SYNERGETIC EFFECTS OF INTEGRATED COLLABORATION BETWEEN HUMANS AND SMART SYSTEMS IN BANKING: AN OVERVIEW

Abstract

This paper aims to shed light on the role of artificial intelligence in banking, the phenomenon which will continue to reshape business models at an accelerated pace, implying the development of a new landscape, founded on synergetic effects of integrated collaboration between humans and smart systems. These synergetic effects bring many opportunities and challenges at the same time. To create sustainable value for an organization from a harmonized collaboration between humans and smart machines, changes are expected in both, hard and soft components of any organization, with the primary focus on AI strategies development, organizational culture, "fusion skills" and knowledge economy. Future management practice relies heavily on the adoption of new managerial and leadership knowledge and skills, implying empowerment of human-centric approach, emotional intelligence, creativity, and flourishing paradigm "leading by heart".

Full text

683
SYNERGETIC EFFECTS OF INTEGRATED
COLLABORATION BETWEEN HUMANS
AND SMART SYSTEMS IN BANKING: AN
OVERVIEW
Vesna Tornjanski*1, Snežana Knežević1, MSc Stefan Milojević2
1University of Belgrade, Faculty of Organizational Sciences
2Director of Sector "Fraud Prevention and Fraud Investigations" at Audit, accounting, financial and
consulting services company MOODYS STANDARDS, RS
*Vesna Tornjanski, e-mail:vtornjanski@gmail.com
Abstract: This paper aims to shed light on the role of artificial intelligence in banking, the phenomenon
which will continue to reshape business models at an accelerated pace, implying the development of new
landscape, founded on synergetic effects of integrated collaboration between humans and smart systems.
These synergetic effects bring many opportunities and challenges at the same time. To create sustainable
value for an organization from a harmonized collaboration between humans and smart machines, changes
are expected in both, hard and soft components of any organization, with the primary focus on AI strategies
development, organizational culture, "fusion skills" and knowledge economy. Future management practice
relies heavily on the adoption of new managerial and leadership knowledge and skills, implying
empowerment of human-centric approach, emotional intelligence, creativity, and flourishing paradigm
"leading by heart".
Keywords: Artificial intelligence, integrated collaboration between humans and smart systems, human-
centricity, AI strategy, knowledge economy, fusion skills.
1. INTRODUCTION
The business ecosystem that consists of various, dynamic networks and components that interact with each
other at an accelerated pace represents environment characterized by many challenges and opportunities
for organizations in the banking sector. Digitization determines the rules of competition. Organizations that
are founded on traditional business models are at risk of being left behind (Lauterbach & Bonim, 2016).
Overall trends in the business ecosystem will create a difference between competing banks in the future and
significantly reshape the business landscape. In addition, the fast-changing marketplace and "COVID-19 era"
put sustainability in the narrow focus of executives, implying continuous reconsideration of strategic direction
and development of competitive strategies (Gartner, 2017; Tornjanski et al., 2017).
Innovations are widely recognized as a key value driver for economic growth, sustainability, and
development of organizations and core of competitive advantage in banking (Porter & Ketels, 2003;
Tornjanski et al., 2015). Moreover, some authors argue that organizations may achieve improvements in
business performance with clear and unambiguous innovation and open innovation strategies (Löfsten,
2014; Tornjanski et al., 2015a; Tornjanski, 2016). Aslam et al. (2020) have proposed the "Absolute
Innovation Management (hereafter: AIM)" framework that synergizes innovation ecosystem, design thinking,
and corporate strategy with twofold objectives, to make innovation concept more practical, and to prepare
organizations for forthcoming IoT and Industry 5.0 revolution.
1.1. Shifting from Industry 4.0 to Industry 5.0
The birth of Industry 4.0 relies heavily on disruptive technologies: Internet of Things (Hereafter: IoT), Cyber-
Physical System (Hereafter: CPS), Information and communications technology (Hereafter: ICT), Enterprise
Architecture (Hereafter: EA), and Enterprise Integration (Hereafter: EI) (Lu, 2017). Industry 4.0 was
introduced in 2011 by the German Government, when new information technologies penetration secured

684
"the world to stand on the threshold of the 4th industrial revolution" (Skobelev & Borovik, 2017, p. 307).
Industry 4.0 is based on four key principles: interoperability, decentralization, technical assistance, and
information transparency, representing flywheel for business transformation (Paschek et al., 2019). Almada-
Labo (2017) depicted the key benefits of Industry 4.0 changes in manufacturing companies that encompass:
efficiency, agility, innovation, customer experience, costs, and revenues (Paschek et al., 2019). With
reference to Industry 4.0 and the continuous adoption of new digital practice, traditional business models are
increasingly faced with high pressure to respond to all internal and external challenges. Tornjanski et al.
(2015b) have synergized various aspects of the digital disruption in the banking sector, pointing out the
suitable framework for banks to effectively deal with the 4th industrial revolution (Sundaram et al., 2020) that
signifies digitalization - a phenomenon that drives all societies and businesses worldwide (Paschek et al.,
2019). Fitzgerald et al. (2014) have recognized:
 Streamlining of operations,
 Better customer experience and
 Integration of innovation into the existing business model
as key benefits organizations may achieve by adopting digital transformation. However, despite all
recognized opportunities, organizations still struggling to achieve expected benefits from all these
opportunities. Key recognized constraints lie in managerial behavior in terms of the sense of urgency,
leadership quality, organizational culture, IT systems, unclear roles, and lack of vision (Sundaram et al.,
2020). Although Industry 4.0 takes a significant part in our daily lives (Ozkeser, 2018), Skobelev & Borovik
(2017) are with the opinion that Industry 4.0 is at the initial stage of the development, expecting real
business achievements in years to come.
On the other side, Industry 5.0, named: "Society 5.0" flourishes. Industry 5.0 represents a core concept and
growth strategy for Japan, adopted by the Japanese Cabinet in 2016. "Society 5.0" is a strongly promoted
concept outside the country's boundaries as a response to global challenges (Fukuyama, 2018). Faruqi
(2019) explained Industry 5.0 as an idea that reshapes people's lives with the extended development of
Industry 4.0, by integrating both, technology and humanities aspects. Similarly, Ozkeser (2018) has shown
the main difference between Industry 4.0 and Industry 5.0. Industry 5.0 concept shifts focus to increased
human-smart machine interaction while empowering people towards personalization (Aslam et al., 2020).
According to Fukuyama (2018), the world is faced with increased uncertainty and complexity, caused by
numerous challenges of global scale such as COVID-19, global warming, exploitation of natural resources,
growing economic disparity, and terrorism. When Japan is in question, Fukuyama (2018) has recognized
specific challenges that encompass: declining birth rate, increased social security costs, and shrinking labor
force. Moreover, Fukuyama (2018) noted that these challenges of Japan's society may be threats for many
other countries in the future, too. Accordingly, Society 5.0 is developed as a response to all global
challenges that require the adoption of open innovation concept in the social ecosystem, by the active
participation of multiple stakeholders at the global scale, in which government, academy, and industry play a
leading role. More specifically, Industry 5.0 aims at maximizing leverage ICT to acquire new knowledge and
to create sustainable value by integrating "people and things" and "real and cyber" worlds to efficiently and
effectively ensure better quality of life, sustainable and healthy economic growth. In other words, the purpose
of Industry 5.0 development is to shift focus to human-centricity by synergizing effects of integrated
collaboration between humans and smart systems (Fukuyama, 2018).
2. OVERVIEW OF ARTIFICIAL INTELLIGENCE
Artificial Intelligence (hereafter: AI) is viewed as a big game-changer in the global marketplace (Rao &
Verweij, 2017). AI dates back for more than 60 years (Duan et al., 2019, Sharma et al., 2020) and represents
a phenomenon that has been subjected to public discourses for a decade (Dwivedi et al., 2019). Over time,
AI has arrived in the present as an emerging strategic topic, attracting numerous and different views from
leading experts (Duan et al., 2019). Despite all controversial opinion regarding AI, PwC research results
predict an increase in GDP up to 14% higher in 2030 at the global scale, as a result of AI. The results of the
analysis are viewed as a significant commercial potential for the world, which can be achieved on the basis
of AI (Rao & Verweij, 2017). Moreover, the report shows that the greatest benefits from AI are likely to be in
China, by boosting up to 26% GDP and North America, by boosting up to 14% GDP in 2030. From the
sectors' perspective, the highest values are expected to be in retail, financial services, and healthcare (Rao
& Verweij, 2017).

685
2.1. Defining AI
There is no unique definition of AI. According to Duan et al. (2019), AI refers to a machine's ability to learn
from experience, adapt to new inquires, and simulate human cognitive tasks. Similarly, Russell & Norving
(2016) view AI as "systems that mimic cognitive functions generally associated with human attributes such
as learning, speech and problem solving" (Duan et al., 2019, p. 2). A broader definition is given by Rao &
Verweij (2017), who understand AI as a term for computer systems with a sense for the environment,
machines that think, learn, and take actions at the same time. Dwivedi et al. (2019) have synthesized all
definitions of AI and concluded that all concepts rely on non-human intelligence systems that are
programmed to carry out specific tasks. In other words, AI represents the increasing capability of machines
to provide particular roles and cognitive assignments, currently performed by humans in overall society.
Lauterbach & Bonim (2016) define AI as one component of computer science that deals with machine
learning and enables software to carry out problem-solving akin to the cognitive intelligence of humans.
Authors have analyzed AI from two key perspectives:
 Narrow AI: AI that is incorporated into the system to perform specific tasks, and
 Deep AI or AGI: AI that is conceptualized to "think in general", designed after the neural networks of
the human brain (Lauterbach & Bonim, 2016).
Narrow AI includes speech and text recognition, expert systems, heuristic classification systems, knowledge
engineering, gaming technologies, advances in algorithms of data mining. Examples of narrow AI are given
in text hereafter: Google, Uber, Amazon Alexa, Connected Home, Drones, Kespry, Drive ai. Connected Car,
Building Robots, Robots, Neurensic Financial (Lauterbach & Bonim, 2016).
On the other hand, Deep AI or AGI represents an emerging area with a primary objective to build thinking
machines with intelligence that can be compared to human cognitive intelligence. Examples of deep AI are
as follows: Vicarious, Curious AI Co., Deep Mind, Watson, GoodAI, Fanuc, Numenta (Lauterbach & Bonim,
2016).
2.2. AI values, opportunities, and challenges
Dwivedi et al. (2019) have recognized AI technology is no longer an area of futurologists, but an integral
element of a business model and key strategic priority in plans for many organizations in various sectors,
worldwide. AI as the key source of a new wave of disruption, transformation and competitive advantage (Rao
& Verweij, 2017) brings simultaneously value to the business, opportunities, and challenges that should be
holistically perceived and properly addressed to all stakeholders including government, academia, and
industries.
The existing theoretical fund records factors that contributed to the AI development, i.e.: the growth of data,
cloud technology, better algorithms, gaps in smart networks, cyber insecurity, and development (coding)
mistakes (Lauterbach & Bonim, 2016). Duan et al. (2019) have developed research propositions with
reference to opportunities and challenges from different perspectives when the application of AI for decision
making is in question. Duan et al. (2019) have concluded that Big Data, improved computing storage, and
power, advanced algorithms represent key driving forces of AI popularity nowadays.
The existing literature suggests that AI is likely to have huge opportunities in various sectors and domains.
For example, Sharma et al. (2020) have carried out an analysis of existing literature with reference to AI in
the government sector. According to the research results, it may be concluded that potential in investments
and adoption of AI lies in (Sharma et al., 2020):
 Education,
 Healthcare,
 Physical infrastructure,
 Transportation,
 Data security and management,
 Telecommunication,
 Research and development,
 Finance,
 Legal and justice system,
 Policymaking,
 Public safety,
 Defense,
 Predictive maintenance and many others.

686
The purpose of AI application in the government sector is twofold:
 Efficiency improvement for the government and
 Better quality of life for citizens (Marda, 2018; Sharma et al., 2020).
On the other hand, research results depicted in the PwC report indicate sectors' and products' impact of AI,
aiming at enabling businesses to recognize opportunities and threats as well as the capacity for greatest
return of investment. The general AI application area according to Rao & Verweij (2017) is shown in Table 1.
Table 1: PwC AI index evaluation according to the sector
Number
Sector
Consumption impact*
1
Healthcare
3.7
2
Automotive
3.7
3
Financial services
3.3
4
Transportation and logistics
3.2
5
Technology, Communication, and Entertainment
3.1
6
Retail
3.0
7
Energy
2.2
8
Manufacturing
2.2
*Consumption impact represents PwC AI index evaluation, where 1 indicates the lowest potential impact, while 5 signifies the highest potential
impact.
Source: PwC Publication (Rao & Verweij, 2017).
Moreover, Rao & Verweij (2017) are with the opinion that robotic doctors may be one of the revolutionary
changes. However, when the application of deep learning (AGI) is in question, Sharma et al. (2020) argue
that there are still open questions needed to be further researched in the context of better understanding the
range and effects of AI-based implementation, including a holistic view of all related challenges this area
implies. Duan et al. (2019) have analyzed key opportunities and challenges of AI from different perspectives.
Results have shown that both, opportunities and challenges lie in the same area, i.e. theoretical
development, technology-humans interactions, and AI implementation.
Wilson & Daugherty (2018) understand the value for an organization is joining forces of collaboration
between humans and AI. The collaborative value that is based on humans and AI interaction primarily lie in:
 Flexibility,
 Speed,
 Scale,
 Decision making and
 Personalization.
According to the presented results in the report, these five elements may be beneficial for all organizations
in all industries (Wilson & Daugherty, 2018).
However, there are still lots of challenges that the AI revolution brings, which should not be neglected, but
rather should be taken into account for further analysis and beneficial solutions development. For example,
Dwivedi et al. (2019) in their recently published paper have analyzed AI-based challenges from a
multidimensional perspective. Recognized challenges are retrieved from existing literature and are grouped
into seven aspects, shown in Table 2.
Table 2: AI challenges retrieved from the literature
AI challenge
aspects
AI challenge details
Political, legal and
policy dimension
 National security threats
 Privacy and safety
 Copyright issues
 Responsibility and accountability
 Lack of clear rules
 Autonomous intelligent system governance
 Lack of officially industry standards and performance evaluation
 Competent human resources that are legally required to deal with the AI-
based decisions
Economic dimension
 Costly patients
 Computational expenses transparency

687
 High costs and reduced profits for hospitals
Social dimension
 Insufficient knowledge of values and advantages of AI
 Cultural limitations
 Human rights
 Unrealistic expectations from AI
 Patient / Clinician education
 Country specific medical practices
 Country specific disease profiles
Organizational and
managerial
dimensions
 Organizational resistance to data sharing
 Lack of AI strategy development
 Lack of in-house AI talents
 Lack of interdisciplinary talents
 The threat of humans replacement
 Realism of AI
Technology-related
dimension
 Lack of interoperability and transparency
 AI safety
 Specialization and expertise
 Architecture issues and complexities in interpreting unstructured data
 Big data
Ethical dimension
 Lack of trust regarding AI-based decision making
 Unethical utilization of shared data
 Responsibility and explanation of the decision made using AI
 Moral dilemmas and AI discrimination
 Processes concerning AI and human behavior
 Compatibility between human assessments and judgment in comparison
with machines
Data dimension
 Lack of data to certify benefits from AI
 Quality and quantity to input data
 Reproducibility
 Transparency
 Available data pool size
 Data collection standards
 Lack of data integration and continuity
 Format and quality
 Dimensionality constraints
Source: Dwivedi et al. (2019)
Based on the holistic overview of artificial intelligence, its values, opportunities, and challenges, the next
chapter aims to narrow focus on the future perspective of integrated collaboration between humans and
smart systems in the banking industry, and further development of synergetic effects by incorporating joined
forces of both toward sustainability.
3. SYNERGETIC EFFECTS OF INTEGRATED COLLABORATION BETWEEN HUMANS AND
SMART SYSTEMS IN BANKING
3.1. Overview of banking landscape
Continuous development of the banking sector represents a vital component in an overall country's financial
sector stability and growth. The significance of the banking sector lies in its role incorporated in shaping the
industrial structure and the country's economic growth through extending and deepening financial markets
(Ye et al., 2019). On the other side, recent decades for the banking sector are characterized by numerous
challenges as a result of various external forces that foster various types, sizes, and shapes of changes to
both, market and organizations at high speed (Čudanov et al., 2019). To sustain competitively, banks have
undertaken various strategic actions to simultaneously:
 Fit effectively to ever-present external changes and
 To ensure internal growth.
These strategic perspectives opted on key components that strengthen organizational performances and
growth on the long run (Fasnacht, 2009; Tornjanski et al., 2014; Tornjanski et al., 2015; Tornjanski et al.,
2017):

688
 Ambidextrous development of an organization,
 Tailoring of appropriate strategies towards long-term vision achievement and
 Creation of a strong operating model to foster time to market and ensure smooth short-term
implementation capabilities.
Continuous banks' adaptation to the 4th Industrial revolution creates benefits, opportunities, costs, and risks
at the same time, implying strong strategic alignment to all internal and external components in the business
ecosystem, aiming at achieving sustainable competitiveness based on smart investments and shift in
generating and managing intellectual property. More specifically, this shift is founded on principles of the
holistic innovation management approach, primarily focused on generating value for the business through
(Chesbrough, 2006; West & Gallagher, 2006; Vargo et al., 2008; Tornjanski et al., 2014; Tornjanski et al.,
2015; Tornjanski et al., 2015a):
 Open business models,
 Open innovation management and
 Value co-creation process.
From an innovation perspective, artificial intelligence is one of the emerging subjects that should be further
better understood in the context of achieving overall performances and sustainable growth in the banking
industry. To this end, Rao & Verweij (2017) have clustered the biggest AI potential from the perspective of
time as follows:
 Early adoption
 Potential for the medium-term period
 Potential for the long-term period
3.2. AI in banking: Early adoption
Artificial intelligence in the banking industry has found its place in terms of the initial phase of implementation
and further opportunities for adoption in various domains of a banking business. According to the report
published by PwC, key areas of the biggest AI potential include (Rao & Verweij, 2017):
 Personalized financial planning,
 Anti-money laundering,
 Fraud detection and
 Process automation
Robo-advisers, insurance underwriting, and robotic process automation in various areas of banking
operations are recognized domains that fit into early "AI birds" perspective when the time horizon is in
question (Rao & Verweij, 2017). Many banks across the globe integrate FinTech into services to effectively
respond to customers' high demands. FinTech understood as a specialized banking branch for intelligence
machines, enables more flexibility, alternatives, and control over banks' products and services (Lui & Lamb,
2018).
Early adoption of AI in the banking industry was introduced in the UK and refers to banking applications that
are based on voice recognition. Other banks, such as Bank of America, Swedbank, Societe Generale, and
Capital One have introduced chatbots with the purpose to virtually assist and interact with customers in the
financial advising area using online web chat or textual messages. According to the experimental results
related to virtual assistants, key benefits that are recognized by customers lie in strong confidence, trust,
accurateness, efficient services, and 24/7 availability. Moreover, wealth management is viewed as one of the
beneficial areas when the utilization of virtual assistants is in question (Lui & Lamb, 2018).
The mortgage application area is recognized as one of the AI potentials that might be achieved in the short-
term period. To reduce the time-consuming and bureaucratic process, two approaches were introduced in
banking. One approach enables Robo-advisers in synergy with the online broker community to interact with
customers. Another approach is based on creating synergy between "digital mortgage advisors" and human
advisers (Lui & Lamb, 2018). Nevertheless, virtual assistants have limitations that should be taken into
account. At initial implementation phase of chatbot queries, which are simple and repetitive in its basis, there
are no doubts that humans will continue to assist and interact with customers for more complex subjects in
the financial advising area, regardless of customers' demands that might be in correlation with products,
services and/or discussion on a specific topic initiated by customers (Lui & Lamb, 2018). The future
perspective of AI adoption for the financial advising area lies in providing all complex and non-repetitive
answers to customers, which signifies a lot of challenges that should be further taken into account before
going forward. The key challenge is recognized in providing inaccurate answers by virtual assistants, which

689
could disturb the trust and confidence of customers (Lui & Lamb, 2018). As such, future perspective in
financial advising may create various risks that should be carefully evaluated and customized to fit strategic
objectives and organizational capabilities.
Leonov et al. (2020) have recognized a need to develop an analytical model that is based on artificial
intelligence technology for ATM service. The purpose of the model is to optimize cash collection, i.e. to
(Leonov et al., 2020):
 Reduce the number of cash collection and
 Secure a sufficient number of banknotes to satisfy customers' demand, timely.
The AI-based analytical model is designed to open a new perspective to the banks by providing efficient
cash flow management in ATM networks and effective cash management to reduce overall costs related to
financial monitoring (Leonov et al., 2020).
Examples of early AI adoption potential in banking have multidimensional values. The synergy that is created
between AI and humans in the financial advising area satisfies customers' needs in one hand and increases
the quality of work to humans on the other, making services more personalized, efficient, and effective at the
same time. In other words, joint forces of AI and humans show that benefits may be achieved in cost
management, increased quality in service delivery, better customer experience, employees' and customers'
satisfaction by boosting a human-centric and customer-centric approach.
On the other side, the AI potential in the medium-time period encompasses consumer sentiments and
preferences with the purpose to optimize product design in banking (Rao & Verweij, 2017). According to Lui
& Lamb (2018), the AI area is both, wide and deep, which opens a lot of space for further research to better
understand all hidden benefits and challenges in the long run that should be taken into account when
developing AI strategies.
3.3. Future perspective of integrated collaboration between humans and smart systems in
banking: towards sustainability - Society 5.0
Sustainability has been analyzed from numerous perspectives in theory over decades. Various definitions of
the concept strengthen views on the phenomenon, which indicate its multi-dimensional and complex nature.
Accordingly, sustainable development should be taken into account as an integrated system of economic,
social, ecological, and institutional aspects to shed light on its real value in the ecosystem (Ciegis et al.,
2009; Tornjanski et al., 2017). Society 5.0 represents a vision of sustainability, which is grounded in the 4th
Industrial revolution and primarily focused on the human-centric approach development in the fifth phase
(Fukuyama, 2018; Fukuda, 2020). Society 5.0 aims at designing a cyber-physical society to enhance the
quality of life of people. The vision relies significantly on the close collaboration between humans and
artificial intelligent systems (Gladden, 2019).
Translating Society 5.0 vision at the level of the banking sector, human-centricity has attracted attention due
to three underlying and correlated elements that directly and/or indirectly contribute to the overall
organizational performances and long-term sustainability (Tornjanski & Milosavljević, 2016; Tornjanski et al.,
2017):
 Significance of employees' work-life balance
 Efficient and effective service to customers
 Quality (in terms of services and work)
To achieve sustainability, Society 5.0 focuses on the deep integration of rapidly evolving technologies into
daily operations, while Industry 4.0 focuses on production utilization within business segments. Industry 4.0
paradigm is understood to create a "smart factory", yet, Industry 5.0 is conceptualized to design "super smart
society" for the world, making a key difference in relation to Industry 4.0 (Gladden, 2019).
In other words, Society 5.0 vision seeks to harmonize emerging technologies with humans to create
sustainable development in the long run (Gladden, 2019). According to Gladden (2019), emerging
technologies include:
 Embodied AI,
 Social robotics,
 IoT,
 Ambient intelligence,
 Advanced human-computer interfaces and

690
 Augmented and virtual reality.
Wilson & Daugherty (2018) have recognized that collaborative intelligence founded on joined forces of
humans and AI, transforms business with a significant paradigm shift to effectively incorporate smart
machines in daily operations with human intelligence to sustain in the long run. Similarly, Bryndin (2018)
noted that Society 5.0 is designed to create equal opportunities for everyone in the ecosystem, by
maximizing each individual's value. The primary objective of emerging technologies in Industry 5.0 is to
eliminate administration, physical, and social boundaries while allowing people to deal with more quality of
work (Bryndin, 2018; Gladden, 2019).
Synergetic effects from an integrated collaboration between humans and smart systems in banking may be
analyzed from different perspectives. Wilson & Daugherty (2018) are with the opinion that organizations can
benefit from integrated collaboration between the two, based on the following principles:
 Business process rewriting,
 Experimentation/employee involvement,
 Active design of AI strategies,
 Responsible data collection,
 Work redesign and
 Employees' skills development.
Lui & Lamb (2018) argue that the synergetic effects of integrated collaboration between humans and smart
systems lie in segregates roles. When humans are in question, the future perspective creates new
opportunities that lie in inputting, training, and assisting machines to learn. Moreover, Lui & Lamb (2018) are
with the opinion that augmented collaboration is necessary to allow regulators and industry players to carry
out supportive regulation and financial stability, implying that the future of AI regulation is in the
interdisciplinary character.
Taking into account that Industry 5.0 may be revolutionary for the banking industry, implying the
transformation of banks, changes can be expected in both, hard and soft elements of any organization.
Taking into account that Society 5.0 vision is oriented to human-centricity, future management practices rely
on the adoption of new managerial and leadership knowledge and skills, with a narrow focus on
empowerment of people, emotional intelligence development, boosting creativity in day-to-day business and
development of paradigm "leading by heart". Moreover, "fusion skills" and knowledge economy come to the
fore.
On the other side, besides technical knowledge to implement all potential that Society 5.0 concept may bring,
organizational culture barriers play a significant role in a newly designed model of any organization, as well
as management and unclear AI strategies. Finally, the organizational maturity level is also one of the
challenges that should be taken into account before going forward.
Working in harmony with cognitive computing brings a lot of opportunities, but also challenges that should be
transformed into opportunities at both levels: government and regulations in one hand, and those that related
to the organization itself.
4. CONCLUSION
Innovations are widely recognized as a key value driver for economic growth, sustainability, and
development of organizations and core of competitive advantage in banking (Porter & Ketels, 2003;
Tornjanski et al., 2015). In the 4th industrial revolution, digitization defines the rules of competition, implying
the risk of being left behind to all organizations that run operations on traditional business models
(Lauterbach & Bonim, 2016). Besides, the fast-changing marketplace and "COVID-19 era" put sustainability
in the narrow focus of executives, implying continuous reconsideration of strategic direction and development
of competitive strategies (Gartner, 2017; Tornjanski et al., 2017).
Artificial Intelligence is viewed as a big game-changer in a global marketplace (Rao & Verweij, 2017).
Dwivedi et al. (2019) have recognized AI technology as an integral component of the business model and
key strategic priority in plans for organizations. However, AI brings simultaneously value to the business,
opportunities, and challenges that should be holistically viewed and properly addressed to all stakeholders
including government, academia, and industries.
Wilson & Daugherty (2018) have recognized that collaborative intelligence founded on joined forces of
humans and AI, transforms business with a significant paradigm shift to effectively incorporate smart

691
machines in daily operations with human intelligence to sustain in the long run. Future perspective of
integrated collaboration between humans and smart systems relies significantly on the Society 5.0 vision,
bringing a vast variety of opportunities and challenges for the banking industry. Future management
practices rely on the adoption of new managerial and leadership knowledge and skills, with a narrow focus
on human-centricity, implying empowerment of people and development of emotional intelligence, creativity,
and "leading by heart" paradigm. Moreover, "fusion skills" and knowledge economy come to the fore.
On the other side, technical knowledge to successfully implement Society 5.0 concept, organizational
culture, management, and unclear AI strategies may be key challenges to fully benefits from harmonized
work between humans and smart machines in Society 5.0. In addition, the organizational maturity level is
also one of the challenges that should be taken into account before going forward. A synergetic effect of
integrated collaboration between humans and smart systems brings a lot of opportunities but also challenges
that should be considered, to achieve effectiveness in sustainability in the long run.
This paper is conceptualized on the basis of secondary data collection. Future perspective should
incorporate empirical research to extend and deepen existing theoretical fund and all recognized
opportunities and challenges when integrated collaboration between humans and smart systems are in
question. However, the paper may contribute to strategic managers in the banking industry, IT sector,
FinTech companies, government, and academics who are interested in the implementation of the Society 5.0
concept.
REFERENCES
[1] Al Faruqi, U. (2019). Future Service in Industry 5.0. Jurnal Sistem Cerdas, 2(1), 67-79.
[2] Almada-Labo., F. (2017). Six benefits of Industrie 4.0 for businesses. Retrieved from:
https://www.controleng.com/articles/six-benefits-of-industrie-4-0-for-businesses/
[3] Aslam, F., Aimin, W., Li, M., & Ur Rehman, K. (2020). Innovation in the Era of IoT and Industry 5.0:
Absolute Innovation Management (AIM) Framework. Information, 11(2), 124, 1-24.
[4] Bryndin, E. (2018). System synergetic formation of society 5.0 for development of vital spaces on basis
of ecological economic and social programs. Annals of Ecology and Environmental Science, 2(4), 12-19.
[5] Chesbrough, H. (2006). Open business models: How to thrive in the new innovation landscape. Harvard
Business Press.
[6] Ciegis, R., Ramanauskiene, J., and Martinkus, B., 2009. The concept of sustainable development and its
use for sustainability scenarios. Engineering Economics, 62(2), pp. 28-37.
[7] Čudanov, M., Tornjanski, V., & Jaško, O. (2019). Change equation effectiveness: empirical evidence
from South-East Europe, 22(1), 99-114.
[8] Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of
Big Data–evolution, challenges and research agenda. International Journal of Information Management,
48, 63-71.
[9] Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Galanos, V. (2019).
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and
agenda for research, practice and policy. International Journal of Information Management, 101994.
[10] Fasnacht, D. (2009). Open Innovation in the financial services: growing through openness, flexibility and
customer integration. 1st edn. Springer.
[11] Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2014). Embracing digital technology: A new
strategic imperative. MIT sloan management review, 55(2), 1.
[12] Fukuda, K. (2020). Science, technology and innovation ecosystem transformation toward society 5.0.
International Journal of Production Economics, 220, 107460, 1-14.
[13] Fukuyama, M. (2018). Society 5.0: Aiming for a new human-centered society. Japan Spotlight, 1, 47-50.
[14] Gartner (2017). CIOs need to think about strategy, relationships and value exchange when considering
digital ecosystem. Retrieved from: https://www.gartner.com/smarterwithgartner/8-dimensions-of-
business-ecosystems/
[15] Gladden, M. E. (2019). Who will be the members of Society 5.0? Towards an anthropology of
technologically posthumanized future societies. social sciences, 8(5), 148, 1-48.
[16] Lauterbach, B. A., & Bonim, A. (2016). Artificial intelligence: A strategic business and governance
imperative. NACD Directorship, September/October, 54-57.
[17] Leonov, P., Sviridenko, A., Leonova, E., Epifanov, M., & Nikiforova, E. (2020). The use of artificial
intelligence technology in the process of creating an ATM service model. Procedia Computer Science,
169, 203-208.
[18] Löfsten, H. (2014). Product innovation processes and the trade-off between product innovation
performance and business performance. European Journal of Innovation Management 17(1), 61-84.

692
[19] Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of
Industrial Information Integration, 6, 1-10.
[20] Lui, A., & Lamb, G. W. (2018). Artificial intelligence and augmented intelligence collaboration: regaining
trust and confidence in the financial sector. Information & Communications Technology Law, 27(3), 267-
283.
[21] Marda, V. (2018). Artificial intelligence policy in India: a framework for engaging the limits of data-driven
decision-making. Philosophical Transactions of the Royal Society A: Mathematical, Physical and
Engineering Sciences, 376(2133), 20180087, 1-19.
[22] Ozkeser, B. (2018). Lean innovation approach in Industry 5.0. The Eurasia Proceedings of Science,
Technology, Engineering & Mathematics, 2, 422-428.
[23] Paschek, D., Mocan, A., & Draghici, A. (2019). Industry 5.0-The expected impact of next Industrial
Revolution. In Thriving on Future Education, Industry, Business, and Society, Proceedings of the
MakeLearn and TIIM International Conference, Piran, Slovenia (pp. 15-17).
[24] Porter, M.E. & Ketels, C.H.M. (2003). UK Competitiveness: Moving to the Next Stage. DTI Economics
Paper No 3, URN 03/899
[25] Rao, A., & Verweij, G. (2017). Sizing the prize: What’s the real value of AI for your business and how can
you capitalise. PwC Publication, PwC.
[26] Russell, S., & Norvig, P. (2002). Artificial intelligence: a modern approach. Malaysia: Pearson Education
Limited.
[27] Sharma, G. D., Yadav, A., & Chopra, R. (2020). Artificial Intelligence and Effective Governance: A
Review, Critique and Research Agenda. Sustainable Futures, 100004.
[28] Skobelev, P. O., & Borovik, S. Y. (2017). On the way from Industry 4.0 to Industry 5.0: from digital
manufacturing to digital society. Industry 4.0, 2(6), 307-311.
[29] Sundaram, R., Ziade, J., & Quinn, E. (2020). DRIVERS OF CHANGE: AN EXAMINATION OF
FACTORS THAT PROMPT MANAGERS TO ENFORCE CHANGES IN BUSINESS. International
Journal of Management, 11(05), 22-30.
[30] Tornjanski, V., & Milosavljević, G. (2016). Asian Journal of Multidisciplinary Studies. Asian Journal of
Multidisciplinary Studies, 4(2), 136, 135-147.
[31] Tornjanski, V., Čudanov, M., & Săvoiu, G. (2015). A holistic approach to innovation management in
banking: a review. Econophysics, Sociophysics & Other Multidisciplinary Sciences Journal
(ESMSJ), 5(2), 8-15.
[32] Tornjanski, V., Marinković, S. & Lalić, N. (2014). Application of ANP method based on a BOCR model for
decision-making in banking. Proceedings of the XIV International Symposium SymOrg: New business
models and sustainable competitiveness (SYMORG 2014), Zlatibor, Serbia.
[33] Tornjanski, V., Marinkovic, S., & Jancic, Z. (2017). Towards sustainability: Effective operations
strategies, quality management and operational excellence in banking. Amfiteatru Economic, 19(44), 79-
94.
[34] Tornjanski, V., Marinković, S., Levi-Jakšić, M., & Bogojević-Arsić, V. (2015a). The prioritization of open
innovation determinants in banking. Industrija, 43(3), 81-105.
[35] Tornjanski, V., Marinković, S., Săvoiu, G., & Čudanov, M. (2015b). A need for research focus shift:
Banking industry in the age of digital disruption. Econophysics, Sociophysics & Other Multidisciplinary
Sciences Journal (ESMSJ), 5(3), 11-15.
[36] Tornjanski, V., Petrović, D., & Milanović, M. (2016). The effects of IT and open innovation strategies on
innovation and financial performances in the banking sector. Bankarstvo, 45(1), 70-91.
[37] Vargo, S. L., Maglio, P. P., & Akaka, M. A. (2008). On value and value co-creation: A service systems
and service logic perspective. European management journal, 26(3), 145-152.
[38] West, J., & Gallagher, S. (2006). Challenges of open innovation: the paradox of firm investment in
open‐source software. R&D Management, 36(3), 319-331.
[39] Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: humans and AI are joining forces.
Harvard Business Review, 96(4), 114-123.
[40] Ye, J., Zhang, A., & Dong, Y. (2019). Banking reform and industry structure: Evidence from China.
Journal of Banking & Finance, 104, 70-84.