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Frontiers in Psychology 01 frontiersin.org
Integrating artificial intelligence
into a talent management model
to increase the work
engagement and performance of
enterprises
Maja Rožman
1
*, Dijana Oreški
2 and Polona Tominc
1
1 Faculty of Economics and Business, University of Maribor, Maribor, Slovenia, 2 Faculty of
Organization and Informatics, University of Zagreb, Zagreb, Croatia
The purpose of the paper is to create a multidimensional talent management
model with embedded aspects of artificial intelligence in the human resource
processes to increase employees' engagement and performance of the
enterprise. The research was implemented on a sample of 317 managers/
owners in Slovenian enterprises. Multidimensional constructs of the model
include several aspects of artificial intelligence implementation in the
organization's activities related to human resource management in the field
of talent management, especially in the process of acquiring and retaining
talented employees, appropriate training and development of employees,
organizational culture, leadership, and reducing the workload of employees,
employee engagement and performance of the enterprise. The results show
that AI supported acquiring and retaining a talented employees, AI supported
appropriate training and development of employees, appropriate teams, AI
supported organizational culture, AI supported leadership, reducing the
workload of employees with AI have a positive eect on performance of
the enterprise and employee engagement. The results will help managers
or owners create a successful work environment by implementing artificial
intelligence in the enterprise, leading to increased employee engagement and
performance of the enterprise. Namely, our results contribute to the ecient
implementation of artificial intelligence into an enterprise and give owners or
top managers a broad insight into the various aspects that must betaken into
account in business management in order to increase employee engagement
and enterprise’s competitive advantage.
KEYWORDS
artificial intelligence, talent management, employees, employee engagement,
performance of the company
TYPE Original Research
PUBLISHED 25 November 2022
DOI 10.3389/fpsyg.2022.1014434
OPEN ACCESS
EDITED BY
Astadi Pangarso,
Telkom University,
Indonesia
REVIEWED BY
Sachi Nandan Mohanty,
College of Engineering Pune, India
Veronika Kabalina,
National Research University Higher School
of Economics, Russia
*CORRESPONDENCE
Maja Rožman
maja.rozman1@um.si
SPECIALTY SECTION
This article was submitted to
Organizational Psychology,
a section of the journal
Frontiers in Psychology
RECEIVED 08 August 2022
ACCEPTED 18 October 2022
PUBLISHED 25 November 2022
CITATION
Rožman M, Oreški D and Tominc P (2022)
Integrating artificial intelligence into a
talent management model to increase the
work engagement and performance of
enterprises.
Front. Psychol. 13:1014434.
doi: 10.3389/fpsyg.2022.1014434
COPYRIGHT
© 2022 Rožman, Oreški and Tominc. This
is an open-access article distributed under
the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
Rožman et al. 10.3389/fpsyg.2022.1014434
Frontiers in Psychology 02 frontiersin.org
Introduction
e global COVID-19 has undoubtedly accelerated the process
of deploying articial intelligence. erefore, developing the
capacity to deploy and use articial intelligence in an enterprise is
even more important for its competitiveness (Nayal etal., 2021).
e future is primarily related to the advancement of new intelligent
technologies and the rapid development of computer capabilities
(Dhamija and Bag, 2020). During the Industrial Revolution an
essential part of the development of technological innovation and
the transformation of many routine tasks and processes, which had
existed for decades, was observed, especially when people reached
the physical limits of capacity (Dabbous etal., 2022). Articial
intelligence oers a similar transformational potential to increase
and possibly relocate human tasks in social, industrial and
intellectual elds (Munir et al., 2022). e impact of articial
intelligence technologies can besignicant, especially in activities
such as nance, human resources, healthcare, manufacturing, retail,
supply chain, logistics, and the public sector (Paschen etal., 2019).
e need to use articial intelligence has grown with opportunities
for digitization. Processes in enterprises have been shortened, a
large part of business communications takes place via digital media,
and last, but not least, part of the business has moved to digital
platforms (Goel etal., 2022). With the advent of internet business,
new metrics have also emerged that require in-depth and
computationally demanding analytics. e advantage of articial
intelligence for the use of marketing analysis is that it enables the
calculation and allocation of large databases and learning. Articial
intelligence works similarly to humans and learns similarly to
humans (Danyluk and Buck, 2019; Dabbous etal., 2022).
e advantage of using AI is that the knowledge generated by
articial intelligence becomes an added value to the enterprise. is
can protect the employer from leaking knowledge (Goel etal., 2022).
e knowledge that an enterprise would acquire in the case of using
articial intelligence in marketing is knowing the customers, their
preferences, their behavior, knowledge of the business environment
and its changes, and knowledge of the enterprise, strategies and
desires (Lauterbach, 2019). In human resource management, talent
acquisition, education, and other fundamental areas of human
resources, using articial intelligence aects changes in the work
environment and the entire eld of human resource management
(Kumar et al., 2020; Saxena and Kumar, 2020). New articial
intelligence technologies that help automate HR processes could
bethe key to solving some of the HR function’s challenges, where less
resources could beachieved more (Nayal etal., 2021). AI can reduce
the stress of nding a suitable candidate and thereby reduce the
monotony of the work of managers in nding candidates with the
desired qualications (Kiron, 2017; Wamba-Taguimdje etal., 2020;
Kambur and Akar, 2021).
Increasing the use of digital technologies through articial
intelligence will signicantly impact changes in the labor market,
namely the focus on individual work tasks and the departure from
standard forms of employment (Lee and Chen, 2022). Nowadays,
the digitization of business models is one of the biggest challenges
in all industries. Digital technologies strongly impact how
enterprises create and bring added value to their customers. In
addition, enterprises need to update their business models,
emphasizing integrating technology into their internal
organization, administration, operations, and strategy (Di
Francescomarino and Maggi, 2020). Although there is a lot of
enthusiasm for the value of articial intelligence, enterprises
starting to use articial intelligence solutions face a number of
challenges that prevent them from achieving greater performance
(Bag etal., 2020). ere are many challenges in enterprises that
managers and employees face when they want to introduce an
articial intelligence system into their work process (Chiarini
etal., 2020; Amoako et al., 2021). ese limitations range from
systems bias and distrust in data collection and algorithms to
more theoretical issues related to the decision-making system and
taking control of workplace decision-making (Okunlaya etal.,
2022). Adopting articial intelligence is considered a challenge for
enterprises. New technologies, such as articial intelligence, will
change the way wework and consequently aect the organization
of work and processes in the enterprise (Yigitcanlar etal., 2020).
erefore, the enterprise must adequately implement articial
intelligence systems into existing processes and properly educate
employees to avoid feelings of self-preservation and conict
situations (Soni, 2020).
Also, the battle for talented employees has never been so
challenging. Due to the automation of repetitive tasks, jobs are
changing and some jobs are even disappearing, while the number
of very complex tasks is growing. In view of this, modern human
resources departments face challenges we have not yet seen
(Kambur and Akar, 2021). Articial intelligence technologies can
signicantly support nding and hiring employees and improve
employees’ well-being and loyalty, and work experience. Articial
intelligence technologies help the enterprise build a competitive
advantage with technology and the talents it employs (Wamb a -
Taguimdje etal., 2020).
AI has an increasing eect on the economy and presents a new
dimension of business. Slovenian enterprises need to take a step
forward in using articial intelligence, both in supporting business
and production processes and in upgrading the products and
services themselves. e implementation of articial intelligence
requires the transformation of the entire enterprise, which manifests
itself through organizational culture, new management methods,
new employee training methods and the creation of new enterprise
strategies. e problem of implementing articial intelligence is also
manifested in the fact that enterprises should change the way their
employees work. e goal of articial intelligence is to optimize,
automate, or oer decision support in the enterprise. Articial
intelligence increases productivity, and pave the way for new
products. All this can lead to a change in the way people do their
work. us, enterprises will need to carefully analyze expected
outcomes and prepare plans to adjust their workforce capabilities,
priorities, goals and jobs accordingly. us, managing articial
intelligence models requires new types of work skills. Based on this,
wedeveloped a multidimensional model with key constructs that
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Frontiers in Psychology 03 frontiersin.org
are important in implementing articial intelligence in the
enterprise to increase employee engagement and performance of
the enterprise (Fountaine et al., 2019). Also, weformulated two
research questions: (1) Are there positive eect of key constructs
that are important in implementing articial intelligence in the
enterprise (AI supported acquiring and retaining a talented
employees, AI supported appropriate training and development of
employees, appropriate teams, AI supported organizational culture,
AI supported leadership, reducing the workload of employees with
AI; Shanmugam, 2015; IBM, 2018; Hunt, 2021) on performance of
the enterprise? and (2) are there positive eect of key constructs that
are important in implementing articial intelligence in the
enterprise (AI supported acquiring and retaining a talented
employees, AI supported appropriate training and development of
employees, appropriate teams, AI supported organizational culture,
AI supported leadership, reducing the workload of employees with
AI) on employee engagement?
Literature review
Definition of talent management and
talent employees
e term talent management is composed of the word talent,
which means a personal ability (mental or physical), which makes
one person stand out from the crowd of others, and the word
management, which means managing, leading and dealing with
individuals (Narain et al., 2019; Jooss etal., 2022; Kafetzopoulos
et al., 2022). us, talent management is an eective way of
managing individuals who are very successful in their eld of
operation in the enterprise (Aljbour etal., 2021). e term talent
is associated with the ability to nd the cause of a problem,
synthesize information and create solutions or ways to solve a
certain problem (Luna-Arocas etal., 2020). Talent is one of the
most popular characteristics that employers expect from their
employees, which enables them to perform better than average
employees. e characteristics of talented employees are shown in
the fact that they are curious, set ambitious goals, like to do several
things at the same time, and work long and hard on things that
actually interest them (Costello and Osborne, 2005; Mensah,
2015). Talent management is a strategic approach to business
planning and human resources management, as well as one of the
new ways of achieving organizational eciency (Kafetzopoulos
etal., 2022). Such an approach makes it possible to improve the
results and potential of human resources (specically – talents),
which can bring a measurable and essential dierence to the
enterprise (Aljbour etal., 2021). Talent management is the answer
to the challenges of attracting and retaining employees with high
competences and enabling those employees to achieve
extraordinary work results, develop and advance in the enterprise
(King, 2017). Schreuder and Noorman (2019) dene talent
management as placing the right employees with the right skills
in the right position in the enterprise. Talent management
includes three key activities, which are talent acquisition, talent
development and talent retention (Seethalakshmi et al., 2020;
Jooss etal., 2022). e retention and development of talent at the
individual level are important components of the talent
management strategy (Pandita and Ray, 2018). e retention of
key talents in the enterprise must besupported through various
human resources management activities, which must
beadditionally adapted to the talents (Yildiz and Esmer, 2022).
Two of the ways to realize this are the development of individual
career development plans and the development of programs that
encourage employees to grow together with the enterprise
(Kafetzopoulos etal., 2022). Talent management activities must
beaimed at increasing employee engagement, because this directly
aects the possibilities for long-term talent retention in the
enterprise and the achievement of the enterprise’s performance
(Pandita and Ray, 2018; Yildiz and Esmer, 2022).
Definition of AI
Articial intelligence mimics the ability of the human brain to
learn, analyze, and make decisions (Mikalef and Gupta, 2021).
Jabłońska and Pólkowski (2017) emphasize that the key reasons
for implementing articial intelligence-based processes include
problem-solving, reducing the human workload, and reducing the
cost of cheaper labor. us, articial intelligence represents the
next step in developing enterprise, where employers can now
capture, store and analyze more data and information than ever
before. Adapting, investing, and conducting research and
development of advanced systems is increasing rapidly on the part
of both the state and enterprises around the world (Davenport and
Ronanki, 2018; Eubanks, 2018). In addition, according to Mikalef
and Gupta (2021), articial intelligence can foster creativity in
enterprises. Automating many repetitive and manual tasks will
allow employees to have more time for creative activities. Also,
with certain applications, articial intelligence can increase
employees’ ability to perform tasks with the help of extended
intelligence (Eubanks, 2018). Special articial intelligence
techniques can manage a large set of data and help professionals
with creative tasks such as engineering by improving their input
and making suggestions that would otherwise be dicult to
develop (Bag etal., 2020; Yigitcanlar etal., 2020).
Implementation of artificial intelligence
in an enterprise
e biggest challenge in implementing articial intelligence is
changing the enterprise’s culture and leadership, acquiring new
knowledge and skills, and changing business processes (Eriksson
etal., 2020). AI in the eld of HRM in the enterprise means using
technology to solve tasks in various human resources processes,
especially in the eld of talent acquisition, education, employee
development, and workforce management (Kambur and Akar,
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Frontiers in Psychology 04 frontiersin.org
2021). erefore, the enterprise must take care of employees’
proper training and development (Eriksson etal., 2020). AI can
be used in practically all phases of work in human resource
management, from short-term talent selection and candidate
screening to subsequent procedures for introducing new
employees and evaluating performance (Mikalef and Gupta,
2021). In addition to restructuring the repetitive administrative
tasks, articial intelligence tools help to rationalize personnel tasks
and gain exceptional insights into each candidate and employee
(Di Francescomarino and Maggi, 2020). Articial intelligence
tools for human resource management represent the future of
work, as they perform their tasks without the limitations of human
bias and the ability to make mistakes (Coulibaly etal., 2019). In
general, articial intelligence is mainly used in high-tech
enterprises, the nancial industry, healthcare, and logistics. In
Slovenia, the biggest lag can be felt in processing and
manufacturing enterprises, agriculture, healthcare, tourism, and
trade (SURS, 2021a). Organization and human resources are the
biggest problem in enterprises. Most enterprises lag far behind in
their readiness to introduce articial intelligence. Enterprises do
not have a sucient number of employees with the appropriate
skills. e prevailing belief in most enterprises is that they need
technical personnel who know how to develop and maintain
technologies. is is the biggest mistake made by most Slovenian
enterprises (SURS, 2021a). ere are two equivalent branches of
necessary knowledge and innovation when introducing articial
intelligence. In addition to technology, organizational, process,
and personnel aspects are also important. Enterprises need
employees with the skills to connect business and technology.
Above all, enterprises need employees who know how to recognize
the opportunities oered by articial intelligence and how to use
it in their work. erefore, enterprises need to invest heavily in the
education, training and retraining of all employees (Shaer et al.,
2020). ese are the biggest backlogs of Slovenian enterprises
(SURS, 2020). On the other hand, there are also risks that aect
the quality of decision-making by leaders, considering the results
of data analysis using articial intelligence. AI algorithms are made
by humans, they can have built-in biases from those they
inadvertently or intentionally introduce into the algorithm. In the
event that articial intelligence algorithms are built biased, they
will produce biased results (Tambe etal., 2019; Paesano, 2021). AI
may be that some important aspects are not included in the
algorithm or that it is programmed to reect and reproduce a
structural bias. Moreover, bias during decision-making can
be attached not only to human decision-making, but also to
decisions made by articial intelligence, since bias can already
appear during machine learning (Barn, 2020; Pangarso etal., 2022).
e rst step towards expanding the use of AI among
enterprises is to raise the level of awareness of how AI will have a
positive impact on their business in the future (SURS, 2021b).
Also, the use of articial intelligence today is no longer limited to
large enterprises. Due to the availability of technologies, smaller
enterprises can also use them to improve their business (Wamb a -
Taguimdje et al., 2020). us, the enterprise must have a
comprehensive strategy for implementing articial intelligence
(Yigitcanlar et al., 2020). erefore, we designed essential
constructs that are crucial in implementing articial intelligence
in the enterprise, increasing employee engagement and
performance of the enterprise.
Acquiring and retaining a talented employees
AI helps analyze the proles of dierent candidates, where it
checks whether the candidates have the required competencies. It
also helps with communication by sending automated emails to
candidates. With the help of artistic intelligence, employers get an
in-depth set of required knowledge and skills, thus helping to
select potential employees in acquire talent in a much faster time
(Vaishnavi and Achwani, 2018). Technology helps HR
professionals select suitable candidates for the job and allows them
to devote more time to tasks with greater added value and focus
on more critical parts of the business and strategic tasks (Eubanks,
2018; Hogg, 2019). Talented employees can connect and structure
business processes as a whole, know how to solve problems
quickly and eciently, are eager for new challenges, are motivated
and self-initiative, condent, curious, capable of empathy, and
want to improve business change. Talented employees show great
loyalty to the enterprise as they identify with it (Anlesinya and
Amponsah-Tawiah, 2020). us, weformulated hypotheses:
H1: AI supported acquiring and retaining talented employees
have an eect on performance of the enterprise.
H2: AI supported acquiring and retaining talented employees
have an eect on employee engagement.
Appropriate training of employees
Despite the advantages that articial intelligence oers for
performing mentally demanding work, the evaluation of an
investment in articial intelligence needs to be evaluated
appropriately (Goel et al., 2022). An enterprise may start
programming articial intelligence, but it gets stuck in transferring
employees’ tacit knowledge into a programming language (De
Bruyn et al., 2020). Employees do not understand dierent
phenomena independently, which makes it dicult to transfer
certain decisions made in the business world to articial
intelligence, which will otherwise perform all operations rationally
(Kambur and Akar, 2021). Another problem is the transfer of
knowledge in the opposite direction. e knowledge created by
articial intelligence needs to betransferred to employees, which
is an even greater challenge, as the data needs to bepresented in a
visual form that will facilitate the transfer of knowledge. In
addition, the learning cycle needs to berepeated for employees, as
with articial intelligence (Maity, 2019; De Bruyn etal., 2020).
Articial intelligence can also beused to smooth learning and
development activities. For example, an enterprise can use
articial intelligence to develop a custom learning program for its
employees (Soltani etal., 2020). is program can betailored to
Rožman et al. 10.3389/fpsyg.2022.1014434
Frontiers in Psychology 05 frontiersin.org
the individual’s needs and preferences, which will help them learn
new skills more quickly and eectively (Maity, 2019). us,
articial intelligence improves employees’ engagement levels and
helps them learn faster (Kashive et al., 2021). Additionally,
enterprises can use articial intelligence to track employee
progress and provide feedback accordingly (Paesano, 2021). is
will help employees feel more supported, motivated, and engaged
as they develop their skills (Wijayati etal., 2022). e following
hypotheses were formulated:
H3: AI supported appropriate training and development of
employees have an eect on performance of the enterprise.
H4: AI supported appropriate training and development of
employees have an eect on employee engagement.
Forming appropriate teams
New ideas, new views on production or products, and how
employees work bring enterprises opportunities to compete more
successfully. Each team can develop creative processes for
themselves that help them improve eciency and better solve
tasks, leading to an increase in employees’ work engagement
(Webber et al., 2019). With the help of articial intelligence,
employees can now collaborate with teams quickly and easy. e
technology can identify and group similar topics, which makes it
easier for team members to work on specic tasks related to a
project. Also, it helps reduce misunderstandings and strengthens
relationships between employees (Arslan etal., 2021). Articial
intelligence can help employees communicate more eciently by
automatically sorting and organizing incoming emails, messages,
and documents. Also, it can provide summaries of conversations
or specic topics to help employees stay up-to-date on all the latest
developments. Consequently, employees will spend less time
managing communications and more time working on tasks
(Saxena and Kumar, 2020). Articial intelligence is used as a
communication tool for enterprises with employees working from
home or in dierent locations. is allows them to communicate
and update the necessary information about their work or projects
they are working on (Lesgold, 2019). erefore, it is a necessity
that leaders facilitate and build the teamwork skills of their
employees if they are to steer an enterprise toward success
(Webber etal., 2019). e following hypotheses were formulated:
H5: Appropriate teams have an eect on performance of
the enterprise.
H6: Appropriate teams have an eect on employee engagement.
New organizational culture
For an enterprise to beready for the future, its leaders need to
create an innovative organizational culture. Organizational culture
is key to building an articial intelligence-driven enterprise (Munir
etal., 2022). Enterprises that manage to build a positive articial
intelligence culture and an inclusive and inspiring environment
will successfully manage change and attract all their employees
(Behl etal., 2021). e leader must create a culture that will allow
the enterprise to develop and adapt to new business realities
quickly. is will beexpressed through better ideas and products
and will help create a more inclusive future (Jarrahi etal., 2022).
Moreover, articial intelligence chatbots help enterprises
engage their employees and maintain a positive and inclusive
work culture regardless of background. is helps bring people
together by creating an open environment for all employees, not
just those closest to senior management (Dabbous etal., 2022). It
is important to create a new organizational culture that encourages
experimentation and continuous innovation, and the development
of new solutions. is in turn leads to increased performance of
the enterprise (Behl etal., 2021). Building a culture that supports
innovation with articial intelligence aects competitiveness.
Based on a global survey of 2,197 managers, 75% of respondents
saw improvements in team morale and engagement, collaboration,
and collective learning (Ransbotham et al., 2021). us,
weproposed hypotheses:
H7: AI supported organizational culture has an eect on
performance of the enterprise.
H8: AI supported organizational culture has an eect on
employee engagement.
New ways of leadership
One of the main obstacles to adopting articial intelligence is
the lack of leadership support for articial intelligence initiatives.
Realizing the business value of investing in articial intelligence
requires leaders’ genuine understanding and commitment to drive
far-reaching change (Mikalef and Gupta, 2021). e
implementation of articial intelligence in the enterprise will
be maximized because of the role of a leader (Dhamija etal.,
2021). New technologies like articial intelligence have changed
the nature of leadership. e use of robust data analytics grounded
in articial intelligence and machine learning techniques reveals
new business applications insights (Wijayati etal., 2022). With the
use of articial intelligence, leaders will focus more on the human
aspects (for example, personality characteristics and behaviors)
and less on the cognitive processing of facts and information
(Chang, 2020). is will help improve employee engagement and
performance and increase operational eciencies to improve the
enterprise’s bottom line (Kambur and Akar, 2021). us,
weproposed hypotheses:
H9: AI supported leadership has an eect on performance of
the enterprise.
H10: AI supported leadership has an eect on
employee engagement.
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Reducing the workload of employees with
artificial intelligence
Today, the introduction of AI in business human resources
processes requires a new symbiosis between human resources,
technology, and employees, as it affects the enterprise’s work
tasks and business processes (Dhamija and Bag, 2020).
Artificial intelligence will increasingly perform routine
operational tasks, thus enabling employees to devote their
time to more creative and strategic tasks that help develop the
human resources function of the future (Goel etal., 2022; Lee
and Chen, 2022). Artificial intelligence influences employee
engagement by improving remote employee monitoring. With
artificial intelligence-powered tools, employees can now
collaborate more quickly and effectively with colleagues who
are located remotely. This helps to enhance communication
and collaboration between employees (or team members),
regardless of their location (Agarwal etal., 2022). Artificial
intelligence can help employees set goals, provide timely
feedback on their progress, and help them find training
courses that are relevant for improving specific skills (Sari
etal., 2020). Artificial intelligence allows employees to save
nearly a third of their otherwise spent on uncomplicated and
monotonous tasks. This leads to an increase in employees’
work performance and employee engagement (Wijayati etal.,
2022). According to Bushweller (2020) and Wang (2021),
artificial intelligence could help employees in repetitive and
time-consuming tasks, which, in turn, would reduce their
workload and increase their productivity. Thus, artificial
intelligence significantly decreases workplace stress and
workload (Dhamija and Bag, 2020; Saxena and Kumar, 2020;
Okhifun, 2022). The following hypotheses were formulated:
H11: Reducing the workload of employees with AI has an
eect on performance of the enterprise.
H12: Reducing the workload of employees with AI has an
eect on employee engagement.
Increasing employee engagement and
performance of the enterprise with
artificial intelligence
Today, articial intelligence oers excellent value in a
market where people are developing articial intelligence
systems to perform complex tasks (Goel etal., 2022). New
articial intelligence applications herald a major step in
technology development (Lee and Chen, 2022). Traditional
soware is powerful but requires a large conguration and setup
to provide added value (Cichosz et al., 2020). Articial
intelligence systems are exible and require less time to
complete a particular task, as they learn quickly (Nayal etal.,
2021). Nowadays, articial intelligence is becoming a
competitive advantage for early users (Bag etal., 2020). ose
enterprises that do not adopt and implement articial
intelligence in their processes will beless competitive and less
successful in the market (Okunlaya etal., 2022). us, articial
intelligence positively inuences performance of the enterprise.
e primary goal of implementing articial intelligence into
enterprises’ work processes is to reduce costs and improve the
quality of products and services. e use of articial intelligence
encourages enterprises to both innovative and successful
responses to modern challenges as well as to improve work by
reducing the number of repetitive tasks through automation
(Ribeiro etal., 2021). In addition, articial intelligence with
algorithms and techniques enhances the accuracy of the
implementation of automated processes (Yigitcanlar et al.,
2020). Industry 4.0 is characterized by a set of technologies that
enable even greater progress in processes, and automation
contributes to the better eciency of organizational processes
and presents new opportunities in the market (Malik et al.,
2021; Mikalef and Gupta, 2021). e combination of concepts
and technologies such as automation, smart appliances, and
processes brings signicant changes in business processes,
aecting the ow of digital processes throughout the enterprise
(Ribeiro etal., 2021). With new technologies, the enterprise can
streamline and optimize business processes, relieve employees’
workload, and thus enable faster, more ecient, and higher
quality achievement of business goals and results (Eriksson
etal., 2020; Yigitcanlar etal., 2020). Bag etal. (2020); Kambur
and Akar (2021); Goel etal. (2022) emphasize that enterprises
oen face a problem when employees lose their potential and
creativity in the routine. Articial intelligence can take over
cyclical processes and execute them strictly on schedule. In this
way, the enterprise enables employees to have more time for
creativity and innovation. In the long term, articial intelligence
can signicantly increase the eciency of the department and
the enterprise as a whole (Bag etal., 2020; Kambur and Akar,
2021; Goel etal., 2022). Also, working with large amounts of
information is a laborious process that requires a lot of time and
resources. Such a task is extremely dicult for humans but easy
for articial intelligence. e use of technology signicantly
reduces the lead time and eliminates errors (Bushweller, 2020;
Sari etal., 2020; Wang, 2021). us, weproposed hypotheses:
H13: Employee engagement has an eect on performance of
the enterprise.
Figure1 presents the conceptual model of implementation
of AI in the enterprise to increase employee engagement and
performance of the enterprise. Figure1 shows six independent
variables which are AI supported acquiring and retaining a
talented employees, AI supported appropriate training and
development of employees, appropriate teams, AI supported
organizational culture, AI supported leadership, reducing
the workload of employees with AI and two dependent
variables which are performance of the enterprise and
employee engagement.
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Frontiers in Psychology 07 frontiersin.org
Methodology
Data and sample
e main survey involved randomly selected 317 medium-
sized and large Slovenian enterprises. From each enterprise, a
manager or owner participated in our research. Data were
collected from April 2022 to the end of June 2022. Random
sampling was carried out from the population, where the sample
frame is represented by the AJPES (Slovenian Business Register)
database of business subjects (AJPES, 2022). Empirical research
was conducted in 500 randomly selected medium-sized and large
enterprises out of 2,576 medium-sized and large Slovenian
enterprises (AJPES, 2022). In research, the main survey involved
317 medium-sized and large enterprises. e response rate was
63.4%. When considering non-responses in the questionnaire,
wetook into account the non-response of the element and the
non-response of the variable. Eight enterprises did not answer the
questionnaire, so we excluded them from consideration, 317
medium-sized and large enterprises answered the questionnaire
in full. According to the Companies Act (ZGD-1, 2022), for
medium-sized enterprises, the average number of employees in a
business year does not exceed 250, while for large enterprises, the
average number of employees in a business year exceeds 250
employees. According to the standard classication of enterprises
activities, managers or owners were from manufacturing (25.9%),
trade, maintenance, and repair of motor vehicles (23.9%),
information and communication activities (22.4%), nancial and
insurance activities (18.6%), professional, scientic and technical
activities (7.9%) and other diversied business activities (1.3%).
e biggest share of enterprises presents large enterprises (54.6%).
Medium-sized enterprises comprised 45.4%. According to gender,
57.1% of male and 42.9% of female respondents participated in
the study.
Research instrument
We used a questionnaire which was closed type a 5-point
Likert-type scale. e questionnaire was translated into the
Slovenian language. Items for construct AI supported acquiring
and retaining a talented employees were adopted from Kambur
and Akar (2021). e items for construct AI supported acquiring
and retaining a talented employees referred to the usefulness of AI
in acquiring and retaining a talented employees and which skills
are required for employment. Items for construct AI supported
appropriate training and development of employees were adopted
from Pillai and Sivathanu (2020) and referred to the usefulness of
FIGURE1
Conceptual model and hypotheses.
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Frontiers in Psychology 08 frontiersin.org
AI in training and development of employees. Items for construct
appropriate teams were adopted from Mikalef and Gupta (2021).
Items for construct appropriate teams refer to the work of team
members. An enterprise that uses articial intelligence technology
should have a well-formed team where team members produce
many novels and valuable ideas, they work without a leader, solve
problems independently, etc. Items for construct AI supported
organizational culture were adopted from Dabbous etal. (2022)
and relate to whether the organizational culture supports changes
and articial intelligence. Items for construct reducing the
workload of employees with AI were adopted from Qiu etal.
(2022) refer to whether the enterprise reduces employee stress
with the help of AI. Items for constructs AI supported leadership,
employee engagement and performance of the enterprise were
adopted from Wijayati et al. (2022). Items for construct AI
supported leadership relate to understanding business problems
and to direct AI initiatives to solve them, design AI solutions to
support customers needs, open communiacation, etc. Items for
construct employee engagement relate to the way employees are
engaged in their work. All items are presented in Table1.
Statistical analysis
We tested the hypotheses with the SEM and used the soware
tool WarpPLS 7.0. e WarpPLS 7.0 program was used to verify the
existence of eects between constructs. Wedecided to use WarpPLS
7.0 program because it oers many advantages and unique
solutions compared to others. Wesee one of the key advantages in
the possibility of explicitly dening non-linear connections
between pairs of latent variables (Kock, 2019). As part of the
validity, weexamined the AVE and CR (Kock, 2019). To check for
multicollinearity, weused VIF (Hair etal., 2010). Wealso used the
criterion of quality indicators listed in Table2 to test the model.
Research results
In addition to the results in Table 2, the total variance
explained for acquiring and retaining a talented employees is
68.345%, for appropriate training and development of employees
is 71.324%, for appropriate teams is 73.492%, for organizational
culture is 67.592%, for leadership is 75.289%, for reducing the
workload of employees is 72.178%, for employee engagement is
74.425% and for performance of the enterprise is 78.576%.
Table 3 shows key quality assessment indicators of the
research model.
Table3 shows that all indicators are suitable. e result of
indicator GoF shows that the model is highly appropriate. Table4
presents indicators of the quality of the structural model.
Table5 presents the results of SEM. Figure2 presents the
conceptual model with the values of path coecients.
e results in Table5 and Figure2 show that AI supported
acquiring and retaining a talented employees (AR → PC = 0.459,
p < 0.05; AR → EE = 0.443, p < 0.05), AI supported appropriate
training and development of employees (TD → PC = 0.536,
p < 0.05; TD → EE = 0.562, p < 0.05), appropriate teams
(AT →PC = 0.567, p < 0.01; AT→EE = 0.538, p < 0.01), AI supported
organizational culture (OC → PC = 0.449, p < 0.001;
OC → EE = 0.475, p < 0.01), AI supported leadership
(L → PC = 0.582, p < 0.001; L → EE = 0.574, p < 0.001) and reducing
the workload of employees with AI (RW → PC = 0.476, p < 0.05;
RW → EE = 0.451, p < 0.01) have an eect on performance of the
enterprise and employee engagement. Also, employee engagement
has an eect on performance of the enterprise (EE → PC = 0.649,
p < 0.01). us, weconrmed hypotheses H1–H13.
Discussion
Table5 and Figure2 show that AI supported acquiring and
retaining talented employees have a positive eect on performance
of the enterprise and employee engagement. AI supported
appropriate training and development of employees have a
positive eect on performance of the enterprise and work
engagement in Slovenian enterprises. Table2 shows that the most
important role in AI supported acquiring and retaining talented
employees is employing candidates who have the appropriate skills
to perform their work successfully. Nowadays, it is extremely
important for the success of an enterprise to select the right
candidates for the work. AI helps enterprises avoid the bias that
always occurs in the recruitment process when employers decide
on candidates. In addition, articial intelligence can help identify
opportunities for growth in existing employees, training needs,
and further advancement (Lee and Chen, 2022). Articial
intelligence can connect and make connections between employee
development opportunities and the arrival of new employees,
leading to higher employee engagement (Di Francescomarino and
Maggi, 2020). Lack of career advancement opportunities is one of
the common reasons for decreased employee engagement and
why employees decide to leave (Chiarini etal., 2020). erefore,
the enterprise should provide continuous learning programs such
as supplementary skills training and subsidies for seminars. Have
a solid career development and promotions system as part of the
enterprise benets. It is also imperative to make sure that the
enterprise is able to adapt to certain changes in the industry and
the overall business landscape. According to Spartaq. E-learning
(2022), using articial intelligence tools for employee education
increases their productivity by 30%. While learning with articial
intelligence tools, employee engagement is 18% better than
traditional methods, reducing the time required to learn by 65%.
Table 5 and Figure 2 show that appropriate teams have a
positive eect on performance of the enterprise and employee
engagement. Table 2 shows that the most important role of
appropriate teams is that all team members achieve their goals
equally eectively, followed by the team members producing
many novel and valuable ideas. According to Arslan etal. (2021),
successful teams are connected, accept diversity, and know how to
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TABLE1 Items for each construct.
Construct Item
AI supported
acquiring and
retaining a talented
employees
AR1: AI helps in a better quality of decisions for recruiting and selecting candidates.
AR2: AI helps in conducting primary interviews of bulk candidates using chatbots.
AR3: AI technology save the monotony of the job done during the process of nding candidates.
AR4: AI technology reduce the time spent in nding candidates.
AR5: Wehire those candidates that have the right skills to accomplish their work successfully.
AR6: Wehire those candidates that are very capable of using ai technologies (e.g., machine learning, natural language processing, deep learning).
AR7: Wehire those candidates that are eective in data analysis, processing, and security.
AR8: Wetake care of retaining suitable candidates with help to acquire the necessary skills for their career plan.
AI supported
appropriate training
and development of
employees
TD1: AI technology reduces the time spent on in-enterprise training courses.
TD2: AI technology reduces the attention decit that employee experienced in classical in-enterprise training courses processes.
TD3: AI technology increases accessibility to in-enterprise training courses.
TD4: In-enterprise training courses with articial intelligence technology lead to a successful training program.
TD5: Employee professional knowledge will bekept up to date with in-enterprise training courses through articial intelligence technology.
TD6: when the in-enterprise training courses take place with articial intelligence technology, the restrictions regarding to place where the training will begiven will beremoved.
TD7: Employees are provided with the required training to deal with AI applications.
Appropriate teams AT1: e team members produce many novels and valuable ideas (services/products).
AT2: e team members work without a leader.
AT3: e team members coordinate the work themselves.
AT4: Team members solve problems independently.
AT5: All team members work equally creatively and enthusiastically to nd ideas and solve problems.
AT6: All team members achieve their goals equally eectively.
AI supported
organizational
culture
OC1: e enterprise’s culture is very responsive and changes easily.
OC2: Weused AI technology in any part of our business.
OC3: ere is a high level of agreement about how wedo things in the enterprise.
OC4: ere is a shared vision of what enterprise will belike in the future.
OC5: Policies of the enterprise are clearly dened.
OC6: Employees fully understand the goals of our enterprise.
OC7: e enterprise’s management provides information to employees in a timely manner.
OC8: Employees are familiar with all the services / products weoer / produce in our enterprise.
AI supported
leadership
L1: Wedeveloped a clear vision for what was going to beachieved by our department.
L2: Weare able to understand business problems and to direct AI initiatives to solve them.
L3: Weare able to anticipate future business needs of functional managers, suppliers and customers and proactively design AI solutions to support these needs.
L4: Weare able to work with data scientists, other employees and customers to determine opportunities that AI might bring to our organization.
L5: Employees have strong leadership to support AI initiatives and are commitment to AI projects.
L6: In the enterprise prevails open communication and wesolve employees’ problems on the spot.
Reducing the
workload of
employees with AI
RW1: With AI wereduce the burden on administrative sta in the enterprise.
RW2: e AI technology applied in our enterprise can take orders and complete tasks which reduces the workload of employees.
RW3: e AI technology applied in our enterprise can communicate with users/customers which reduces the workload of employees.
RW4: e AI technology applied in our enterprise can search and analyze information which reduces the workload of employees.
RW5: Articial intelligence can help in getting the job done which saves employees work time.
Employee
engagement
EE1: Using AI enhance employee eectiveness.
EE2: Employees are engaged to the quality of their work.
EE3: Employees do their work with passion.
EE4: Employees are engaged to achieve successful business results.
EE5: Employees are aware of the importance of innovation for our enterprise and they are helping to develop the enterprise.
EE6: Employees are enthusiastic in their work.
EE7: Employees are engaged for business ideas and solutions.
EE8: Employees believe in the successful development and operation of our enterprise.
Performance of the
company
PC1: rough AI the enterprise can able to get accurate results.
PC2: rough AI the chance of employees error at work are less.
PC3: AI improves the eectiveness of decisions and actions.
PC4: AI accelerates making quick and better decisions to achieve successful results.
PC5: AI provides accurate data and information.
PC6: Products or services meet the expectations of customers.
PC7: e delivery of goods or services is conducted in a timely fashion.
PC8: Compared to our key competitors, our enterprise is growing faster.
PC9: Compared to our key competitors, our enterprise is more protable.
PC10: Compared to our key competitors, our enterprise is more innovative.
AI, articial intelligence.
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TABLE2 Factor analysis results.
Construct Item Communalities Loadings Cronbach’s alpha
AI supported acquiring and
retaining a talented employees
AR1 0.742 0.861 0.918
AR2 0.702 0.838
AR3 0.594 0.772
AR4 0.590 0.768
AR5 0.770 0.878
AR6 0.763 0.870
AR7 0.765 0.873
AR8 0.754 0.867
KMO = 0.908; Bartlett’s Test of Sphericity: Approx. Chi-Square = 2318.471, df = 28, p< 0.001
Cumulative percentage of explained variance: 68.345%
AI supported appropriate
training and development of
employees
TD1 0.708 0.841 0.897
TD2 0.847 0.912
TD3 0.838 0.906
TD4 0.726 0.869
TD5 0.734 0.875
TD6 0.845 0.897
TD7 0.849 0.908
KMO = 0.928; Bartlett’s Test of Sphericity: Approx. Chi-Square = 1671.946, df = 21, p< 0.001
Cumulative percentage of explained variance: 71.324%
Appropriate teams AT1 0.774 0.867 0.889
AT2 0.741 0.853
AT3 0.738 0.849
AT4 0.711 0.827
AT5 0.748 0.858
AT6 0.763 0.869
KMO = 0.895; Bartlett’s Test of Sphericity: Approx. Chi-Square = 1421.645, df = 15, p< 0.001
Cumulative percentage of explained variance: 73.492%
AI supported organizational
culture
OC1 0.726 0.839 0.869
OC2 0.711 0.843
OC3 0.694 0.802
OC4 0.673 0.818
OC5 0.823 0.904
OC6 0.772 0.896
OC7 0.706 0.824
OC8 0.718 0.851
KMO = 0.872; Bartlett’s Test of Sphericity: Approx. Chi-Square = 1362.285, df = 28, p< 0.001
Cumulative percentage of explained variance: 67.592%
AI supported leadership L1 0.874 0.922 0.876
L2 0.774 0.878
L3 0.858 0.916
L4 0.708 0.834
L5 0.861 0.920
L6 0.765 0.866
KMO = 0.884; Bartlett’s Test of Sphericity: Approx. Chi-Square = 1572.285, df = 15, p< 0.001
Cumulative percentage of explained variance: 75.289%
Reducing the workload of
employees with AI
RW1 0.724 0.851 0.940
RW2 0.683 0.826
RW3 0.699 0.836
RW4 0.694 0.833
RW5 0.638 0.799
(Continued)
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nd a common language. In this way, the teams will focus on
dierent views on solving and achieving the enterprise’s successful
goals (Webber etal., 2019; Arslan etal., 2021). erefore, for a
successful team, it is necessary to select individuals with dierent
expertise and personality types that complement each other.
Successful articial intelligence teams also exhibit empathy for
customers and other users. is ultimately paves the way for
solving problems holistically. Understanding the problem to depth
makes the individuals creative, curious, and innovative beyond
imagination. According to Shaikh and Cruz (2022), for a team to
beproductive and eective, its members must beunited by the
same vision and committed to bring that visionto life (Shaikh and
Cruz, 2022).
Moreover, for an enterprise to become ready for the future, its
leaders must create an innovative organizational culture (Behl
etal., 2021). Organizational culture is key to building an articial
intelligence-driven enterprise (Munir etal., 2022). e enterprises
that manage to build a positive articial intelligence culture and
an inclusive and inspiring environment will successfully manage
change and attract all their employees with articial intelligence
teams (Behl etal., 2021; Jarrahi etal., 2022). is is in line with our
research ndings that AI supported organizational culture has a
positive eect on employee engagement and performance of the
enterprise. Table 2 shows that the most important role of AI
supported organizational culture is that policies of the enterprise
are clearly dened, followed by employees fully understanding the
enterprise’s goals. e third important role of AI supported
organizational culture is that employees are familiar with all the
services/ products that oer/produce in an enterprise, followed by
using AI technology in any part of business and the enterprise’s
culture is very responsive and changes easily. e average level of
agreement with the statement “we used AI technology in any part
of our business” is 3.58, which means that employees on average
agree but do not completely agree. e results of the survey show
that enterprises are embarking on the implementation of articial
intelligence and changing their organizational culture to embrace
AI, but the average value of agreement is still low. In the modern
economy, data is an invaluable resource in any business. AI is
eective at quickly processing data to generate relevant answers to
any questions that arise in business. us, the main aim of leaders
is to create an organizational culture that will allow the
organization to quickly develop and adapt to new business realities.
Table5 and Figure2 show that AI supported leadership has a
positive eect on performance of the enterprise and on employee
engagement. Table 2 shows that the most important role of AI
supported leadership is to developed a clear vision for what was
going to beachieved by department, followed by strong leadership
to support articial intelligence initiatives. According to Wan g
(2021), leaders are an essential part of any enterprise’s success.
Leaders provide the vision that drives other enterprise employees to
TABLE2 (Continued)
Construct Item Communalities Loadings Cronbach’s alpha
KMO = 0.918; Bartlett’s Test of Sphericity: Approx. Chi-Square = 3275.217, df = 10, p< 0.001
Cumulative percentage of explained variance: 72.178%
Employee engagement EE1 0.860 0.927 0.948
EE2 0.828 0.910
EE3 0.851 0.922
EE4 0.714 0.837
EE5 0.732 0.856
EE6 0.728 0.853
EE7 0.806 0.898
EE8 0.706 0.728
KMO = 0.925; Bartlett’s Test of Sphericity: Approx. Chi-Square = 3062.092, df = 28, p< 0.001
Cumulative percentage of explained variance: 74.425%
Performance of the company PC1 0.775 0.880 0.948
PC2 0.825 0.908
PC3 0.669 0.836
PC4 0.879 0.942
PC5 0.806 0.898
PC6 0.837 0.915
PC7 0.874 0.939
PC8 0.765 0.875
PC9 0.893 0.952
PC10 0.881 0.946
KMO = 0.938; Bartlett’s Test of Sphericity: Approx. Chi-Square = 5203.703, df = 45, p< 0.001. Cumulative percentage of explained variance: 78.576%
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TABLE5 Standardized path coecients for the proposed model.
Hypothesized path Path coecient
(γ)Sig. Eect size
(ƒ2)Standard error Link direction Shape of link
AR → PC 0.459 p< 0.05 0.354 0.031 Positive Nonlinear
TD → PC 0.536 p< 0.05 0.412 0.031
AT→PC 0.567 p< 0.01 0.389 0.030
OC → PC 0.449 p< 0.001 0.352 0.031
L → PC 0.582 p< 0.001 0.421 0.030
RW → PC 0.476 p< 0.05 0.376 0.032
AR → EE 0.443 p< 0.05 0.357 0.031
TD → EE 0.562 p< 0.05 0.459 0.030
AT→EE 0.538 p< 0.01 0.395 0.030
OC → EE 0.475 p< 0.01 0.356 0.032
L → EE 0.574 p< 0.001 0.465 0.031
RW → EE 0.451 p< 0.01 0.353 0.031
EE → PC 0.649 p< 0.01 0.517 0.029
AR, acquiring and retaining a talented employees; TD, appropriate training and development of employees; AT, appropriate teams; L, leadership; RW, reducing the workload of
employees; PC, performance of the company; EE, Employee engagement.
realize their goals (Niehueser and Boak, 2020; Wang, 2021).
Technology has strongly interfered with existing ways of working,
especially in routine and repetitive tasks. e trend will only intensify
with the development and application of articial intelligence, which
will greatly change leaders’ work (Wijayati etal., 2022). Today, the
leaders need to inculcate the right skills to help enterprises maintain
a sense of competitiveness in aspects of upskilling as well as initiating
mentoring for the betterment of the teams.
TABLE3 Model fit and quality indicators.
Quality indicators e criterion of quality indicators Calculated values of indicators of model
Average path coecient (APC) p < 0.05 0.528, p< 0.001
Average R-squared (ARS) p < 0.05 0.631, p < 0.001
Average adjusted R-squared (AARS) p < 0.05 0.674, p < 0.001
Average block variance ination factor (AVIF) AVIF <5.0 1.339
Average full collinearity VIF (AFVIF) AFVIF <5.0 1.482
Goodness-of-t (GoF) GoF ≥ 0.1 (low)
GoF ≥ 0.25 (medium)
GoF ≥ 0.36 (high)
0.624
Simpson’s paradox ratio (SPR) SPR ≥ 0.7 1.000
R-squared contribution ratio (RSCR) RSCR ≥0.9 1.000
Statistical suppression ratio (SSR) SSR ≥ 0.7 1.000
Nonlinear causality direction ratio (NLBCD) NLBCD ≥0.7 1.000
TABLE4 Indicators of quality of the structural model.
Constructs CR AVE R2Adj. R2Q2VIF
AI supported acquiring and retaining a talented employees 0.936 0.684 (−) (−) (−)1.161
AI supported appropriate training and development of employees 0.952 0.738 (−) (−) (−) 2.296
Appropriate teams 0.944 0.707 (−) (−) (−) 2.301
AI supported organizational culture 0.926 0.715 (−) (−) (−) 1.076
AI supported leadership 0.958 0.740 (−) (−) (−) 1.865
Reducing the workload of employees with AI 0.962 0.826 (−) (−) (−) 1.185
Employee engagement 0.952 0.869 0.473 0.458 0.379 1.071
Performance of the company 0.970 0.865 0.469 0.450 0.326 1.042
(−) values cannot becalculated because the construct is a baseline.
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Table5 and Figure2 show that reducing the workload of
employees has a positive eect on employee engagement and
performance of the enterprise. Table 2 shows that the most
important role of reducing employees’ workload with AI is to
reduce the burden on administrative sta with articial
intelligence in the enterprise, followed by articial intelligence
technology applied can communicate with users/customers,
which reduces the workload of employees. Reducing employee
workloads can have a tremendous impact on stress levels and free
up their schedules to focus on tasks that articial intelligence
cannot automate. According to a survey of 34.000 employees in
18 countries, 72% of working professionals attribute low stress
levels to productivity boosting tools and tech. Employees are
welcoming articial intelligence-powered automation technology
as it will help them work more eectively and reduce stress. us,
the implementation of articial intelligence into an enterprise has
a positive eect on performance of the enterprise. Enterprises
that have adopted articial intelligence in their operations have
seen great success (Palanivelu and Vasanthi, 2020). Table2 shows
that the most important role of performance of the enterprise is
theprotability and innovativeness of the enterprise. Following,
articial intelligence accelerates making quick and better
decisions to achieve successful results. According to Bag etal.
(2020), Chiarini et al. (2020), Lezoche et al. (2020), and
Wamba-Taguimdje etal. (2020), as enterprises are becoming
more employee-centric, articial intelligence is helping them
create a happier work environment, increase workforce’s
productivity and creating a positive experience which lead to
higher employee engagement and performance of the enterprise.
Theoretical and managerial implications
e implementation of articial intelligence in an enterprise
is a comprehensive change of the enterprise’s processes and may
lead to greater productivity, growth and competitiveness of the
enterprise. e use of AI technology oers new business
opportunities to enterprises, greater productivity, new ways of
designing business models of enterprises, encourages
innovation and development, and new ways of promoting.
From this point of view, wehave developed a multidimensional
talent management model with embedded aspects of articial
intelligence in the human resource processes to increase
employees’ engagement and performance of the enterprise. e
research model highlights the importance of certain aspects
that are necessary for the successful operation of an enterprise
in today’s rapidly changing environment. us, regardless of the
size of the enterprise, the following aspects must betaken into
FIGURE2
The conceptual model with the values of path coecients.
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Frontiers in Psychology 14 frontiersin.org
account when implementing articial intelligence to increase
performance of the enterprise and employee engagement: AI
supported acquiring and retaining talented employees, AI
supported appropriate training and development of employees,
appropriate teams, AI supported organizational culture, AI
supported leadership and reducing the workload of employees
with AI. e development of a digital society and the
digitization of the economy requires appropriate legislation and
an environment that will encourage the development of
digitization and digital entrepreneurship. It is necessary to
facilitate access to nancial resources for enterprises, especially
for SMEs, so that they can more easily access funds for nancial
investment in technological development, digital
transformation, articial intelligence, knowledge and skills,
which will help them to bemore competitive in Slovenia and in
the international business environment.
Limitations and further research
Our study is limited to a sample of managers/owners in
Slovenian enterprises. Limitations of our research are reected in
the size of enterprises because weselected medium-sized and
large enterprises. e results show the situation of medium-sized
and large enterprises and do not provide conclusions for small
enterprises, especially in terms of whether the use of AI is suitable
for small enterprises. e challenge faced by micro, small and
medium-sized enterprises (SMEs) is certainly the fear of changes
brought about by digitization and digital transformation of
enterprises. When dealing with challenges, SMEs oen have
insucient information, nancial resources and personnel who
have the appropriate skills for the digital transformation of the
enterprise. e limitations of our research are also reected in the
constructs that we have chosen for the survey. erefore,
werecommend further research to develop new constructs for
example, implementation of management information system
into the enterprise, adopting AI technologies, using AI solutions
in a project, enterprise’s competitiveness and analyze them
through structural equation modeling. Also, for further research,
wesuggest the examination of constructs that wehave chosen for
the survey in other countries to compare the results.
Author’s note
Artificial intelligence has taken over the enterprise sector
and changed the way enterprises do business today. Thus, the
new advancing trends like artificial intelligence and other
cutting-edge technologies are transforming the way of work,
changing the workforce’s profile. The paper highlights the
important constructs for successfully implementing artificial
intelligence applications in the enterprise to increase
employee engagement and performance of the enterprise. The
main highlights that enterprises need to beaware of when
implementing artificial intelligence are improving employee
engagement and productivity, gaining knowledge in the field
of artificial intelligence, and increasing an enterprise’s
profitability. Artificial intelligence is becoming part of the
digitization strategy as it enables enterprises to leverage vast
amounts of process data with advanced algorithms to improve
efficiency and reduce production costs. The use of artificial
intelligence in production will beespecially increased for
quality control. Artificial intelligence is very promising for
visual inspection of parts at different stages of the product
development process and in all workplaces, including
production and assembly, with the aim of achieving a certain
quality. As the demand for innovative products increases
rapidly, the use of artificial intelligence is becoming essential
to understanding customer needs, requirements, and desires.
Data availability statement
e original contributions presented in the study are included
in the article/supplementary material, further inquiries can
bedirected to the corresponding author.
Ethics statement
Ethical review and approval was not required for the study on
human participants in accordance with the local legislation and
institutional requirements. Written informed consent from the
patients/participants or patients/participants legal guardian/next
of kin was not required to participate in this study in accordance
with the national legislation and the institutional requirements.
Author contributions
All authors listed have made a substantial, direct, and
intellectual contribution to the work and approved it for publication.
Funding
is work has been fully supported by Croatian Science
Foundation under the project UIP-2020-02-6312. e authors
acknowledge the nancial support from the Slovenian Research
Agency (research core funding No. P5–0023, ‘Entrepreneurship
for Innovative Society).
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
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Publisher’s note
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authors and do not necessarily represent those of their aliated
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