera position and orientation

(pose) estimation, inverse rendering, procedure storage, virtual object

creation, registration, and rendering

[63, 92]

Data-driven predictive analytics and decision-making in highly complex

[93]

Efficient and intelligent automation

[7, 65, 94]

Timely acquisition, distribution, and utilization of real-time data

[63, 95]

Virtual manufacturing

[65, 96]

Informed deep learning, explainable AI, domain adaptation, active

learning, multi-task learning, and graph neural networks

[97]

Human Resources

Candidate screening, employee engagement, performance management,

and talent management

[67, 98]

Job replacement, human-robot/AI collaboration, decision-making and

learning opportunities, and HRM activities, namely, recruiting, training

and job performance.

[67, 99]

Complexity of HR phenomena, constraints imposed by small data sets,

ethical questions associated with fairness and legal constraints, and

employee reaction to management via databased-driven algorithms

[66, 100]

Interaction of artificial intelligence (AI) (primarily robots) and human

workers

[9, 101]

Recruitment and performance management

[9, 66]

5. Impact of AI in Business Areas

The impact of AI in business areas has been

significant, with many companies realizing

its benefits [102]. The automation of time-

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

consuming tasks, allows AI to focus on

higher-value tasks and leading to increased

efficiency and productivity in business [103,

104]. Additionally, another reason for a

successful impact in business, is that AI

algorithms can analyze large amounts of

data, providing valuable insights that can

inform better decision-making [105, 106].

There is an invaluable contribution that AI

can make to future knowledge management

systems, emphasizing their promising and

transformative role to revolutionize the field

of innovation management, potentially

transforming the way we approach work

and innovation [107]

Many businesses across various industries

have successfully implemented AI to drive

growth and innovation [108]. For instance,

Amazon has implemented AI-powered

recommendation systems that provide

personalized product recommendations to

customers, driving sales and improving

customer satisfaction [34, 49] . IBM has

implemented Watson, an AI-powered

system that enables businesses to analyze

large amounts of data, improving decision-

making and driving growth [105]. Chatbots

have been a recent and very successful AI

based innovation [54]. Additionally, Google

has implemented AI-powered voice

recognition, enabling users to use voice

commands to access information and

perform tasks [109]. The recent figures of

start-ups focusing on different AI areas are

shown in Table 2 and clearly signify the

importance of AI in different industries,

sectors, and functions of the business.

Table 2. AI areas and the percentage of startups in these areas in 2021 and 2022.

Al Area

Percentage of Al

Startups

Natural Language Processing [NLP)

24%*

Machine Learning [ML)

22%**

Computer Vision

16%*

Robotic Process Automation (RPA)

7%*

Speech Recognition

5%*

Predictive Analytics

5%**

Autonomous Vehicles

2%**

Other

20%**

*Emerj Al Startup Insights Report 2021

**CB Insights Al 100 Report 2022

As per the latest report, several important

facts were revealed that helped to

understand the trend and industrial review

of AI. According to the report, AI adoption

has more than doubled in the last five years

[110]. A survey in the same report shows that

service operations, marketing and sales,

product and service development, risk

modeling, and manufacturing have been top

functions of the business at the forefront of

integrating AI into their business model

[111]. One such example is GE, which

employs digital models and twins to rapidly

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

test product design in turbine development,

effectively reducing time and costs. Similarly,

BASF utilizes AI to identify new molecules

suitable for customer formulations, aiding in

the creation of innovative products. AI has

also proved to be instrumental in generating

new-product ideas, identifying novel

product opportunities, and even fostering

the creation of entirely new product concepts

[112]. The level of investment in AI has

touched new heights in these functions as

per the survey.

Figure 2 demonstrated the profit margin of

business functions from AI adaptation in

2021. Marketing, sales, and production or

service development have seen the highest

increases in revenue, while supply chain

management has witnessed the largest cost

reductions among all the functions.

In summary, the impact of AI in business

areas has been substantial, transforming

business processes and leading to improved

decision-making, enhanced customer

experiences, and increased productivity.

Ongoing research and development in AI

will continue to shape the future of

businesses across various sectors [113].

Figure 2: Cost decrease and revenue increase from AI adoption

Source: McKinsey and company, 2021

6. Current Trends in AI in Business Areas

The impact of AI on business is undeniable

and significant, as evidenced by numerous

studies and research papers [102, 113]. AI is

transforming the business industry by

providing intelligent solutions for various

business problems. In this section, we

discussed the current trends of AI in the

business industry, including AI-powered

chatbots and customer service, predictive

analytics and forecasting, personalization

and recommendation systems, supply chain

optimization, and cybersecurity. As AI

continues to advance, it is expected that

more businesses will adopt AI technologies

to stay competitive in the market. With the

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

growing trend towards integrating AI with

other technologies, businesses are

experiencing a surge in demand for AI skills

among their employees. This demand is

driving the development of more advanced

and sophisticated AI algorithms [114].

One area where AI is having a profound

impact on business is customer service by

providing personalized recommendations.

Xu et al. suggested that organizations can

reduce their costs by substituting human

customer service with AI customer service as

it has been found AI customer service

systems are perceived to have a greater

problem-solving ability [115]. On the other

hand, Khan & Iqbal acknowledges that AI

reduces the cost to organizations, but warns

about inefficient customer satisfaction [116].

Research has shown that AI-powered

chatbots and virtual assistants are becoming

more common and effective in handling a

range of customer inquiries and issues,

leading to improved customer loyalty and

retention, and increased revenue and profit

[68]. AI can also significantly improve

efficiency and productivity by automating

repetitive and tedious tasks, allowing

companies to focus on more strategic and

creative work, resulting in better use of

resources [117]. In addition, AI can enhance

decision-making processes by analyzing vast

amounts of data in a fraction of the time it

would take humans to do the same task [118].

Moreover, AI can reduce costs by

automating processes that were previously

done manually, such as customer service

and supply chain management [36]. AI can

also be used to optimize supply chain

management, which can help businesses

reduce inventory costs, transportation costs,

and other expenses [119]. The future of AI in

business is promising, with continued

integration with other technologies, such as

the Internet of Things, and the development

of more specialized and targeted AI

applications. Despite the many benefits,

implementing and adopting AI technology

comes with a set of challenges, we discuss

the challenges and limitations in the next

session.

7. Challenges and Limitations of AI in

Business Areas

While AI has significant benefits for

businesses, it also presents several

challenges and limitations that must be

considered. In this section, we will explore

some of the key challenges and limitations of

AI in business areas.

Data quality and availability: There is a

popular phrase called “Garbage in, Garbage

out” to describe the fact that the quality of

the output produced by a computer system

is directly related to the quality of the input

that it receives. In other words, if the input to

a computer system is of poor quality, then

the output produced by that system will also

be of poor quality [120]. Therefore, the

quality of input data has is phrase is often

used to emphasize the importance of data

quality in the context of computer systems

and artificial intelligence.

The concept of "garbage in, garbage out"

applies to a wide range of computer systems,

including those that use artificial intelligence

and machine learning algorithms [121]. In

these systems, the quality of the input data is

critical to the accuracy and effectiveness of

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

the algorithms. If the input data is

incomplete, inaccurate, or biased, then the

algorithms may produce inaccurate or

biased results. This can lead to serious

consequences, particularly in applications

that require high levels of accuracy, such as

medical diagnosis, financial forecasting, or

autonomous driving. To mitigate the risks of

"garbage in, garbage out," businesses must

prioritize data quality throughout the entire

data lifecycle, from data collection and

storage to data processing and analysis. This

requires a commitment to data governance

and management practices, including data

profiling, cleansing, and enrichment. It also

requires a focus on data privacy and security

to ensure that data is accurate, unbiased, and

protected from unauthorized access.

Ethical concerns: AI can raise ethical concerns,

particularly with regards to the potential for

AI to make decisions that may be

discriminatory or biased. This is especially

relevant in areas such as hiring, where AI

algorithms may be used to screen job

applicants. There is also a concern that AI

may be used for surveillance or other

purposes that may infringe on individual

privacy [122].

Data privacy and security issues: AI relies

heavily on data, which raises concerns about

data privacy and security. As businesses

collect and use large amounts of data, there

is a risk of data breaches or other security

issues [123]. Additionally, there are concerns

around the ethical use of data, particularly

when it comes to sensitive information such

as medical records [124].

Lack of technical expertise and knowledge: AI

requires specialized technical expertise and

knowledge. This means that businesses may

struggle to find qualified staff to implement

and maintain AI systems. Additionally, the

pace of technological development in the

field of AI means that businesses need to

continually invest in training and

development to keep up with the latest

trends [125].

High implementation costs: The development

and implementation of AI systems can be

costly, particularly for small and medium-

sized businesses. This can make it difficult

for businesses to adopt AI, particularly if the

benefits are not immediately clear [126].

Limitations in AI technology and its applications:

AI is not a panacea, and there are limitations

to what it can do. For example, AI is still

relatively weak in areas such as common-

sense reasoning and creativity. Additionally,

there may be limitations to the types of

problems that AI can solve, particularly if

they are complex or involve human

emotions [127].

Lack of transparency: Many AI algorithms are

considered “black boxes” because it is

difficult to understand how they make

decisions. This lack of transparency can be a

significant challenge for businesses,

particularly in regulated industries where

transparency is required. Businesses need to

ensure that they can explain how their AI

systems make decisions and provide

transparency to stakeholders [128].

Integration with existing systems: Many

businesses already have complex IT

infrastructures, and integrating AI systems

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

with these systems can be challenging.

Businesses need to ensure that their AI

systems can seamlessly integrate with

existing systems and that they can effectively

manage the complexity of their IT

infrastructure [129].

In conclusion, while AI offers significant

benefits to businesses, it also presents several

challenges and limitations that need to be

carefully considered. These include ethical

concerns, data privacy and security issues, a

lack of technical expertise and knowledge,

high implementation costs, and limitations

in AI technology and its applications.

Addressing these challenges will be crucial

for businesses to fully realize the potential of

AI and improve the future of it in various

sectors. We discuss the future works and

recommendations for AI in business areas, in

the next session.

8. Future Work and Recommendations

According to a report by Accenture (2022)

[130], the use of AI in business is expected to

continue to grow and become even more

ubiquitous, Businesses that successfully

apply for AI could increase profitability by

an average of 38 percent by 2025. The

introduction of AI could boost the economy

by US$14 trillion across 16 industries in 12

economies [131]. In addition, there is likely

to be an increased focus on developing more

ethical and transparent AI systems, with

businesses striving to ensure that their AI

systems are fair, transparent, and

accountable [132].

To successfully implement AI, businesses

should focus on building a strong data

infrastructure, hiring, and training

employees with AI skills, and creating a

culture that is open to experimentation and

innovation. It is also important to have a

clear understanding of the goals of the AI

implementation and to ensure that the AI is

aligned with the overall business strategy

[133].

As AI continues to be used in business, it is

important to consider the ethical and social

implications of the technology. This includes

addressing issues such as bias and

discrimination, privacy concerns, and the

potential impact on jobs and the workforce

[134]. There is a widespread belief that AI

will partially replace certain job functions.

The integration of AI in the workplace

demands the endorsement not only from

senior management but also, more

importantly, from the engineers and

technicians responsible for its day-to-day

implementation [135].

To address these concerns, businesses

should prioritize transparency, fairness, and

accountability in their AI systems, and work

to develop AI systems that are aligned with

ethical and social values [136, 137]. Overall,

the future of AI in business is likely to be

characterized by continued growth and

innovation, as well as an increased focus on

ethical and responsible use of the technology.

To successfully implement AI, businesses

should focus on building a strong data

infrastructure, hiring and training

employees with AI skills, and addressing

ethical and social considerations.

The primarily focused of this research was to

explore the impact of AI on the business

industry. Future research can be done

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

looking at the other domain, which is critical

to humanity i.e. Environmental

Conservation, transportation etc. How AI

tool can be used to monitor ecosystem,

identify environmental pollution and

optimized natural calamity could be other

area of future study. Furthermore, research

can also be done to explore the application

and future trends and challenges in

Humanoid robots that can assist individual

with disabilities. Individual with hearing

disability or speech impairments can be

guided by AI enabled technology to improve

communication and interaction.

9. Future of AI in Business and Theoretical

Impact

The rapid evolution of artificial intelligence

(AI) has undeniably transformed the

business landscape, but the future holds

countless possibilities and uncertainties.

While AI has proven its potential to drive

growth, enhance decision-making, and

improve customer experiences, the

unknown future presents new challenges

and opportunities [138]. As AI continues to

advance, its integration with other cutting-

edge technologies like the Internet of Things

and blockchain may unlock even more

transformative applications in business

domains [139]. However, ethical concerns,

data privacy, and AI's impact on the

workforce remain pivotal challenges that

demand attention and responsible

governance [140]. As businesses strive to

stay competitive, they must grapple with the

delicate balance between embracing AI's

potential and addressing potential risks

[141]. Additionally, the emergence of new

AI-driven innovations and the potential for

AI to exceed human capabilities in certain

areas raise questions about the long-term

impact on various industries and society as a

whole [142]. To harness the full potential of

AI in the unknown future, businesses must

continually invest in research, talent

development, and robust ethical

frameworks to ensure the responsible and

sustainable deployment of AI technologies

in business operations [143]. Embracing the

unknown with a proactive and ethically-

driven approach will pave the way for a

transformative AI-powered future in the

business industry [144].

The theoretical impact of this study lies in

the synthesis and analysis of various

research works related to AI in the business

industry. By presenting a comprehensive

overview of AI applications, trends,

challenges, and future possibilities, our

review contributes to the theoretical

understanding of AI's influence on business

processes and decision-making. The

identification of current trends and figures,

along with a focus on ethical and social

considerations, provides valuable insights

for scholars and practitioners in the field of

AI and business. Additionally, the section on

the "Unknown Future of AI in Business"

explains the potential transformative effects

of AI integration with other technologies and

the implications for the workforce. These

discussions offer theoretical perspectives

that can guide future research and inform

strategic decision-making for businesses

seeking to leverage AI effectively.

10. Conclusion

In recent years, the impact of AI on the

This article has been accepted for publication in IEEE Engineering Management Review. This is the author's version which has not been fully edited and

content may change prior to final publication. Citation information: DOI 10.1109/EMR.2024.3355973

business industry has been profound. AI has

been used to improve customer service,

optimize supply chains, and analyze

consumer behavior, among other

applications. This review paper provides

details about the application areas and the

respective impacts of AI in those areas. We

also highlight the challenges and limitations

associated with AI in real world scenarios.

The review also discussed current trends

and demonstrates how AI is transforming

the way businesses operate, providing new

opportunities for innovation and growth.

The findings of this review have important

implications for policymakers, business

leaders, and researchers, providing insights

into the potential benefits and challenges

associated with the integration of AI into

business operations. Future research should

focus on developing effective strategies to

overcome the challenges associated with AI

implementation, promoting responsible AI

governance, and assessing the long-term

impacts of AI on the business industry.

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Unveiling the Nuances: How Fuzzy Set Analysis Illuminates Passenger Preferences for AI and Human Agents in Airline Customer Service

Article

Full-text available

Mar 2025

This research tackles an essential gap in understanding how passengers prefer to interact with artificial intelligence (AI) or human agents in airline customer service contexts. Using a mixed-methods approach that combines statistical analysis with fuzzy set theory, we examine these preferences across a range of service scenarios. With data from 163 participants’ Likert scale responses, our qualitative analysis via fuzzy set methods complements the quantitative results from regression analyses, highlighting a preference model contingent on context: passengers prefer AI for straightforward, routine transactions but lean towards human agents for nuanced, emotionally complex issues. Our regression findings indicate that perceived benefits and simplicity of tasks significantly boost satisfaction and trust in AI services. Through fuzzy set analysis, we uncover a gradient of preference rather than a stark dichotomy between AI and human interaction. This insight enables airlines to strategically implement AI for handling routine tasks while employing human agents for more complex interactions, potentially improving passenger retention and service cost-efficiency. This research not only enriches the theoretical discourse on human–computer interaction in service delivery but also guides practical implementation with implications for AI-driven services across industries focused on customer experience.

The Significance of GenAI Empowered ERP Systems Course Teaching in Quality Education

Conference Paper

Full-text available

Nov 2024

This paper aims to analyse the significance of Generative Artificial Intelligence (GenAI) Empowered Enterprise Resource Planning (ERP) systems as a course in higher education. With the advancement of GenAI technology, ERP systems are capable of automating and streamlining business operations across various industries. The usage of ERP systems has become prevalent in small to medium-sized organizations worldwide, akin to the utilization of office packages for day-to-day operations. Therefore, fundamental knowledge and understanding of GenAI- Empowered ERP system concepts is an advantageous skill for undergraduate and graduate students as potential jobseekers in various industries.

AN ALGORITHMIC SOCIETY AND ITS IMPACT ON THE COMPETITIVENESS OF ORGANIZATIONS

Article

Jan 2025

Marek Szajczyk

Purpose: This article aims to provide a deeper understanding of how algorithms influence competitive advantage, organizational decision-making, and potential ethical dilemmas. Design/methodology/approach: The primary research assumption is that the integration and use of algorithms significantly affect organizational competitiveness and communication. Algorithms offer opportunities for enhanced efficiency, improved decision-making, and product differentiation, but they also pose challenges related to transparency, organizational dynamics, and ethics. The central research question is: how do algorithms influence the creation and maintenance of competitive advantage, the dynamics of organizational communication, and decision-making processes within organizations? Findings: The research highlights differences in the adoption and use of algorithms (including artificial intelligence) across countries, identifies the most common application areas of generative AI in organizations, and examines cost reduction and revenue growth driven by GenAI implementation. Additionally, the study explores the level of personal understanding of GenAI and its perceived impact on business processes across various industries. Research limitations/implications: The study faces limitations in assessing the nuanced impact of algorithms on human interactions and in adapting findings to diverse industries. Practically, organizations must balance automation with human oversight to ensure ethical and effective decision-making. Navigating these dynamics is critical to fully leveraging the benefits of algorithms while addressing associated risks. Originality/value: This research provides a comprehensive exploration of how algorithms shape organizational dynamics and competitiveness. It offers practical insights into the diverse applications of algorithms and highlights challenges such as transparency and communication dynamics posed by AI integration. By bridging theoretical perspectives with practical implications, the study delivers valuable guidance for organizations adapting to the transformative impact of AI.

Role of Artificial Intelligence in Human Capital Management: A Review at PT. Pos Indonesia

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Full-text available

Mar 2024

PT. Pos Indonesia is committed to overcoming digital era challenges by implementing strategic transformations, particularly through the adoption of Artificial Intelligence (AI). AI plays a key role in enhancing operational efficiency, service quality, and revenue growth. It optimizes logistics, accelerates courier services, and personalizes customer interactions, contributing to an 18.64% increase in business revenue in 2023, reaching Rp 5,479,12 billion. This growth is driven by AI based automation in supply chain management and customer data analysis. The goal of this research is to explore how PT. Pos Indonesia can address AI implementation gaps and optimize its use to gain sustainable competitive advantages in operations and Human Capital Management (HCM). It also aims to provide strategies for developing human resource management and enhancing efficiency through AI. In HCM, AI improves recruitment, training, and employee development, supporting skill analysis, personalized training, and performance evaluation. This transformation aligns with AKHLAK values, fostering collaboration and adaptability while supporting remote work models for O-Rangers and postal agents via realtime monitoring. AI also helps PT. Pos Indonesia adapt to the evolving e-commerce industry by optimizing delivery routes, predicting demand, and empowering digital platforms like Pospay. Automation improves cost efficiency and customer service, strengthening PT. Pos Indonesia position in the digital logistics and courier services market. Through AI integration in HCM and operations, PT. Pos Indonesia enhances profitability and establishes sustainable competitive advantages, positioning itself as a model for technology driven business innovation in Indonesia logistics and financial sectors.

Perceptions of Artificial Intelligence in the Context of Marketing Innovations of Healthcare Institutions

Article

Jan 2025

The world today is an unstable place that threatens but also offers opportunities. Presently, one of the major innovation trends is the use of artificial intelligence. The use of AI has gained importance and is gradually reaching all areas of human activities, such as healthcare. The present paper focuses on the perception of artificial intelligence in the context of the marketing innovations of healthcare institutions. The main objective of this research is to analyse the ways in which artificial intelligence is used in healthcare settings from different research perspectives. This paper focuses on exploring views on the use of AI through marketing innovation, perceptions of the positive impact of AI as a marketing tool and the willingness to use AI. The subject is examined by quantitative research, using the questionnaire method on a sample of N=208 respondents. The data collected from the respondents show that the size of the healthcare facility has an impact on the use of AI as a marketing tool. This conclusion was supported by statistical verification of the hypothesis, with a value of p= 0.041, thus confirming the initial assumption. From a comprehensive point of view, more than half of the respondents said that they are in favour of using AI as a marketing tool in healthcare facilities. The perception of the positive impact of AI as a marketing tool does not depend on the classification of a healthcare facility’s healthcare activity. The positive impact of AI on all areas of healthcare services prevails. However, in accordance with the results of the research, the category of hospital facilities perceives the positive impact of artificial intelligence the most. The willingness to adopt artificial intelligence in marketing innovation is represented by the difference between healthcare facility owners. A deeper examination of data from respondents shows that private facilities are the most prone to adopt AI in their activities. This willingness is also confirmed by the value of the standard deviation, which was the lowest in health care facilities owned by a private owner. The current use of various forms of AI is widespread in more than 30% of healthcare facilities. Artificial intelligence is and will increasingly come to the forefront as a major innovation capable of solving tasks in ways that human decisions would. Healthcare was one of the first areas in which AI was implemented. In addition, the topic has gained increasing traction. The effective use of AI will help healthcare facilities benefit not only owners but also patients and other interest groups.

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Conference Paper

Feb 2025

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Dec 2024

AI Integrated Product Development: Building Sustainable Competitive Advantage

Article

Sep 2024
IEEE Eng Manag Rev

Artificial intelligence (AI) has long influenced modern life, extending beyond well-known applications like ChatGPT to essential roles in digital advertising, personalized recommendations, and autonomous vehicles. While the latest popularity of Generative AI is leading to superficial product enhancements, true competitive advantage arises from deeply embedding AI into products and core business functions. This paper examines the evolution of AI-powered products, drawing parallels with the smartphone revolution. Just as smartphones transitioned from novelty items to indispensable tools, AI integration will likely progress through phases: enhancing existing experiences, creating AI-first novelty products, and developing AI-integrated value-added solutions. Successful AI integration starts with substantial investment in talent, data infrastructure, and strategic vision. By focusing on deep AI integration, businesses can create sustainable competitive moats and enduring value, transforming industries and enhancing user experiences. Through case examples and historical insights, this paper provides a roadmap for leveraging AI to build innovative, impactful products and build sustainable business.

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Full-text available

Dec 2024

This paper presents a review of the technologies and services associated with online grocery shopping. The progress in the field of online grocery shopping has been very rapid in recent years. Hence, there was a need to systematize knowledge about the latest various solutions used in this topic. The authors searched the internet, focusing on websites of different supermarkets, shops, and other services that offer online shopping, as well as reviewed scientific papers. Based on the collected material, the authors created four thematic parts, which include: (1) supermarket services; (2) dedicated grocery delivery services and farm-to-table; (3) shopping in Virtual Reality and the metaverse; smart devices and (4) AI in food ordering—the last part includes smart devices, such as smart refrigerators, ovens, their functionality, and the services connected with them. The authors refer to 243 sources. The research includes the three following objectives: (1) exploring and presenting the emerging applied ways of online grocery shopping, (2) exploring and presenting the latest technological advances related to the digitalization of grocery shopping, (3) discussing the upcoming technologies, services, and methods in online grocery shopping. This paper provides knowledge about a wide range of solutions offered by both supermarkets and stores (e.g., shopping applications, VR applications, metaverse shopping) and other companies (e.g., deliveries, product tracking), highlighting the numerous functions available thanks to smart devices (e.g., voice control, own shopping lists, control of products, their quantities and expiration dates, management of user preferences, and many more). This paper also discusses social issues related to the presented solutions, such as their influence on consumer behavior, barriers to adoption, and the associated challenges.

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Conference Paper

Full-text available

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Article

Full-text available

Jan 2023

Robert Cooper

Artificial Intelligence (AI) is poised to revolutionize all aspects of business, particularly new-product development (NPD). Currently, our approach to NPD has remained largely unchanged for decades, yielding stubbornly poor results: only 30% of NP development projects become commercial successes. However, the Artificial Intelligence revolution is set to alter this landscape significantly!. Leading early-adopter firms demonstrate that AI not only finds many applications in NPD, but also offers substantial payoffs, such as 50% reductions in development times. Thes article provides an outline of the diverse and powerful applications of AI in NPD, offering numerous examples from leading companies. Examples include GE's use of digital models and twins to quickly test product design time in turbine development; BASF's use of AI to identify new molecules for use in customer's formulations; and AI to generate new-product ideas, identify new product opportunities, and even create new product concepts. Our exploratory journey begins at the idea stage and traverses the entire new-product process to the post-launch period. While AI might still resemble science fiction to many, that future is no longer fiction – it's here now. AI has arrived in full force! With an adoption window of about 13 years, the time is now to embrace AI in NPD in your business: AI will become a major milestone in NPD, perhaps the most important, within the decade (Figure 1)

Accelerating Innovation With Generative AI: AI-Augmented Digital Prototyping and Innovation Methods

Article

Full-text available

Jun 2023

Easy-to-use generative Artificial Intelligence (AI) is democratizing the use of AI in innovation management and may significantly change the way how we work and innovate. In this article, we show how large language models such as GPT can augment the early phases of innovation, in particular, exploration, ideation, and digital prototyping. Drawing on six months of experimenting with large language models in internal and client innovation projects, we share first-hand experiences and concrete examples of AI-assisted approaches. The article highlights a large variety of use cases for generative AI ranging from user journey mapping to idea generation and prototyping and foreshadows the promising role LLMs may play in future knowledge management systems. Moreover, we argue that generative AI may become a game changer in early prototyping as the delegation of tasks to an artificial agent can result in faster iterations and reduced costs. Our experiences also provide insights into how human innovation teams purposively and effectively interact with AIs and integrate them into their workflows.

Supply Chain Disruption versus Optimization: A Review on Artificial Intelligence and Blockchain

Article

Full-text available

Feb 2023

In response to significant disruption, supply chain optimization became sensitive to increasing consumer expectations, unexpected demand fluctuation, and inventory costs. Proactive movement, understanding, and empowerment have fostered the beneficial results of supply chain optimization, cooperation, and operational resilience. These pioneering activities are critical to achieving a paradigm shift in the supply chain, even agility in response to changing demand. However, sophisticated analytics such as artificial intelligence (AI) and blockchain are supposed to overcome these challenges to make smarter decisions on a daily basis. Due to these facts, this study aimed to model AI’s and blockchain’s role in supply chain optimization by conducting a systematic literature review based on the idealized framework of Rejeb et al., (2022) and the SALSA mechanism. In addition, this paradigm-shifting approach will provide fairer views and options for managing forecasting, planning, monitoring, and reporting across the entire supply chain. The emphasis remains on real-time accuracy, easy access, and optimization of operational indicators such as sales, visibility, and end-to-end supply chain operations at all times and from any location. It will be an eye-opening experience to enable stakeholders and partners to communicate information collaboratively, consistently, and efficiently.

Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research

Article

Full-text available

Feb 2023

As part of a business strategy, effective competitive research helps businesses outperform their competitors and attract loyal consumers. To perform competitive research, sentiment analysis may be used to assess interest in certain themes, uncover market conditions, and study competitors. Artificial intelligence (AI) has improved the performance of multiple areas, particularly sentiment analysis. Using AI, sentiment analysis is the process of recognizing emotions expressed in text. AI comprehends the tone of a statement, as opposed to merely recognizing whether particular words within a group of text have a negative or positive connotation. This article reviews papers (2012–2022) that discuss how competitive market research identifies and compares major market measurements that help distinguish the services and goods of the competitors. AI-powered sentiment analysis can be used to learn what the competitors’ customers think of them across all aspects of the businesses.

Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review

Article

Full-text available

Jan 2023
SENSORS-BASEL

Industry 5.0 is projected to be an exemplary improvement in digital transformation allowing for mass customization and production efficiencies using emerging technologies such as universal machines, autonomous and self-driving robots, self-healing networks, cloud data analytics, etc., to supersede the limitations of Industry 4.0. To successfully pave the way for acceptance of these technologies, we must be bound and adhere to ethical and regulatory standards. Presently, with ethical standards still under development, and each region following a different set of standards and policies, the complexity of being compliant increases. Having vague and inconsistent ethical guidelines leaves potential gray areas leading to privacy, ethical, and data breaches that must be resolved. This paper examines the ethical dimensions and dilemmas associated with emerging technologies and provides potential methods to mitigate their legal/regulatory issues.

Artificial Intelligence-Driven Talent Management System: Exploring the Risks and Options for Constructing a Theoretical Foundation

Article

Full-text available

Jan 2023

AI (Artificial intelligence) has the potential to improve strategies to talent management by implementing advanced automated systems for workforce management. AI can make this improvement a reality. The objective of this study is to discover the new requirements for generating a new AI-oriented artefact so that the issues pertaining to talent management are effectively addressed. The design artefact is an intelligent Human Resource Management (HRM) automation solution for talent career management primarily based on a talent intelligent module. Improving connections between professional assessment and planning features is the key goal of this initiative. Utilising a design science methodology we investigate the use of organised machine learning approaches. This technique is the key component of a complete AI solution framework that would be further informed through a suggested moderation of technology-organisation-environment (TOE) theory with the theory of diffusion of innovation (DOI). This framework was devised in order solve AI-related problems. Aside from the automated components available in talent management solutions, this study will make recommendations for practical approaches researchers may follow to fulfil a company’s specific requirements for talent growth.

Engineers' Perspectives on the Use of Generative Artificial Intelligence Tools in the Workplace

Article

Jan 2023

The integration of Artificial Intelligence (AI) into the workplace requires commitment not only from the leadership of company directors but, equally important, from the engineers responsible for its implementation. This paper presents a survey on perceptions concerning the utilization of AI tools in engineering environments. It was focused on engineers and students in the areas of electricity, electronics and computing. The questionnaire covered demographic information, Artificial Intelligence knowledge level, preferred tools, primary applications, perceived impact, and attitudes toward labor substitution. With the endorsement of the IEEE Uruguay Section and IEEE Region 9, the survey was distributed via email to a diverse group of potential participants, in several countries, including all IEEE members in Region 9. There were 375 replies to the survey, from 20 different countries in the Americas.

“Artificial-Intelligence-Driven Management”: Autonomous Real-Time Trading and Testing of Portfolio or Inventory Strategies

Article

Sep 2023

Manfred B. Schrettenbrunner

“Artificial-Intelligence-Driven Management” (AIDM) replaces the Deming cycle with the Artificial Intelligence-assisted Plan-Predict-Act cycle. Collaborative LeanBots for an industrial base and Trading- or StockExBots for trading or financial institutions and their clients, use this paradigm shift to perform cyber-secure, sophisticated decision-making processes (transactions) autonomously and in real-time at large scale, at low cost, and with minimal energy consumption. Recursively utilized gains reinforce and accelerate growth. Expanding on modern portfolio management, autonomous TradingBots diversify portfolios like inventories not only in terms of asset weighting, but also with respect to holding periods and moreover, do so in real-time. TradingBots can also be deployed to test automation, collaboration, portfolio, and inventory strategies. Metrics from modern portfolio management used show that StockExBots are able to outperform high-ranking index and actively managed funds. Five use cases are presented for leveraging the properties of StockExBots commercially. TradingBots can be deployed with a wide range of non-proprietary and proprietary (IoT or IIoT) devices or as software services, supporting decentralized, redundant, and localized transaction processes in proprietary (e.g., financial or trading institutions) and non-proprietary smart (home) environments. Linking both environments improves the scalability of proprietary ones and enables the migration and dispersion of complex real-time decision-making processes for mutual benefit.

The Ethics of AI in Health Care: A Mapping Review

Article

Jan 2020

Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda

Article

Jan 2023
J BUS RES

This study provides a systematic overview of innovation research strands revolving around AI. By adopting a Systematic Quantitative Literature Review (SQLR) approach, we retrieved articles published in academic journals, and analysed them using bibliometric techniques such as keyword co-occurrences and bibliographic coupling. The findings allow us to offer an up-to-date outline of existing literature that are embedded into an interpretative framework allowing to disentangle the key antecedents and consequences of AI in the context of innovation. Among the antecedents, we identify technological, social, and economic reasons leading firms to embrace AI to innovate. In addition to detecting the disciplinary foci, we also identify firms' product innovation, process innovation, business model innovation and social innovation, as key consequences of AI deployment. Drawing on the key findings from this study, we offer research directions for further investigation in relation to different types of innovation.

An Analytical review on the Impact of Artificial Intelligence on the Business Industry: Applications, Trends, and Challenges

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