The_Impact_of_Artificial_Intel.pdf

Volume 26, 2023
Accepting Editor Francesco Tommasi│ Received: December 23, 2022│ Revised: January 28, January 31, 2023
│ Accepted: February 1, 2023.
Cite as: Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact
of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science: The Interna-
tional Journal of an Emerging Transdiscipline, 26, 39-68. https://doi.org/10.28945/5078
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THE IMPACT OF ARTIFICIAL INTELLIGENCE ON
WORKERS’ SKILLS: UPSKILLING AND RESKILLING IN
ORGANISATIONS
Sofia Morandini Alma Mater Studiorum –
University of Bologna, Bologna, Italy
sofia.morandini3@unibo.it
Federico Fraboni Alma Mater Studiorum –
federico.fraboni3@unibo.it
Marco De Angelis Alma Mater Studiorum –
marco.deangelis6@unibo.it
Gabriele Puzzo Alma Mater Studiorum –
gabriele.puzzo2@unibo.it
Davide Giusino Alma Mater Studiorum –
davide.giusino2@unibo.it
Luca Pietrantoni* Alma Mater Studiorum –
luca.pietrantoni@unibo.it
*Corresponding author
ABSTRACT
Aim/Purpose This paper aims to investigate the recent developments in research and practice
on the transformation of professional skills by artificial intelligence (AI) and to
identify solutions to the challenges that arise.
Background The implementation of AI in various organisational sectors has the potential to
automate tasks that are currently performed by humans or to reduce cognitive
workload. While this can lead to increased productivity and efficiency, these
rapid changes have significant implications for organisations and workers, as AI
can also be perceived as leading to job losses. Successfully adapting to this trans-
formation will lead companies and institutions to new working and organisa-
tional models, which requires implementing measures and strategies to upskill or
reskill workers. Organisations, therefore, face considerable challenges such as

Impact of Artificial Intelligence on Workers’ Skills
40
guiding employees towards the change process, dealing with the cost of training,
and ensuring fairness and inclusion posed by age, gender, and cultural diversity.
Methodology A narrative review has been conducted to analyse research and practice on the
impact of AI on human skills in organisations.
Contribution This work contributes to the body of knowledge by examining recent trends in
research and practice on how AI will transform professional skills and work-
places, highlighting the crucial role played by transversal skills and identifying
strategies that can support organisations and guide workers toward the upskilling
and reskilling challenges.
Findings This work found that introducing AI in organisations combines many organisa-
tional strategies simultaneously. First, it is critical to map the transversal skills
needed by workers to mitigate the current skills gap within the workplace. Sec-
ondly, organisations can help workers identify the skills required for AI adop-
tion, improve current skills, and develop new skills. In addition, the findings
show that companies need to implement processes to support workers by
providing ad hoc training and development opportunities to ensure that work-
ers’ attitudes and mental models towards AI are open and ready for the changing
labour market and its related challenges.
Recommendations
for Practitioners
Practitioners should identify the skills needed for the adoption of AI and pro-
vide training and development opportunities to ensure their workers are pre-
pared for the changing labour market.
Recommendations
for Researchers
AI is a complex and multifaceted field that encompasses a wide range of disci-
plines, including computer science, mathematics, engineering, and behavioural
and social sciences. Researchers should take a transdisciplinary approach to ena-
ble the integration of knowledge and perspectives from different fields that are
essential to understanding the full range of implications and applications of AI.
Impact on Society As AI continues to revolutionize various sectors and industries, it is important to
consider the perspectives and needs of different stakeholders, such as employ-
ees, employers, and policy makers. Our findings suggest that investing in up-
skilling and reskilling workers may help to ensure that the benefits of AI are
shared equally among all stakeholders.
Future Research Further research is needed to understand the impact of AI on human skills and
the role of soft skills in the adoption of AI in organisations. Future studies
should also consider the challenges presented by Industry 5.0, which is likely to
involve the integration of new technologies and automation on an even greater
scale.
Keywords artificial intelligence, organisational learning, transversal skills, upskilling, re-
skilling
INTRODUCTION
The use of technology, particularly Artificial Intelligence (AI), significantly impacts the global econ-
omy, business, and society. AI, defined as “a system’s ability to interpret external data correctly, to
learn from such data, and to use those learnings to achieve specific goals and tasks through flexible
adaptation” (Kaplan & Haenlein, 2019), has the potential to augment or even replace human tasks
and activities through recognition, understanding, learning, and action (Dwivedi et al., 2021). Modern
AI systems are currently bound to Machine Learning (ML). The development of machine learning
methods and models enables computers to learn from data without explicit programming (Mohri et

Morandini, Fraboni, De Angelis, Puzzo, Giusino, & Pietrantoni
41
al., 2018). Machine learning involves providing large amounts of data to a computer system, which
then uses statistical techniques to find patterns and relationships in the data. Based on the data it has
learned, the system can use this information to make predictions or take action. Experts predict that
ML and AI will significantly alter the nature of work in the coming decade (Rahman & Abedin, 2021;
Tommasi et al., 2021). The new advances in algorithmic machine learning and autonomous decision-
making will enable organisations to innovate further and optimise processes through automation.
The implementation of AI systems in industries such as finance, healthcare, manufacturing, retail,
supply chain, logistics, and public services has led to a rapid pace of change, referred to as Industry
4.0. As examples of AI systems in some of the aforementioned fields, we can mention PathAI, a tool
that helps doctors diagnose cancer accurately, or the various customer service chatbots, tools that lev-
erage natural language processing (NLP) to simulate human-like conversations and quickly provide
information to customers. To successfully adapt to these changes, organisations need to adjust to
new working and organisational models (Jaiswal et al., 2022). Among the main adaptation expected is
the need for a re-evaluation of the required workforce skills, as the automation of certain tasks may
lead to retraining or developing new skills (Hancock et al., 2020).
In this sense, the adoption of AI has implications for both knowledge workers and blue-collar work-
ers, as AI has the potential to automate a variety of tasks currently performed by humans (Leinen et
al., 2020). From this point of view, while there are arguments that this change may lead to increased
productivity and efficiency for knowledge workers, it may also result in job displacement. Before
2030, it is estimated that 375 million people (14% of the global workforce) may need to change jobs
due to AI-related technological advancements. This transition is similar to the shift of workers from
fields to factories during the industrial revolution but will occur within a considerably shorter period.
For blue-collar workers, the impact of AI may be more severe, as many tasks may be automated, po-
tentially leading to job losses in sectors that rely on manual labour. The demand for so-called mid-
range skills, such as manual, operational, and visual-spatial skills, is declining. On the contrary, there
are arguments suggesting that the AI introduction in the workplace may also lead to the creation of
new jobs, especially in sectors focused on developing and implementing AI technology (Puzzo et al.,
2020).
The impact of AI on human skills will probably depend on the specific tasks and skills being auto-
mated (Chuang, 2022). Some tasks may be more susceptible to automation than others, and the im-
pact on human skills will depend on the specific skills required for those tasks. It is also suggested
that certain skills, such as critical thinking and problem-solving, may become more valuable as AI
continues to advance. The OECD International Conference on AI in Work, Innovation, Productivity,
and Skills (Acemoglu, 2022) discussed the skills needed for the effective adoption of AI in organisa-
tions, success factors and challenges in training managers and workers, and opportunities for policy
makers to help workers acquire the necessary skills. According to the experts, the window of oppor-
tunity for reskilling and upskilling workers in the new labour market has narrowed. The skills re-
quired will change in all occupations over the next five years, resulting in a large skills gap. This is
true not only for those entering the labour market but also for those who will keep their jobs. It is
estimated that the share of key skills will change by 40% in the next five years, and 50% of all work-
ers will need retraining and further education (World Economic Forum (WEF), 2020). Key skills that
are expected to increase in importance by 2025 include technical skills critical for the effective use of
AI systems and soft skills (also called transversal skills) such as critical thinking and analysis, problem-
solving, and self-management (WEF, 2020).
To address these changes, the European Commission recently launched a round of calls on upskilling
in the industry, which led to co-funded international projects on the topic. An illustrative project is
the Up-Skill project (www.upskill-horizon.eu ) coordinated by Mälardalens Universitet. The Euro-
pean Project aims to improve the balance between humans and technology in manufacturing by fo-
cusing on the collaborative relationship between skilled workers and automation. The project will
identify skills that existing workers need to survive in the emerging digitalised workplace and create

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training courses, an Up-Skill Platform, and manuals for hardware and software up-skilling. These
projects demonstrate the need to have a better understanding of how businesses, particularly in in-
dustrial environments, can lever value from human and machine integration.
This paper aims to investigate the recent developments in research and practice on the transfor-
mation of professional skills by artificial intelligence (AI) and to identify solutions to the challenges
that arise. Prior studies (see, e.g., Jain et al., 2021; Rothwell, 2021) have suggested that creating mar-
ket-responsive training routes for skills, responsibilities, and roles requires anticipating the nature of
shifts in organisations caused by the introduction of AI systems. Therefore, we have analysed the
main theories and approaches that explain the impact of AI on human skills in organisations. We
then examined how the introduction of AI impacts the skills required by workers and how AI can
help workers improve and develop key skills. Additionally, we explored the need for organisations to
implement processes for upskilling and reskilling current and future workers, starting with the identi-
fication of skills shortages and the effective measures that can address these challenges. Finally, we
addressed the issues and challenges related to the diversity of opportunities and resources for access-
ing upskilling and reskilling, considering differences in age, gender, and culture.
The present study suggests several contributions. First, it examines recent trends in research and
practice on how AI will transform professional skills and workplaces. Second, it identifies strategies
that can support organisations and workers to face the challenges due to upskilling and reskilling pro-
clivities. Third, it provides recommendations for practitioners to identify the skills needed for the
adoption of AI, ultimately to tailor training and development opportunities to the changing labour
market. Fourth, it stresses the importance of taking a transdisciplinary approach to generate valuable
knowledge in the AI-field, as well as the role of psychology in understanding the complete applica-
tions of AI and its impact on society. Finally, it provides suggestions for future research opportuni-
ties according to the identified labour market trends.
RECENT DEVELOPMENTS IN AI AND HUMAN SKILLS
There have been numerous recent developments in the field of artificial intelligence (AI) in industry
and the workplace. Historically, AI-based systems automated a variety of back-office processes, such
as data entry, document management, customer service, and accounting, through the use of NLP
and AI to understand and mimic human interaction with computer systems (Butler, 2016; Jaiswal et
al., 2022).
A major game-changer is “generative AI”. Generative AI refers to systems that generate new content
or data, rather than just processing or analysing existing data. These systems can learn from a set of
data and then generate new content similar in style or meaning to the input data (Jovanovic & Camp-
bell, 2022). One example of generative AI is a machine learning model trained on a large dataset of
images. The model can then generate new, original images that are similar in style to the training data.
Generative AI can be used in a variety of applications, including creating realistic images and generat-
ing text, but also designing new drugs or materials.
Generative AI systems are also used to replace or mimic human transversal skills, such as communi-
cation, problem-solving, and conflict resolution. For example, an AI system with NLP capabilities
can understand customer conversations, interpret their emotions, and provide helpful and friendly
responses (Jaiswal et al., 2022). It can also learn from customer interactions to improve its responses
over time and provide personalised customer service tailored to each customer’s individual needs.
Other generative AI systems can mimic human skills such as reasoning, problem-solving, and creativ-
ity. One example of this is ChatGPT, which uses natural language generation to create human-like
conversations. These systems use techniques such as sentiment analysis, NLP, and machine learning
to understand the context of the conversation and provide appropriate responses (Jaiswal et al.,
2022). By recognising certain keywords and providing answers based on them, these systems can be
creative in their responses and challenge the user like a human could.

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AI systems that generate images from text descriptions (e.g., DALL-E 2), using a combination of
NLP and computer vision, can mimic or replace human thinking and creativity skills. These systems
can learn from their mistakes and generate increasingly accurate images. They can also go beyond the
scope of the text query to generate creative images.
Another recent development has been represented by “Edge AI”. Edge AI, also known as edge com-
puting, refers to the use of AI technologies that have their computational power at the edge of a net-
work rather than in the cloud or a centralised data centre. In industry, edge AI is often used for appli-
cations that require real-time processing or decision-making, such as autonomous vehicles, industrial
automation, and monitoring systems. Edge AI has the potential to impact human skills as it can lead
to the automation of specific tasks, potentially leading to job displacement for workers who perform
those tasks. On the other hand, it can also create job opportunities in sectors focused on developing
and implementing edge AI technologies. Additionally, edge AI can augment human skills by enabling
workers to make more informed and accurate decisions in real-time, potentially leading to increased
productivity and efficiency.
AI is also crucial to lead the way to the so-called “Industry 5.0”. Industry 5.0 refers to the current
trend of automation and data exchange in manufacturing technologies, including the Internet of
Things (IoT), AI, and cyber-physical systems. In a literature review, Al Mubarak (2022) discusses the
potential benefits and challenges of human-machine interactions in the Industry 5.0 era, focusing on
work-based learning. The author argues that technology can complement human efforts, leading to
improvements in efficiency and production, as well as opportunities for upskilling and job security.
However, to realise these benefits, it will be necessary to address legal, psychological, and ethical is-
sues at the managerial level. It is also fundamental to highlight the importance of positive externali-
ties, such as increased standards of living and sustainable development, through the optimal balance
of both human and technological capital in the context of Industry 5.0. Some ways to achieve this
are to invest in training and development programs that help workers acquire the skills needed to ef-
fectively use new technologies, as well as implementing flexible work arrangements that allow work-
ers to take advantage of AI-based efficiencies while also maintaining a healthy work-life balance.
These solutions may allow workers to effectively use new technologies to improve efficiency and
productivity while ensuring that they are treated fairly and have opportunities to grow and advance in
their careers.
Finally, Artificial General Intelligence (AGI) is a form of AI that has the ability to perform any intel-
lectual task that a human can, including tasks that require general knowledge and problem-solving
abilities. It has been suggested that the development of AGI will bring about significant changes in
organisational processes and outcomes and may surpass human intelligence. AGI could have a signif-
icant impact on human skills in organisations, either by automating tasks currently requiring human
intelligence and problem-solving abilities and the need for workers to acquire new skills or retrain for
different roles, or by augmenting human intelligence and problem-solving abilities, leading to in-
creased productivity and efficiency, as well as the opportunity for workers to focus on more complex
tasks requiring higher-level thinking. Organisations should consider the potential impact of AGI on
human skills as they adopt and integrate these technologies into their operations.
THEORIES AND APPROACHES
There are various theories and approaches that seek to explain the impact of artificial intelligence
(AI) on human skills in organisations. One of the most popular approaches foresees a positive
change brought about by the introduction of AI, in which human capabilities are enhanced and lead
to increased productivity and efficiency. According to the Technology-Mediated Learning Theory
(Bower, 2019; Gupta & Bostrom, 2009), people learn best when they have access to a range of re-
sources and tools that help them understand and process information. This theory suggests that
technology-mediated learning, such as online video tutorials or virtual simulations, can be an effective
way for people to learn new skills and knowledge. In that sense, AI can provide people with a wider

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range of information and tools, helping them to learn and perform more effectively. Thus, AI has the
potential to automate certain tasks and processes, freeing up time and resources for human workers
to focus on more complex and higher-level tasks. This can lead to the development of new skills and
the optimisation of existing skills, enabling workers to be more productive and innovative. Despite
the justified enthusiasm shown by this theory regarding the positive impact of AI on human capabili-
ties in organisations, it does not seem to address the issue of the individual employee’s access to tech-
nology, which the organisation must provide as a resource. An organisation might not always be so
resourceful to provide all the technology needed to learn new skills and knowledge; even when it is,
employees should be aware of its availability. Also, it might be expected that it is not technology per se
that makes employees learn but rather a technology that employees feel comfortable with and find
useful, relevant, satisfying, and easy to use. Thus, employees’ perceptions of the technology and its
characteristics become further factors to consider in the technology-mediated learning process. Fi-
nally, the theory misses covering which factors lead workers to focus on more complex tasks rather
than on any other type of activity when relieved from tasks taken over by AI.
On the opposite side, another approach considers AI as a replacement for certain tasks that will lead
to job displacement (Georgieff & Hyee, 2022). This theory suggests that AI has the potential to au-
tomate tasks that are currently performed by humans, leading to job losses in sectors that rely heavily
on manual labour or repetitive tasks. However, there is also a growing consensus around the idea that
adopting AI may create new jobs in different sectors, especially in those focused on the development
and implementation of AI technology. Furthermore, an aspect that this theory seems not to consider
is that jobs are not fixed pre-defined sets of tasks that do not vary across contexts of application. Ra-
ther, doing the same job in different settings (e.g., geographical, cultural, organisational) might imply
a very different set of tasks implied by how the job is perceived or represented. Thus, the negative
impact of AI could be rethought as much more variable than expected by this theory.
A more recent theory that can be seen as a combination of the different approaches is the “AI Job
Replacement Theory”, developed by Huang and Rust in 2018. The authors argue that the impact of
artificial intelligence (AI) on employment is reshaping jobs and can be both a source of innovation
and a threat. The theory identifies four types of intelligence required for tasks, particularly service
tasks, and suggests that the introduction of AI follows a predictable order. The theory asserts that
the replacement of human labour by AI occurs primarily at the task level and primarily for simple
mechanical tasks. It also suggests that the progression of AI task substitution from lower to higher
and complex tasks will result in predictable variations over time. For instance, the importance of ana-
lytical skills will decrease as AI takes on more analytical tasks, tasks that require logical and rule-based
thinking. Eventually, AI will also be able to perform intuitive and empathic tasks, which has the po-
tential to create innovative ways of integrating humans and machines in service delivery but also
poses a threat to human employment. Chui and colleagues back in 2015 found that a significant per-
centage of tasks performed by humans in high-paying jobs such as portfolio managers, doctors, and
executives could be automated by AI systems. Indeed, most jobs in business involve mechanical tasks
(such as managing daily schedules and taking attendance), thinking tasks (such as analysing customer
preferences and planning logistics) and feeling tasks (such as empathising with customers and advis-
ing patients).
Even if the scope of these tasks and the intelligence required can vary from job to job, as AI has
pushed back mechanical labour, humans will need to focus on tasks that AI is unlikely to take on,
namely those that require “thinking” and “feeling” skills (Huang & Rust, 2018; Huang et al., 2019).
Therefore, companies’ growth in the near future will hinge on the employees’ ability to upskill and
retool, leading employees not to lose jobs but rather to have different assigned tasks relying more on
“feeling skills” (Strack et al., 2021). However, some authors have recently argued that even “thinking
skills” do not protect service workers from replacement by artificial intelligence (Vorobeva et al.,
2022).

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Despite this theory’s great resonance in the scientific community, some authors have raised some crit-
icisms. For example, Dengler and Matthes (2018) argue that the likelihood of automation and the re-
sulting extent to which AI systems will replace workers is overestimated. The authors used about
8,000 tasks in German companies to investigate whether they could be replaced by computers or by
machines controlled by computers according to programmable rules. The results confirmed that
while some tasks in an occupation could be replaced, entire occupations could not. Another example
can be found in a paper by Meskó et al. (2018), which discusses the potential of AI to alleviate labour
shortages in healthcare by facilitating diagnostics, decision-making, Big Data analytics, and admin-
istration. The authors argue that AI does not cover the entire care process: empathy, proper commu-
nication and human contact will still be essential. No application, software, or device can replace per-
sonal relationships and trust. In other words, the role of the human doctor is inevitable, but AI could
be a very useful cognitive assistant. The authors argue that AI is not meant to replace healthcare pro-
viders, but that those who use AI are likely to have a competitive advantage over those who do not
know how to use it and who risk being left behind.
Some theories can explain how organisations can effectively utilise AI by redefining or reconfiguring
the skill set of their employees. The Dynamic Skills Theory (Fischer et al., 2003) posits that the value
of a person’s skills can change over time as technology and the economy evolve. While AI experts
relentlessly develop and train machine learning algorithms to mimic human skills, workers should be
able to identify the specific skills and knowledge that are necessary to effectively incorporate AI into
their work, and then deconstruct existing skills and acquire new ones to remain employable and com-
petitive. This may imply the need to constantly educate, retrain, re-learn, or learn new skills to adapt
to changing market conditions and take advantage of new opportunities. Dynamic Skills Theory
views skills development as a network of context-specific and outcome-oriented activities (Kunnen
& Bosma, 2003). At a time when organisations are being disrupted by the introduction of new and
changing AI systems, workers need to master different skills (social, emotional, technological, and
physical) to perform well and remain competitive in the labour market and avoid losing their exper-
tise. At the same time, organisations need to adapt and learn to acquire and retain the knowledge and
skills necessary to remain competitive in a changing market.
Various theoretical frameworks have been proposed to explain the phenomenon of skill decay and
skill retention following the introduction of novel technology (Arthur & Day, 2020). Skill decay is
generally described as the deterioration of knowledge and abilities due to a lack of utilisation or prac-
tice. AI systems can cause skill decay in two modalities. First, decreased usage of specific skills: if AI
systems are designed to fulfil tasks which were formerly conducted by humans, these individuals may
no longer require to apply these skills as frequently, and this can result in a decrease in their profi-
ciency in those skills over time. Second, limited chances for skill growth: if AI systems are tackling
tasks which were previously done by humans, there may be fewer chances for those workers to learn
and develop new skills, and this can lead to stagnation in their skills and knowledge.
AI systems are used in manufacturing for quality control or assembly, in finance departments to ana-
lyse financial data or produce reports, and in retail for inventory management, customer service, and
sales. With the introduction of AI, employees who previously performed these tasks may lose their
skills because they no longer need to perform them (Coombs et al., 2020). Therefore, skill decay
needs to be prevented by providing training and development opportunities, and this can include on-
the-job training, as well as more formal education and training programs.
Another example is in healthcare, where AI can perform better than humans at key healthcare tasks –
such as diagnosing diseases. Algorithms are already outperforming radiologists at spotting malignant
tumours. A recent study by Aquino and colleagues (2023) sheds light on concerns about the risk of
competency loss among healthcare professionals due to advances in AI. By analysing qualitative and
semi-structured interviews with 72 healthcare professionals with experience in AI, the authors found
two opposing views on the potential impact of AI on clinical skills, such as reasoning in diagnostic
and screening procedures. The “utopian view” was that AI could improve existing clinical skills and

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systems, while the “dystopian view” was that AI would lead to the replacement of tasks or roles with
automation.
From an organisational perspective, the Organisational Learning Theory (Chiva et al., 2014) can iden-
tify the key organisational factors that are crucial for achieving a competitive advantage in a changing
market environment. According to this theory, organisations can acquire and maintain knowledge
and skills through a process of experimentation, reflection, and adaptation. This may involve trying
out different approaches and strategies, reflecting on their experiences, and adjusting their behaviour
accordingly. This process of learning through trial and error allows organisations to continually im-
prove and evolve in response to changing circumstances and demands (Basten & Haamann, 2018).
Organisational learning theory is based on four key components. First, “collective learning” is the
process by which organisations acquire and retain knowledge and skills through the interaction and
collaboration of their members through teamwork, training, and knowledge sharing (Fenwick, 2008).
Second, “reflective practise” is the process of actively reflecting on one’s experiences and behaviours
in order to learn from them and increase adaptation to changing circumstances (Koukpaki & Adams,
2020). Third, the “organisational culture” can facilitate or hinder the learning process. Finally, organi-
sations need “structural supports” such as training programmes, knowledge management systems,
and rewards to facilitate the learning process. Understanding and effectively managing these factors
can help organisations to remain competitive and successful in a rapidly changing market disrupted
by the introduction of a variety of AI products and functionalities.
ARTIFICIAL INTELLIGENCE AND TRANSVERSAL SKILLS
The integration of AI systems into organisations has raised awareness about the importance to iden-
tify and cultivate transversal skills in their workforce. Transversal skills, also known as transferable
skills or soft skills, are those that can be applied across various tasks and industries (Hart et al., 2021).
These skills include critical thinking, problem-solving, communication, and collaboration, which are
essential for working effectively with AI systems. They enable workers to adapt to new technologies
and processes and to continuously learn and develop in the face of rapidly changing technology.
In addition to being important for working with AI systems, transversal skills can also be developed
and improved with AI. By automating certain tasks and processes, AI can free up staff time and re-
sources to focus on more complex and demanding tasks that require transversal skills. By leveraging
AI to optimise and streamline certain processes, organisations can enable their employees to develop
and improve their transversal skills, leading to higher productivity and innovation.
In the 3rd report, published in 2021 by the ESCO Member States Working Group, the authors (Hart
et al., 2021) propose a new taxonomy model for transversal skills and competences (TSCs). TSCs are
skills that are considered necessary or valuable for effective action in any kind of work, learning, or
life activity. They are thus “transversal” because they are not exclusively tied to a particular context.
In a world facing rapid technological and social change, this transversality – and the associated trans-
ferability – is seen as increasingly important (Hart et al., 2021). This expert group notes that transver-
sality can be linked to what it calls “deeper learning”, i.e., skills and competences that underpin and
enable the more specific skills needed in, for example, a work environment.
The TSC model (Figure 1) consists of six main categories, namely, core competencies, thinking com-
petencies, self-management competencies, social and communication competencies, physical and
manual competencies, and life skills. These, in turn, comprise a number of individual clusters to as-
sist in the mapping of each skill. The model facilitates the identification of relevant concepts and the
relationships between them and is useful for different purposes and users from different sectors. In
terms of organisational contexts characterised by the adoption of AI systems, three of the six cate-
gories of TSCs (i.e., thinking skills, self-management, and social and communication skills) predomi-
nantly comprise skills that are considered ‘soft’ or transferable and therefore relevant to Industry 4.0
workers.

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Figure 1. Graphical representation of the TSC model. Adapted from Hart et al., 2021
CORE SKILLS
Core skills refer to the ability to understand, speak, read, and write one or more languages, work with
numbers and measurements, and use digital devices and applications. They form the basis for interac-
tion with others and development and learning as an individual. When working with AI, it is crucial
to improve both employees’ ability to understand, speak, read, and write multiple languages and their
ability to work with numbers and units of measurement and to use devices and applications.
Language skills can help employees better understand and use AI technologies. For example, many
AI tools and platforms have user interfaces and documentation in English, so employees who are flu-
ent in English are better able to navigate and use these tools (Irawan et al., 2022). In addition, em-
ployees who are comfortable with numbers and measurements can better understand and use ma-
chine learning algorithms to predict outcomes, classify data or optimise processes (Verma et al.,
2022). Similarly, employees who are skilled in using digital devices and applications will be better able
to manage and maintain AI systems, which often require technical knowledge and familiarity with
programming languages (Allmann & Blank, 2021).
The literature provides evidence of how AI can promote the acquisition and continuous improve-
ment of employees’ core competencies. First, AI can help improve workers’ language skills by
providing automatic language learning tools (Chen et al., 2021). For example, some AI systems can
provide personalised grammar and vocabulary courses tailored to individuals’ needs by providing
real-time feedback on language use and helping employees identify areas for improvement. In addi-
tion, AI-based translation tools can help bridge the communication gap between employees with dif-
ferent language backgrounds and help them communicate effectively (Piorkowski et al., 2021). Sec-
ond, AI systems can promote the acquisition or improvement of measurement and digital skills
among employees by providing access to real-time data and insights generated by AI itself (Sousa &
Rocha, 2019). This data can help workers identify trends in their work, understand how their perfor-
mance is affected, and develop strategies to improve their performance. In addition, AI-based plat-
forms can provide personalised learning experiences that help workers acquire and refine digital skills
(Kashive et al., 2020). This includes virtual coaching, on-demand exercises, personalised content, and
automated feedback and assessment tools.

48
THINKING SKILLS
Thinking skills refer to the ability to apply the mental processes of collecting, conceptualising, analys-
ing, summarising, and/or evaluating information obtained or generated through observation, experi-
ence, reflection, reasoning, or communication. This is reflected in the use of information of various
kinds to plan activities, achieve goals, solve problems, address issues, and perform complex tasks in
routine and novel ways.
Recent studies show that thinking skills are crucial for working effectively with AI systems. Analyti-
cal, critical, and quick thinking enables employees to understand the data and insights generated by
the AI system and use this information to make informed decisions (Delanoy & Kasztelnik, 2020;
Süsse et al., 2018). AI can help organisations automate some processes, but employees still need to
use their creativity to come up with new ideas, think outside the box, and solve problems that AI sys-
tems cannot. Von Richthofen and colleagues (2022) found that introducing AI systems to automate
repetitive tasks allows employees to focus on more complex and customer-facing tasks. This leads to
employees developing problem-solving skills to effectively resolve such situations. AI systems can
also be used in some phases of a complex problem-solving process (Seeber et al., 2020).
Moreover, AI’s understanding of complex problems is time-dependent and dynamic, requires a lot of
domain knowledge, and has no specific ground truth (Dellermann et al., 2019). This implies that em-
ployees are inherently inclined to integrate transversal skills typical of humans (i.e., creativity, empa-
thy, intuitive and learning skills, and creative thinking) into the process to fill the gaps that AI systems
bring (Xiaomei et al., 2021).
SELF-MANAGEMENT SKILLS
Self-management skills refer to a person’s ability to understand and control their strengths and limita-
tions and to use this self-knowledge to direct activities in a variety of contexts. This is reflected in the
ability to act in a reflective, responsible, and structured manner in accordance with values, to accept
feedback, and to seek opportunities for personal and professional development (Hart et al., 2021).
For example, we will consider time and task management as key self-management skill for employee
performance. AI systems in organisations can potentially reduce the time it takes to complete certain
tasks by automating them or providing more efficient ways to complete them. AI systems can analyse
and process data faster than a human. In this way, a task that would normally take several hours can
be completed in a few minutes. According to Yu and colleagues (2021), effective time management
includes harnessing the power of technology and using the remaining time to complete purely hu-
man tasks. This enables the employees to use their time as productively and efficiently as possible, as
well as fostering the value creation process in organisations. Workers can focus on tasks requiring
creativity, innovation, empathy, or other qualities that are unique to humans. By enhancing self-man-
agement skills and prompting employees to focus on tasks requiring a “human touch”, organisations
can potentially create more value through the development of new ideas, the provision of personal-
ised customer service, or the creation of meaningful work experiences for employees. In this way, AI-
enhanced self-management skills can be an important part of an organisation’s strategy for creating
value.
Artificial intelligence systems can also provide personalised suggestions and advice to employees on
how to better manage their time, set goals, and prioritise tasks, helping them to manage workflows
(N. Malik et al., 2021). Some AI systems can also provide performance feedback and help employees
recognise their successes so they can develop their self-management skills (Tong et al., 2021). Recent
evidence shows that AI systems can help employees monitor their daily activities and analyse their
performance (A. Malik et al., 2022). This leads to them developing the ability to identify areas where
they need to improve and take appropriate action. From an organisational perspective, this data can
also be leveraged to greatly increase the quality of planning and scheduling in organisations. AI can
be used to automate the scheduling of tasks, events, and resources based on various factors such as

49
deadlines, dependencies, and resource availability or to assist with decision-making by providing rec-
ommendations or alternatives based on data analysis and predictive modelling.
A recent EU-project named TUPLES (Tuples, n.d.) is bringing together experts from different disci-
plines to develop AI-based hybrid planning and scheduling (P&S) tools that combine the efficiency
and adaptability of data-driven approaches with the robustness and reliability of model-based meth-
ods. These tools can have a significant impact on a variety of industries and wide a range of applica-
tions such as aeroplane manufacturing, pilots’ assistance, or even power-grid management and waste
collection. By providing efficient, reliable, and adaptable AI tools, the TUPLES project helps individ-
uals and organisations manage their time more effectively and efficiently. For example, in the case of
aeroplane pilot assistance, P&S tools could potentially help pilots optimise their flight plans and
make more informed decisions, improving the efficiency and safety of their operations. One of the
main challenging aspects in AI design and implementation (Wesche et al., 2022) of this tool is “ex-
plainability”, or the ability to understand and interpret the decisions and actions of an AI system. In
fact, if an AI system provides clear explanations for its recommendations or actions, users may be
more likely to rely on it and may be less inclined to question its decisions.
SOCIAL AND COMMUNICATION SKILLS
Social and communication skills refer to the ability to interact positively and productively with others.
This is demonstrated by communicating ideas effectively and empathetically, aligning one’s goals and
actions with those of others, seeking solutions to disagreements, building trust, and resolving con-
flicts, as well as caring for the welfare and progress of others, managing activities, and offering lead-
ership.
Effective and empathetic communication can enable employees to share information and ideas effec-
tively with colleagues and other stakeholders. AI systems are often complex and can be difficult to
understand. Effective communication can help ensure that all stakeholders are on the same page and
working towards the same goals (Kalogiannidis, 2020). First, innovative AI systems can help manag-
ers and employees improve their social and communication skills by providing feedback on their
online interactions, helping them identify potential communication gaps or problems, and giving
them tools to improve communication (Ryan et al., 2019). Some AI systems are designed to provide
employees with communication-oriented games and activities to help them practise their communica-
tion skills and change their communication strategies (Butow & Hoque, 2020).
In addition, an AI system can facilitate communication between employees and customers by provid-
ing automatic responses and intelligent support. AI can also be used to automate customer service
processes through the use of chatbots that can answer customers’ questions or provide them with
information. This often leads to employees being inspired by the performance of AI in effectively
handling communicative interactions with customers, thereby also improving their own communica-
tion performance (Prentice & Nguyen, 2020).
Working with AI also means the need to build trust. Employees need to be able to trust that the tech-
nology is reliable and that their colleagues are working towards the same goals. This helps to create a
sense of unity and collaboration within the organisation, which is essential for effective performance
and competitive advantage (Ramchurn et al., 2021).
Effective leadership can help teams manage and make the best use of technology to ensure that eve-
ryone is using it as productively and efficiently as possible. This can help improve overall team per-
formance and ensure effective organisational performance. Strong leadership skills can be critical in
overcoming challenges or obstacles that may arise when working with AI and ensuring that the team
remains focused and motivated despite setbacks (Frick et al., 2021). This can help ensure that the
team is able to overcome new challenges and continue to make progress towards the organisation’s
goals.

50
AI systems could provide managers with real-time feedback on their performance and help them
identify areas for improvement. They can also provide managers with personalised guidance and ad-
vice to help them improve their leadership skills and gain insights into team dynamics to understand
better how their employees interact with each other and how to guide them in an effective communi-
cation process (Moldenhauer & Londt, 2018). Finally, AI systems can enable managers to better un-
derstand the needs and motivations of their team members, creating an environment that fosters col-
laboration and encourages growth.
PHYSICAL AND MANUAL SKILLS
Physical and manual skills refer to the ability to perform tasks and activities that require manual dex-
terity, agility, and/or physical strength. They can be performed in difficult or dangerous environments
that require endurance or strength. These tasks and activities may be performed by hand, with other
direct physical interventions, or by using equipment, tools, or technologies that require guidance,
movement, or strength, such as ICT devices, machines, hand tools, or musical instruments.
According to recent studies, there are several reasons why it is important for employees working with
AI to improve their physical and manual skills. First, improving physical and manual skills can help
employees work more efficiently when using AI tools. For example, employees who are skilled in us-
ing hardware and software or have good hand-eye coordination can use AI tools more effectively and
efficiently (Haslgrübler et al., 2019). Secondly, employees who work with AI tools may need to han-
dle equipment or materials such as robots or machines. Improving physical and manual skills can
help employees work safely and avoid accidents or injuries at work (Niehaus et al., 2022). Third, im-
proving physical and manual skills can increase adaptability to new technologies and work environ-
ments. For example, workers who are skilled in using different types of machines or equipment can
better adapt to new AI technologies that are introduced in the workplace (Wamba-Taguimdje et al.,
2020).
There is some evidence in the literature about the impact of AI on workers’ physical and manual
skills. For example, according to Parker and Grote (2022), AI tools can automate some tasks that re-
quire physical and manual skills, allowing workers to focus on more complex and demanding tasks.
This can help workers improve their skills in more valuable areas of the business and increase their
overall productivity. In addition, AI tools can also improve the accuracy and precision of physical and
manual tasks, helping employees to work more efficiently and effectively. AI-controlled robots and
machines, for example, can be programmed to perform tasks with a high degree of accuracy and re-
peatability, reducing the risk of error and improving overall quality (Tong et al., 2021). Lastly, AI sys-
tems can be used to provide employees with targeted training and development programmes to help
them improve their physical and manual skills. For example, employees can use simulations or virtual
reality programmes to practice and improve their skills in a safe and controlled environment (X. Li et
al., 2018).
Table 1 summarises the key findings on how the demand for certain skills changes among workers in
organisations that implement AI systems and how AI can help enhance the acquisition or continuous
improvement of workers’ skills.

51
Table 1. Skills of the TSC Models:
Key skills needed in AI era and potential of AI potentials in enhancing skills
Skills of the TSC
model
Key skills needed in the AI era How AI can enhance skills
Core skills Better understanding and use of AI technologies
Better use of machine learning algorithms to pre-
dict outcomes, classify data or optimise processes
Improvement of skills in understanding program-
ming languages to effectively manage and use AI
Enhancement of language skills through AI real-
time feedback and identification of areas for im-
provement
Improvement of measurement and evaluation
skills through access to AI-provided data
Acquiring and refining digital skills through AI-
provided personalised learning experiences
Thinking skills Increasing creativity skills to come up with new
ideas and think outside the box
Development of problem-solving skills to effec-
tively solve complex situations
Development of analytical, critical, and rapid think-
ing to understand AI-generated data and make in-
formed decisions
Improvement of problem-solving of complex
tasks through automation of repetitive tasks by AI
Augmentation and integration of creativity, empa-
thy, learning, and creative thinking skills to fill AI
gaps
Self-management
skills
Development of effective time management skills
to take benefit of AI’s power and use the remain-
ing time to complete purely human tasks
Improvement of self-management skills for the de-
velopment of new ideas, while the management
and monitoring of the workflow is up to the AI
Improvement in time management, workflows
management and prioritisation through AI-pro-
vided personalised advice
Improvement in the quality of planning skills by
automating the scheduling of activities, events, and
resources by AI
Development of critical thinking skills through AI
explainability
Social and com-
munication skills
Development of effective communication skills to
share information on the use of complex AI sys-
tems
Development of effective communication skills to
build trust in AI work
Development of effective leadership skills to en-
sure that staff use AI systems productively and ef-
ficiently
Improvement of social and communication skills
through AI-powered feedback on employees’
online interactions
Improvement of employees’ communication skills
in interactions with customers thanks to AI’s auto-
matic responses and intelligent support
Development of managers’ leadership skills
through AI’s identification of the needs and moti-
vations of team members
Physical and man-
ual skills
Improvement of physical and manual skills to
work more efficiently with AI tools
Development of physical and manual skills to work
safely with AI tools and to prevent accidents or in-
juries at work
Improvement of physical and manual skills to in-
crease adaptability to new AI technologies and
work environments.
Development of physical and manual skills to per-
form complex tasks by automating simple tasks
through AI systems
Improvement of accuracy and precision in physi-
cal activities and manual skills through the capabil-
ities of AI
Enhancement of physical and manual skills in a
safe and controlled environment through AI-based
training programs
THE DUAL NATURE OF AI’S IMPACT ON WORKERS’ SKILLS
As previously discussed, the incorporation of AI into the workplace can provide opportunities for
workers to develop a wide range of skills, including transversal skills. However, it is important to rec-
ognise that there might be a “dark side” or downside of AI when considering how it can help with

52
the acquisition and development of skills. When used uncritically, AI might even undermine the de-
velopment of skills, so there are pitfalls and shortcomings to pay attention to. For instance, individual
workers may approach learning and development processes differently, with different attitudes and
motivations, and may have varying mental models towards the changes expected using AI at work.
Moreover, there might be differences in terms of starting points based on personal disadvantages or
socioeconomic challenges. For example, Smith and Smith (2021) provide first-hand empirical evi-
dence on how AI technology can, at the same time, assist and frustrate the lives of disabled people.
In general, artificial intelligence is not always exempt from bias, such as race-based or sex-based (e.g.,
Ntoutsi et al., 2020). Suppose organisations do not take these elements and differences into consider-
ation and apply AI-based skill development strategies without regard for the vulnerabilities and needs
of their workforce. In that case, these strategies may be counter-productive and exacerbate existing
inequalities within the organisation and society at large. Therefore, organisations must carefully con-
sider the impact of AI on learning and development and ensure that any strategies they implement
take into account the diverse needs and perspectives of their workforce (Zajko, 2022). Thus, it can be
argued and recommended that AI should be thoughtfully implemented and consciously managed to
provide all workers with equal opportunities for learning and developing their set of skills. Awareness
of such a dual nature of the impact of AI on workers’ skills might help managers and organisations
execute sustainable AI deployment programs in their workplaces.
Although AI systems can bring many benefits to the workplace, it is important to recognise that their
use does not always and automatically lead to a systematic improvement in employees’ skills. Accord-
ing to the literature (Alsheibani et al., 2019; Bérubé et al., 2021; Nylin et al., 2022), if not properly
managed, the use of AI in the workplace and the related organisational changes may improve or in-
crease employees’ skills while limiting the pace of work and reduce employees’ autonomy. Indeed,
increased complexity and more need for interaction and adaptability are introduced when AI is intro-
duced. In other words, as we have argued so far, AI can be useful in increasing employees’ job-rele-
vant skills. However, if not properly managed, it can create performance constraints on the employ-
ees because they need to troubleshoot the AI. This, in turn, might create a non-favourable environ-
ment for the employees to acquire skills that are relevant to their jobs, causing delays in this process.
Moreover, the impact of AI varies according to the skill level of the job. For example, Holm and Lo-
renz (2022) found that using AI to support decision-making in high-skill jobs can lead to less auton-
omy but also a faster pace of work, less monotony, more learning, and greater use of a range of
high-performance work practices. In middle-skilled jobs, the impact of using AI to make decisions is
similar, albeit to a lesser extent, while using AI to give instructions leads to a faster pace of work,
greater autonomy, and less learning. For low-skilled jobs, using AI to make decisions has no impact,
while using AI to give instructions increases the pace of work. These findings highlight the complex-
ity of the relationship between AI introduction and workers’ skills and the need to consider the spe-
cific context and skill level of the job when implementing AI systems in the workplace. It might
therefore be important to recognise not only if AI is able to promote workers’ skills development but
also the reasons why and the organisational configurations under which AI works at its best to facili-
tate the development of skills among workers, that is, to understand moderating and mediating fac-
tors that can prompt its introduction effectively.
UPSKILLING AND RESKILLING IN ORGANISATIONS
Processes to develop and retrain employees’ skills are called upskilling and reskilling. They are similar
in that both involve learning new skills, but there is a key difference between the two. The term “up-
skilling” typically refers to the process of acquiring new or improving existing skills that are directly
relevant to an employee's current job or industry. Upskilling is done with the aim of advancing one’s
career or becoming more effective in the current position (Moore et al., 2020). “Reskilling”, on the
other hand, involves learning completely new skills outside one’s current field, e.g., in a different

53
occupation or industry. Reskilling is usually done with the aim of moving to another occupation or
industry (Sawant et al., 2022).
Upskilling employees can help organisations to foster a culture of continuous learning and develop-
ment. This helps employees to stay engaged and motivated, and it can help organisations to attract
and retain top talent. In addition, a culture of continuous learning and development can help organi-
sations to adapt to changing business needs and to remain competitive in a rapidly evolving business
environment (Cukier, 2020). Not surprisingly, academics point to rising turnover rates and unemploy-
ment as AI takes over mundane tasks previously done by humans. Although a technological revolu-
tion may be imminent, its scale and timeframe are currently unknown. In an increasingly competitive
labour market, strong transversal skills can help aspiring workers stand out from other applicants and
become more attractive to potential employers (Avanzo et al., 2015). Therefore, in the coming era,
people will need to upskill appropriate capabilities for newly defined jobs and work closely with AI
technologies to do well in their employment (Jaiswal et al., 2022).
Reskilling is an important process for organisations that introduce AI systems, as it can help employ-
ees adapt to the changes the technology brings. According to Makarius et al. (2020), reskilling is cru-
cial for companies looking to adopt AI as it helps employees develop the knowledge and skills they
need to work effectively with the technology but in a new role. This can include training in areas such
as data analytics, machine learning, and programming. Reskilling can help organisations to improve
their overall competitiveness, as employees with the right skills are better equipped to drive innova-
tion and create value for the company (L. Li, 2022). Therefore, reskilling can cushion the negative im-
pact of AI on the workforce. For example, some employees may be concerned that they will lose
their jobs or that their job security will be threatened by the introduction of AI (Bhargava et al.,
2021). Reskilling can help alleviate these concerns, adjust their mental models towards the use of AI
at work, and ultimately provide employees with the skills they need to take on new roles or responsi-
bilities within the organisation. Reskilling can also help organisations retain their top talent, as em-
ployees who feel they are not offered opportunities for career growth are more likely to switch com-
panies (Tenakwah, 2021). In addition, reskilling can help close the skills gap that often exists between
older and younger workers.
As the introduction of AI into the workplace will continue transforming the nature of work and the
skills required to perform it, it is increasingly important for both workers and organisations to ad-
dress the gap between their current skillset and the skills that will be needed to navigate these
changes successfully. Identifying and understanding this skills gap is a crucial first step in developing
effective strategies for upskilling and reskilling the workforce. Once the skills gap has been identified,
organisations can then develop strategies for upskilling and reskilling their workforce to bridge this
gap and ensure that they have the necessary skills to utilize AI effectively. This ensures that all work-
ers can benefit from the advantages of AI (Kar et al., 2020).
SKILL GAP ANALYSIS
There are several ways in which organisations can assess the current skills gap within their workforce
and determine the skills that will be needed to utilize AI effectively. This may involve considering ex-
ternal factors such as industry trends and market demands, as well as the specific capabilities of AI
tools and systems being adopted, to identify the skills that will be required to leverage AI in their
work effectively. In addition, organisations may also need to conduct a skills audit or skill gap analy-
sis. A skill gap analysis is a process used to identify the skills that are needed in a specific job or in-
dustry and to compare those needs to the skills that are currently possessed by workers in that field
(Hay, 2003; Reich et al., 2002). This analysis can identify any gaps or discrepancies between the skills
required for a job and the skills that workers currently have and can help organisations and individu-
als make decisions about training and development. By identifying skill gaps, organisations can tailor
their training programs to address specific needs, and individuals can target their own learning and
development efforts to improve their job performance and advance their careers.

54
Several methods can be used to conduct a skill gap analysis. Some common methods include (1) sur-
veying workers and managers to gather information about the skills that are needed in a specific job
or industry and the skills that workers currently have, (2) analysing job postings and job descriptions
to identify the skills and qualifications that are most frequently mentioned, (3) conducting focus
groups or interviews with workers and managers to gather more detailed information about the skills
that are needed in a specific job or industry, and (4) comparing the results of the analysis to industry
standards or benchmarks to determine the extent of any skill gaps.
A study conducted by McGuinness and Ortiz (2016) aimed to identify the key factors that determine
the correct identification of skills gaps in an Irish company. The skills gaps were identified in the
company through a survey of managers and workers. By cross-referencing the data, the level of
agreement in the perception of skills gaps within the organisation was assessed, and the impact of
the gaps on firm-level performance was measured. Based on the average responses, the areas where
the most skills gaps were reported were identified (i.e., information technology and communications,
technology, and management). The analyses and results support the added value of the skills gap
identification methodology and the importance of a process of upgrading and retraining in areas that
can be improved for business performance.
In a 2019 study, Aiswarya and colleagues used interviews and focus groups on conducting a skill-gap
analysis among trainers and managers from three training institutions in the Indian state of Kerala.
Eight core competencies were identified for intervention to improve trainers’ performance: commu-
nication skills, subject knowledge, professionalism, programme planning and implementation, leader-
ship skills, resource mobilisation, ICT, and management skills. These results enabled the planning and
implementation of tailored training that allowed trainers and managers to fill gaps in identified trans-
versal skills and improve their performance.
A skill gap analysis has been performed in the Eu project FIT4FoF (www.fit4fof.eu) for six techno-
logical areas, including data analytics, cybersecurity, collaborative robotics, and human-machine inte-
gration, allowing identification of more than 100 new job profiles to face this new job market sce-
nario associated with advanced manufacturing, for instance, Robotics Technicians, whose role in-
volves installing, maintaining, and programming industrial robots and other automated systems, in-
cluding those used in manufacturing and other settings, or Human-Machine Interaction Designers,
whose role involves designing user interfaces and other interactions between humans and machines,
including the development of intuitive and user-friendly interfaces for AI systems.
MEASURES FOR UPSKILLING AND RESKILLING
Innovative methodologies should be established to implement new skills and minimise the skills gap.
Once the need for skills has been defined, a long-term plan and methodology should be established
to implement new skills and minimise the skills gap between organisations and the current work-
force. This should include tools that not only provide solutions for attracting new talent but also
well-organised training and education programs for current workers and redesign of work processes.
Several training and development solutions are available that can support and guide the upskilling and
reskilling of the workforce skillsets (Ceschi et al., 2022).
ADDIE MODEL OF TRAINING.
The skill, knowledge, and performance gaps can be bridged by designing meaningful training pro-
grams using tools like ADDIE (J. Li, 2016). One of the methods most commonly used by organisa-
tions to develop new training programmes is called Instructional Systems Design (ISD). There are
several ISD models, but most are represented by the acronym ADDIE (Analysis, Design, Develop-
ment, Implementation and Evaluation). These phases are in a logical sequence and ensure a practical
approach to designing a training programme. A study conducted by Guevarra et al. (2021) shows the
development of a training programme that aims to improve health workers’ decision-making skills in

55
evaluating data generated by technological tools based on the ADDIE model. The programme in-
volved 128 Filipino public health workers who were asked to rate the clarity and relevance of the ob-
jectives, the discussion of the topics, the methods of delivery and the time spent on addressing the
topics. By comparing participants’ ratings with follow-up data showing improvement in their deci-
sion-making capacity, the results demonstrated the value and reliability of the ADDIE model in de-
veloping a training programme to improve staff capacity.
TRAINING FOR SKILFUL HUMAN-AI INTERACTION
In some sectors, for example, healthcare, the adoption and scaling up of artificial intelligence can
help alleviate the shortage of human resources. AI has transformed some areas, such as patient care,
administrative tasks, and clinical decisions. Rizvi and Zaheer (2022) analysed how healthcare profes-
sionals can be trained to provide services supported by AI. In healthcare, AI can reduce human er-
rors due to human fatigue, support and replace labour-intensive tasks, minimise invasive surgeries,
and reduce mortality rates. There is an urgent need to train healthcare professionals, leading to skilful
interaction between medicine and machines.
HACKATHONS
A hackathon is an event where people work together quickly on a project over a relatively short pe-
riod of time. The goal of a hackathon is to develop working software or hardware by the end of the
event. Hackathons can facilitate further education by offering participants the opportunity to learn
new skills and technologies. Many hackathons focus on specific topics or technologies and provide
participants with resources, workshops, and mentors to help them learn and develop their skills in
these areas. In addition, participants often work together in teams at hackathons, allowing them to
learn from each other and share their expertise. This kind of collaborative learning can help partici-
pants develop a broader range of skills and knowledge. Hackathons can facilitate both upskilling and
reskilling by providing participants with the opportunity to learn new skills and knowledge and apply
them in a real-world setting (Medina Angarita, & Nolte, 2020). For individuals looking to improve
their performance in their current job, hackathons can be an opportunity to learn new technologies.
For individuals seeking a new career, hackathons can learn about different industries or fields and de-
velop the skills and knowledge needed to succeed in those fields.
LIFELONG LEARNING AND “HANDS-ON” LEARNING MODULES
The wide adoption of the I4.0 systems and related technologies is dependent on the efficient imple-
mentation of lifelong learning and training initiatives that address these challenges. Oliveira et al.
(2022) propose hands-on learning modules for upskilling in industry 4.0 technologies. The authors
describe the implementation of a series of short learning modules that relies on solid hands-on prac-
tical experimentation regarding upskilling in emergent ICT technologies. The feedback from the par-
ticipants shows that these short hands-on learning modules strongly contribute to qualifying the
workforce and undergraduate students in emergent ICT.
QUALITY CIRCLES
Since employee training and development is of great interest to organisations, the design of a Future
of Work programme should focus on the company’s offer to its employees (Ellingrud et al., 2020).
Organisations need to develop clear and compelling value propositions so that employees see the
benefits of acquiring new skills and learning to use AI systems. Because Japanese companies have
traditionally emphasised lifelong employment, they have created a valuable culture of training pro-
grammes for their employees. One of the most well-known programs is the Quality Circle, which can
be successfully used for employee upskilling and reskilling to those who work with AI. This pro-
gramme aims to involve employees in decision-making and lead the company towards a more partici-
patory culture. The Quality Circle trains employees to think critically and solve problems as they

56
perform their tasks at work. The circles usually meet four hours a month during working hours. A
team leader, who is usually a trained member of the management team, helps train the circle mem-
bers and ensures that everything runs smoothly. Members receive recognition when their suggestions
for improving production are accepted (Lawler et al., 1984).
CERTIFICATION AND RE-CERTIFICATION
Many professional associations offer certification by examination. As continuing education and skills
development must be ongoing, regular recertification is critical to ensure that staff keep up with new
AI technologies and innovative organisational practices. For example, nurses care for patients in the
midst of the information age. This means that the knowledge they need to do their jobs will increase
exponentially over time. Therefore, certified nurses need periodically to recertify by taking the appro-
priate examination or meeting the clinical practice and continuing education requirements established
for recertification.
AI FOR TRAINING AND DEVELOPMENT
AI can also be used to enhance employee training and development through personalised learning
experiences and real-time feedback. Some AI systems are being used to provide virtual coaching to
employees, including real-time feedback on performance and personalised development plans. AI
systems can also provide employees with access to on-demand exercises and challenges that can help
them learn and develop new skills. AI-powered tools provide automated feedback and assessments to
help employees understand their strengths and areas for improvement and guide their learning and
development efforts. By using AI to provide personalised learning experiences and real-time feed-
back, organisations can help their employees learn and develop new skills more effectively and effi-
ciently.
COSTS AND BENEFITS OF UPSKILLING AND RESKILLING
The ability of AI systems to perform certain tasks more efficiently or accurately than humans can
lead to skill mismatches among workers. Skill mismatch means that some workers’ skills are not fully
utilised or are utilised in a way that does not match their strengths (Brunello & Wruuck, 2019). To
avoid or mitigate this problem, upskilling and reskilling processes seem to be crucial for training and
supporting employees (Giabelli et al., 2021). However, like any major change, implementing these
processes comes with both costs and benefits for the organisation and its employees. According to a
report by the European Centre for the Development of Vocational Training (Cedefop, 2020), there
are 128 million adults in the EU-28 Member States, Iceland and Norway (hereafter referred to as EU-
28+) with the potential for upskilling and reskilling (46.1% of the adult population). The return on
investment in upskilling and reskilling can be significant, with estimates ranging from 10% to 30%
depending on the sector and the specific interventions implemented (Cedefop, 2020). This suggests
that, despite the costs involved, investing in the upskilling and reskilling of workers can bring about
significant benefits for both workers and organisations in the EU.
Recent studies have highlighted the main benefits of upskilling and reskilling for individuals and or-
ganisations facing skill mismatch. First, increasing productivity: by providing their employees with the
skills they need to do their jobs effectively, companies can increase the efficiency and productivity of
their workforce (Zapata-Cantú, 2022). Second, increasing competitiveness: upskilling and reskilling
can help organisations remain competitive by ensuring that they have a skilled and adaptable work-
force that can meet the changing needs of the business (Ponce Del Castillo, 2018). Thirdly, improv-
ing employee satisfaction: by allowing their employees to learn and develop, organisations increase
their job satisfaction and engagement, which can lead to better retention and lower turnover (Lee et
al., 2022).

57
However, implementing upskilling and reskilling programmes can require a significant investment in
terms of time and resources. According to the authors (see, e.g., Abe et al., 2021), these processes are
primarily associated with financial costs: upskilling and reskilling can be costly for the organisation,
especially if it has to hire external trainers, pay for training materials or pay employees to attend
courses or workshops. In addition, upskilling and reskilling involve time costs: employees may have
to be absent from work to attend training, causing interruptions to business. To attend training, em-
ployees must be absent from their usual duties. This can lead to reduced productivity and possible
delays in completing tasks (Hiremath et al., 2021). Finally, organisations need to know how to invest
resources to overcome resistance to change. Some employees may resist upskilling and reskilling
measures because they are sceptical about the value of training or because they are reluctant to learn
new skills. This may lead to their resistance to change and possibly lower participation (Aguiar et al.,
2022).
Overall, the benefits of training can outweigh the costs, especially if programmes are well-designed
and effectively implemented. When companies invest in workforce development, they can create a
more adaptable and skilled workforce that is better equipped to meet the challenges and opportuni-
ties of the future.
Figure 2 represents the main costs and benefits for organisations planning to implement upskilling
and reskilling interventions.
Figure 2. Costs and benefits of upskilling and reskilling for organisations
CHALLENGES FOR UPSKILLING AND RESKILLING
Are skills a panacea? Not always. Upskilling and reskilling can be difficult or counterproductive for a
variety of reasons. It can be challenging for workers to find the time and resources to learn new
skills, particularly if they are working full-time and have other responsibilities. Some workers may be
resistant to change or may not see the value in learning new skills, which can make upskilling and re-
skilling efforts difficult. If there are limited opportunities for workers to advance their careers or use
their new skills, they may be less motivated to invest in upskilling and reskilling. If the skills that
workers are learning do not align with the needs of the organisation, upskilling and reskilling efforts
may be counterproductive. Without adequate support from the organisation, including training, re-
sources, and support for learning, upskilling and reskilling efforts may be difficult or unsuccessful.
Therefore, in order to be effective, upskilling and reskilling efforts must be well-planned and well-
supported by the organisation and the individuals involved.

58
At a broader level, countries with poor education and structures are reluctant to invest in upskilling
and reskilling. To address the challenges and opportunities of AI, skills have been identified as key in
their national strategies. Yet, little attention is paid to the underlying social relations of production in
which the practices of skills development occur, which is crucial for understanding the outcome of
skills policies and practices (Hammer & Karmakar, 2021; Petersen et al., 2022). Skilling and reskilling,
which are so central to harnessing and managing the impact of new technologies, might be difficult
in some countries with poor education and skill structures, exclusion of vulnerable segments of the
workforce in the informal economy, and firms’ reluctance to invest in training and reliance of infor-
mal skilling (Hammer & Karmakar, 2021; Ramaswamy, 2018). In developing countries, there might
be difficulty in investing in upskilling and reskilling. Access to education and skilling is difficult and
does not translate into employment opportunities. In this context, except for a few highly skilled
workers in the automotive and IT sectors, the inability of most workers to access skills development
initiatives and the lack of recognition of informally gained skills are likely to be persistent challenges
to the up/reskilling that is so essential for the adoption of high technologies. Organisations need to
find strategies to reverse the trends.
Hammer and Karmakar (2021) point out that the adoption of new technologies can be uneven and
inconsistent. It may improve employment conditions for some individual workers but will not change
employment conditions for the majority. Public policies are needed to ensure that emerging technolo-
gies are used responsibly to complement rather than replace labour, which would have negative distri-
butional effects. Public sector investment in skills initiatives is much lower in some developed coun-
tries like India than in developed countries like Germany. India has tried to change this through pub-
lic-private partnerships in Industrial Training Institutes and industry-led vocational training pro-
grammes. In Europe, governments are working with technology companies to solve employment
problems and fill the skills gaps as the skills in demand in different occupations will change in the
near future. Policies aim to foster a national skills ecosystem for emerging digital technologies.
In addition, the impact of technology on the labour market has the potential to disproportionately
affect different groups of workers, including men and women belonging to different age groups. A
gender-sensitive approach to upskilling and reskilling workers is necessary to ensure that both men
and women have the opportunity to benefit from technological advances and to prevent further gen-
der inequalities in the labour market. In this context, it is important to take into account the different
needs and challenges of men and women, as well as the potential impact of these programmes on
gender equality in the workplace, when designing training and retraining programmes.
The gender-related digital divide in access to technology and the internet can negatively affect re-
skilling. There is a significant gender-related digital divide in access to technology and the internet,
which has a negative impact on skills development initiatives. Women’s access to digital technologies
is likely to increase as the affordability and penetration of internet services and devices increases. In
some countries where gender inequality is particularly pronounced, low levels of literacy, education,
and skills, reinforced by social norms, are likely to prevent women and other socially disadvantaged
groups from leveraging new technologies. The number of jobs requiring extensive knowledge in sci-
ence, technology, engineering, and mathematics has increased over the last decade, and advances in
AI will require even more expertise in STEM. According to Billionniere and Rahman (2022), it is im-
portant to build capacity and widen participation in computing through training and retraining
women with the emerging technology gateway. A gender-sensitive approach to upskilling and re-
skilling requires a comprehensive understanding of how existing gender inequalities in the labour
market are exacerbated or mitigated by technological change. That means ensuring that both men
and women have the opportunity to benefit from technological progress and contribute to the future
of work.
The age-related digital divide can also have significant implications for the way individuals are able to
perform their jobs and on both upskilling and reskilling. Older individuals who lack access to and
proficiency in using new technologies may be at a disadvantage when it comes to finding and

59
maintaining employment, as many jobs now require digital skills and the ability to use technology ef-
fectively. This can lead to age discrimination in the workplace and contribute to age-related inequali-
ties in employment and income (Truxillo et al., 2015). In addition, the age-related digital divide can
impact organisational performance and productivity. Organisations that do not invest in training and
supporting their older employees to use new technologies may miss out on the valuable knowledge,
experience, and perspective that these employees bring to the workplace. This can result in a missed
opportunity for organisations to benefit from the diversity of their workforce and may lead to a less
inclusive and innovative work environment. Therefore, organisations need to consider the age-related
digital divide and take steps to support the adoption and use of new technologies by all employees,
regardless of age. This can include providing training and resources, offering flexible work arrange-
ments, and promoting a culture of inclusivity and continuous learning. In a systematic review, Longo-
ria and colleagues (2022) highlight the importance of including design considerations for inclusive
and accessible ICTs in design or engineering programs. Addressing the ICTs design in a diverse ap-
proach might foster students’ innovation capabilities and sensibility towards vulnerable populations
during the design process. There are plenty of possibilities to bridge the age-related digital gap using
technology as a responsive, empathetic, and learning tool to address the age-related digital divide, ul-
timately enhancing workers’ skills to maximise organisational performance and productivity.
CONCLUSIONS
AI is a complex and multifaceted field that encompasses a wide range of disciplines, including com-
puter science, mathematics, engineering, and behavioural and social sciences. A transdisciplinary ap-
proach allows for the integration of knowledge and perspectives from different fields, which is essen-
tial for understanding the full range of implications and applications of AI. With its interdisciplinary
approach, this paper joins the ongoing discourse on the extent to which the implementation of AI
systems in organizations has and will continue to have an impact on the nature of work in the com-
ing decade. A thorough and critical examination of the literature has revealed that AI has the capacity
to augment and to disrupt existing work practices and processes. From this perspective, the findings
highlight the importance of considering both individual and organisational factors when introducing
AI into organisations. In particular, the focus should be on upskilling and reskilling employees, as AI
is increasingly able to take over tasks previously performed by human workers, as predicted by AI Job
Replacement Theory and demonstrated by recent developments in AI (e.g., chatGPT).
The importance of investing in human capital is proving to be a crucial aspect of successfully inte-
grating AI into companies and maximising its potential benefits for organisations and employees.
The adaptation process involves and combines several organisational strategies. Firstly, capturing the
soft skills needed by workers is critical to addressing the current skills gap in the workplace. Organi-
sations can then help workers identify the skills needed for AI adoption and improve and develop
new skills. Then, organisations need to put processes in place to support workers by providing train-
ing and development opportunities to ensure that workers’ attitudes and mental models towards AI
are open and prepared for the evolving labour market and its challenges.
As with all major changes, the transition to new organisational models comes with both costs and
benefits and requires careful consideration of individual factors such as the gender gap, age differ-
ences, and cultural diversity. The benefits outweigh the costs if programmes are designed with these
factors in mind and implemented effectively. Therefore, one of the most pressing challenges for or-
ganisations is to guide employees through the transition to Industry 5.0 by considering the cost of
training and ensuring equality and inclusion for all, regardless of age, gender, and cultural diversity.
Given the evidence from the literature, we believe that a transdisciplinary approach to enhancing AI
skills and retraining workers can provide a more comprehensive and nuanced understanding of the
potential impact of AI on the future of work and society and help ensure that the benefits of AI are
shared equitably across all stakeholders. Hence, practitioners and stakeholders need to invest in

60
upskilling and reskilling workers to create a more adaptable and skilled workforce that can meet the
challenges and opportunities of the future.
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AUTHORS
Dr. Sofia Morandini is a dedicated researcher in the field of work, or-
ganizational and personnel psychology. Her passion for the field stems
from her interest in studying the impact of human behaviour on organi-
sational outcomes. She currently works as a research associate in the Hu-
man Factors Risk and Safety Research Unit at the University of Bologna
(Italy). She has also been collaborating with the APRESO research centre
(University of Verona, Italy) since 2020. Her research focuses on human-
AI interaction, organisational innovation, safety in high-risk organisations
and human resource training and development.

67
Dr. Federico Fraboni is a researcher with a background in Work, Organ-
izational, and Personnel Psychology, and holds a Ph.D. in Psychology
from the University of Bologna. He has expertise in human-technology
interaction, human-robot collaboration, and safety in high-risk organiza-
tions. His current research focuses on cognitive workload, technology ac-
ceptance, human-machine interfaces, and risky behaviours in the work-
place. He has participated in various European-funded research projects
and has collaborated with private companies on internally funded pro-
jects. He is currently a researcher in the Human Factors Risk and Safety
research unit of the University of Bologna.
Prof. Marco De Angelis is an Assistant Professor at the University of
Bologna. He is a member of the Human Factors, Risk, and Safety re-
search unit at the Department of Psychology. He holds a degree in Work
and Organisational Psychology and is actively involved in academic lec-
tures and consultant activities. His areas of expertise include error and
risk management, nudging behavioural safety in high-risk organisations,
digital interventions for occupational health, and integrating new technol-
ogies into organisational processes.
Dr. Gabriele Puzzo is a PhD Candidate enrolled at the University of
Bologna (37th Cycle). His research interests have led him over the years to
conduct study and work activities in Italy and abroad. Enrolled in the
Register of Psychologists in 2022, his research focuses on investigating
and implementing innovative techniques to foster pro-environmental be-
haviors in organizational and community contexts. His interests also in-
clude human factors, human-robot collaboration, human-machine inter-
face design and organizational development.
Dr. Davide Giusino is a PhD Candidate at the Human Factors, Risk and
Safety (HFRS) Research Unit, Department of Psychology, Alma Mater
Studiorum – University of Bologna. His research domains cover Work
and Organizational Psychology as well as Occupational Health Psychol-
ogy. His scientific interests relate to digital-based interventions for teams
in the workplace as well as mental health and psychosocial well-being in
working environments.

68
Prof. Luca Pietrantoni is a Full Professor of Work and Organisational
Psychology at the University of Bologna. He is the coordinator of the
EU-funded project H-WORK on health and safety at work. He is cur-
rently leading a research team at the University of Bologna on Human
Factors in three EU projects on AI at work (Edge AI, TUPLES) and Hu-
man-Robot Collaboration (SESTOSENSO). His main research interests
include adoption of technology in organizations, human-machine inter-
face and health and safety at work. He has published in international aca-
demic journals, including Accident Analysis, Prevention and Risk Analy-
sis, and Frontiers in Psychology.

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