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Volume 26, 2023
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Accepting Editor Francesco Tommasi│ Received: December 23, 2022│ Revised: January 28, January 31, 2023
│ Accepted: Febr uary 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 –
University of Bologna, Bologna, Italy
federico.fraboni3@unibo.it
Marco De Angelis
Alma Mater Studiorum –
University of Bologna, Bologna, Italy
marco.deangelis6@unibo.it
Gabriele Puzzo
Alma Mater Studiorum –
University of Bologna, Bologna, Italy
gabriele.puzzo2@unibo.it
Davide Giusino
Alma Mater Studiorum –
University of Bologna, Bologna, Italy
davide.giusino2@unibo.it
Luca Pietrantoni*
Alma Mater Studiorum –
University of Bologna, Bologna, Italy
luca.pietrantoni@unibo.it
*Corresponding author
ABSTRACT
Aim/Purpose
This paper examines the transformative impact of Artificial Intelligence (AI) on
professional skills in organizations and explores strategies to address the result-
ing challenges.
Background
The rapid integration of AI across various sectors is automating tasks and re-
ducing cognitive workload, leading to increased productivity but also raising
concerns about job displacement. Successfully adapting to this transformation
requires organizations to implement new working models and develop strategies
for upskilling and reskilling their workforce.
Impact of Artificial Intelligence on Workers’ Skills
40
Methodology
This review analyzes recent research and practice on AI's impact on human skills
in organizations. We identify key trends in how AI is reshaping professional
competencies and highlight the crucial role of transversal skills in this evolving
landscape. The paper also discusses effective strategies to support organizations
and guide workers through upskilling and reskilling processes.
Contribution
The paper contributes to the existing body of knowledge by examining recent
trends in AI's impact on professional skills and workplaces. It emphasizes the
importance of transversal skills and identifies strategies to support organizations
and workers in meeting upskilling and reskilling challenges. Our findings suggest
that investing in workforce development is crucial for ensuring that the benefits
of AI are equitably distributed among all stakeholders.
Findings
Our findings indicate that organizations must employ a proactive approach to
navigate the AI-driven transformation of the workplace. This approach involves
mapping the transversal skills needed to address current skill gaps, helping work-
ers identify and develop skills required for effective AI adoption, and imple-
menting processes to support workers through targeted training and develop-
ment opportunities. These strategies are essential for ensuring that workers' atti-
tudes and mental models towards AI are adaptable and prepared for the chang-
ing labor market.
Recommendations
for Practitioners
For practitioners, we recommend identifying the specific skills required for AI
adoption and implementing comprehensive training and development programs.
This approach will ensure workers are well-prepared for the evolving demands
of an AI-integrated labor market.
Recommendations
for Researchers
We emphasize the need for researchers to adopt a transdisciplinary approach
when studying AI's impact on the workplace. Given AI's complexity and its far-
reaching implications across various fields including computer science, mathe-
matics, engineering, and behavioral and social sciences, integrating diverse per-
spectives is crucial for a holistic understanding of AI's applications and conse-
quences.
Impact on Society
The societal impact of AI's continued revolution across sectors underscores the
importance of considering diverse stakeholder perspectives, including those of
employees, employers, and policymakers. Our research suggests that investments
in upskilling and reskilling initiatives can promote a more equitable distribution
of AI's benefits among all stakeholders.
Future Research
Looking ahead, further research is needed to deepen our understanding of AI's
impact on human skills, particularly the role of soft skills in AI adoption within
organizations. Future studies should also address the challenges posed by Indus-
try 5.0, which is expected to bring about even more extensive integration of new
technologies and automation.
Keywords
artificial intelligence, organisational learning, transversal skills, upskilling, re-
skilling
Morandini, Fraboni, De Angelis, Puzzo, Giusino, & Pietrantoni
41
INTRODUCTION
The use of Artificial Intelligence (AI) is significantly impacting 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 al., 2018). Machine learning involves provid-
ing large amounts of data to a computer system, which then uses statistical techniques to find pat-
terns and relationships in the data. Based on the data it has learned, the system can use this infor-
mation 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 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. As examples of AI sys-
tems in some of the fields, some tools are helping doctors to diagnose cancer accurately, or various
customer service chatbot leverage natural language processing (NLP) to simulate human-like conver-
sations and 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 ad-
aptation 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 erosion. Before 2030, it is
estimated that 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 in-
dustrial 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, potentially leading to job losses
in sectors that rely on manual labour. The demand for so-called midrange skills, such as manual, op-
erational, 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, 2020). Key skills that are ex-
pected 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 (Worl d Economic Forum, 2020).
Impact of Artificial Intelligence on Workers’ Skills
42
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 identi-
fied skills that existing workers need to survive in the emerging digitalised workplace and create train-
ing 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 industrial en-
vironments, 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 and to discuss some of these challenges. Prior
studies (see, e.g., Jain et al., 2021; Rothwell, 2021) have suggested that creating market-responsive
training routes for skills, responsibilities, and roles requires anticipating the nature of shifts in organi-
sations 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. Additionally, we explored the need for
organisations to implement processes for upskilling and reskilling current and future workers, starting
with the identification of skills shortages and the effective measures that can address these chal-
lenges. Finally, we addressed the issues and challenges related to the diversity of opportunities and
resources for accessing upskilling and reskilling, considering differences in age, gender, and culture.
RECENT DEVELOPMENTS IN AI AND HUMAN SKILLS
There have been 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. AI was
used to understand and mimic human interaction with computer systems (Butler, 2016; Jaiswal et al.,
2022).
A major game-changer has been “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 &
Campbell, 2022). One example of generative AI is a machine learning model trained on a large da-
taset 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 im-
ages and generating 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 problem-solving and creativity. One ex-
ample of this is ChatGPT, which uses natural language generation to create human-like conversa-
tions. These systems use techniques such as sentiment analysis, NLP, and machine learning to under-
stand the context of the conversation and provide appropriate responses (Jaiswal et al., 2022). AI sys-
tems that generate images from text descriptions (e.g., DALL-E, Midjourney), using a combination
of NLP and computer vision, can mimic or replace human thinking and creativity skills. These sys-
tems can learn from their mistakes and generate increasingly accurate images. They can also go be-
yond the scope of the text query to generate creative images.
Morandini, Fraboni, De Angelis, Puzzo, Giusino, & Pietrantoni
43
Another recent development relevant in the industrial setting has been represented by “Edge AI”.
Edge AI, also known as edge computing, refers to the use of AI technologies that have their compu-
tational power at the edge of a network rather than in the cloud or a centralised data centre. In indus-
try, edge AI is often used for applications 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 dis-
placement for workers who perform those tasks. On the other hand, it can also create job opportuni-
ties 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.
Human-AI teaming is an important component of "Industry 5.0". Industry 5.0, as envisioned by the
European Commission, is a human-centric, sustainable, and resilient approach to manufacturing that
goes beyond Industry 4.0. It prioritizes collaboration between humans and machines, sustainability,
and aims to create resilient industrial ecosystems that can adapt to changing demands and disrup-
tions. According to this vision, the creation of new technologies, products, and services should prior-
itize the well-being of workers and society.
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, at the managerial level it will be neces-
sary to address legal, psychological, and ethical issues and increase 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 effectively use new technologies and implement flexible work ar-
rangements that allow workers 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 they are treated fairly and have opportunities to
grow and advance in their careers.
THEORIES AND APPROACHES
There are various theories and approaches that may be adopted to explain the impact of artificial in-
telligence on human skills in organisations.
Technology-Mediated Learning (TML) Theory focuses on the integration of technology in learning
processes, examining how technology can enhance or alter the learning experience (Bower, 2019;
Gupta & Bostrom, 2009). 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. Technological “affordances” and social contexts significantly influence learning out-
comes. In that sense, AI technology can provide people with a wider 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. The theory does not seem to address the issue of the individual employee’s access
to technology, 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.
Impact of Artificial Intelligence on Workers’ Skills
44
Finally, 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.
The AI Job Replacement Theory developed by Ming-Hui Huang and Roland Rust (2018) addresses
the significant impact of artificial intelligence (AI) on job markets, specifically focusing on how AI
can replace human tasks in service industries. The theory identifies four types of intelligence required
for tasks, particularly service tasks, and suggests that the introduction of AI follows a predictable or-
der. 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 substitu-
tion from lower to higher and complex tasks will result in predictable variations over time. For in-
stance, the importance of analytical 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 em-
pathic tasks, which has the potential 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 percentage of tasks performed by humans in high-paying jobs such as port-
folio managers, doctors, and executives could be automated by AI systems. Indeed, most jobs in busi-
ness 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 empa-
thising with customers and advising patients).
This theory suggests that AI has the potential to automate tasks that are currently performed by hu-
mans, leading to job losses in sectors that rely heavily on manual labour or repetitive tasks (Georgieff
& Hyee, 2022). However, 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. Rather, 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.
Despite this theory’s great resonance, some authors have raised some criticisms. For example,
Dengler and Matthes (2018) argue that the likelihood of automation and the resulting extent to
which AI systems will replace workers is overestimated. The authors used about 8,000 tasks in Ger-
man companies to investigate whether they could be replaced by computers or by machines con-
trolled 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 administration. The
authors argue that AI does not cover the entire care process: empathy, proper communication and
human contact will still be essential. No application, software, or device can replace personal relation-
ships 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 providers, 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.
AI Job Replacement Theory and its criticisms underscore the complex interplay between AI and hu-
man skills across various occupations. Public discourse often focuses on jobs perceived as less vul-
nerable to AI impact, including masseurs, hairdressers, plumbers, electricians, psychologists, pet
groomers, cooks, athletes, craftsmen, beekeepers, art restorers, babysitters, nurses, doctors, and club
entertainers. These occupations can be categorized based on their required skills. Many demand di-
rect human interaction, emphasizing empathy, interpersonal communication, and adaptability to indi-
vidual needs. Others rely on manual and tactile skills, requiring physical dexterity and sensitivity that
are difficult to replicate mechanically. Some necessitate specialized technical knowledge and problem-
solving abilities in diverse contexts. Creative and performance-based roles center on artistic expres-
sion, entertainment, or physical prowess. Lastly, certain occupations focus on the care and manage-
ment of living organisms. This classification suggests that jobs seen as less susceptible to AI
Morandini, Fraboni, De Angelis, Puzzo, Giusino, & Pietrantoni
45
automation typically require a blend of complex cognitive skills, refined manual abilities, situational
adaptability, and emotional intelligence—elements that current AI technologies struggle to replicate
effectively. While some tasks may be automated, distinctly human characteristics like forming per-
sonal relationships, building trust, and expressing genuine empathy remain challenging to replace
with AI in the short term. However, as Gmyrek et al. (2023) noted, AI's impact on these occupations
may be more nuanced than initially thought. The perception of AI-resistant jobs could evolve as AI
capabilities advance, highlighting the need for ongoing assessment of AI's potential influence across
various professions.
How human skills develop in this evolving scenario? The Dynamic Skills Theory, developed by Kurt
Fischer and colleagues (Fischer et al., 2003), provides a framework for understanding the develop-
ment of human skills in a dynamic and context-dependent manner. The theory posits that the value
of a person’s skills can change over time as technology and the economy evolve. Skills are highly
context-dependent, meaning that a person's ability to perform a skill can vary significantly based on
the specific environment and circumstances. The theory integrates ideas from constructivism, em-
phasizing that skills are constructed through interactions with the environment and are not static en-
tities. In this context, workers need 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 competitive. 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 opportu-
nities (Kunnen & Bosma, 2003).
Skill decay, defined as the deterioration of knowledge and skills due to lack of use or practice
(Klostermann et al., 2022), is an emerging phenomenon following the introduction of new technolo-
gies into organizations. According to Arthur and Day (2020), the prolonged use of AI systems by
workers has the potential to cause their skill deterioration, and this can occur in two primary modali-
ties. First, specific skills may decline: if AI systems are designed to perform tasks previously executed
by humans, the frequency of applying these skills decreases, leading to a decline in competence over
time. For example, AI systems are used in manufacturing for quality control or assembly, in finance
departments to analyse 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). Sec-
ond, there is a limitation of opportunities for skill growth: as AI systems take over tasks previously
performed by humans, workers may have fewer chances to learn and develop new skills, resulting in
stagnation in their skill sets and knowledge. For instance, in customer service, AI chatbots can handle
a wide range of inquiries, reducing the need for human agents to engage in problem-solving or com-
plex customer interactions. This reduction in diverse customer interactions can limit the development
of critical thinking and advanced communication skills among customer service representatives
(Prentice & Nguyen, 2020)
In healthcare, AI 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-struc-
tured interviews with 72 healthcare professionals with experience in AI, the authors found two op-
posing views on the potential impact of AI on clinical skills, such as reasoning in diagnostic and
screening procedures. The “utopian view” posits that AI can improve existing clinical skills and sys-
tems, increasing the accuracy and efficiency of medical procedures and enabling healthcare profes-
sionals to focus on more complex, patient-centred aspects. In contrast, the “dystopian view” argues
that AI would lead to the replacement of tasks or roles by automation, thus eroding essential clinical
skills over time. In this sense, AI takes over diagnostic and procedural tasks, healthcare workers could
become overly dependent on technology, resulting in a decline in their ability to perform these tasks
independently (Aquino et al., 2023). These contrasting perspectives highlight the dual potential of
AI: while it has the capacity to increase human competence and improve services delivery, it also
Impact of Artificial Intelligence on Workers’ Skills
46
presents risks to the maintenance and development of essential skills. The challenge is to integrate AI
in a way that supports and enhances human competencies, rather than reducing them.
The Organisational Learning Theory (Chiva et al., 2014) can represent a comprehensive theoretical
framework to identify the key organisational factors that are crucial for achieving a competitive ad-
vantage in a changing market environment based on AI integration. According to this theory, organi-
sations can acquire and maintain knowledge and skills through a process of experimentation, reflec-
tion, 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 improve and evolve in response to changing circum-
stances and demands (Basten & Haamann, 2018).
Organisational learning theory is based on four key components (Fenwick, 2008; Koukpaki & Ad-
ams, 2020). 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 Second, “reflective practise” is the process of actively reflecting on
one’s experiences and behaviours to learn from them and increase adaptation to changing circum-
stances. Third, the “organisational culture” can facilitate or hinder the learning process. Finally, or-
ganisations need “structural supports” such as training programmes, knowledge management sys-
tems, 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 dis-
rupted 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.
Figure 1. Graphical representation of the Transversal Skills and Competences model.
Adapted from Hart et al. (2021)
Morandini, Fraboni, De Angelis, Puzzo, Giusino, & Pietrantoni
47
Hart and colleagues (2021) proposed a taxonomy model for Transversal Skills and Competences
(TSCs) to be used at a European level. TSCs are defined as skills 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. This transversality – and the associated transferability – is seen
as increasingly important (Hart et al., 2021). Transversality 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 five main categories: 1) core
skills and competences, 2) thinking skills and competencies, 3) physical and manual skills and compe-
tencies, 4) self-management skills and competencies, 5) social and communication skills and compe-
tencies. The model facilitates the identification of relevant concepts and the relationships between
them and is useful for different purposes and users from different sectors.
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.
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 is changing the acquisition and continuous improvement
of employees’ “core skills and 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.
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 specific types of thinking skills may become more relevant when working
with AI systems. Analytical, 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
Impact of Artificial Intelligence on Workers’ Skills
48
employees still need to use their creativity to come up with new ideas, think outside the box, and
solve problems that AI systems 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 sit-
uations. 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., intuitive and
learning skills, and creative thinking) into the process to fill the gaps that AI systems bring (Xiaomei
et al., 2021).
Recent discussions in the field of psychology and artificial intelligence have highlighted the distinc-
tion between explicit and tacit knowledge, which is particularly relevant when considering AI's capac-
ity for learning and replicating human cognition. Explicit knowledge, being codifiable and transfera-
ble, is more readily accessible for AI systems to process and utilize. However, tacit knowledge, often
acquired through personal experience and difficult to articulate, presents a significant challenge for
AI implementation (Andrews & Smits, 2018; Oranga, 2023). While AI may excel in processing
deeper, explicit knowledge, it faces considerable obstacles in replicating the higher-order cognitive
functions associated with understanding, wisdom, and purpose. This underscores the importance of
recognizing the current limitations of AI in fully emulating the nuanced, experiential learning pro-
cesses inherent to human psychology. Consequently, the integration of human thinking skills, partic-
ularly those related to tacit knowledge and higher-order cognition, remains crucial in complementing
and enhancing AI capabilities in complex problem-solving scenarios.
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
Morandini, Fraboni, De Angelis, Puzzo, Giusino, & Pietrantoni
49
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
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 using 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 com-
municative interactions with customers, thereby also improving their own communication perfor-
mance (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
Impact of Artificial Intelligence on Workers’ Skills
50
team is able to overcome new challenges and continue to make progress towards the organisation’s
goals.
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.
Recent studies have highlighted several reasons why employees should enhance their physical and
manual skills in the era of AI integration. Firstly, improved physical and manual skills can signifi-
cantly boost efficiency when working with AI tools. Haslgrübler and colleagues (2019) found that
employees with advanced hardware and software proficiency or superior hand-eye coordination uti-
lize AI tools more effectively. Additionally, Niehaus et al. (2022) highlighted that as AI often involves
interaction with physical systems like robots or automated machinery, enhanced physical and manual
skills are crucial for ensuring workplace safety and preventing accidents or injuries. Lastly, developing
these skills increases adaptability to new technologies and evolving work environments. Wamba-
Taguimdje et al. (2020) revealed that workers who are adept at handling various types of equipment
are better positioned to adapt to new AI technologies as they are introduced in the workplace. Thus,
the improvement of physical and manual skills remains vital in complementing and effectively lever-
aging AI technologies in the modern workplace.
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 outlines the opportunities and limitations of using AI to acquire or improve Transversal
Skills and Competencies (TSC). It categorizes TSC based on the model we described. For each spe-
cific skill within these categories, the table presents potential opportunities for skill development,
such as personalized learning tools and AI-powered assistants. It also highlights limitations, including
risks of over-reliance on AI, potential loss of fundamental skills, and the inability of AI to fully repli-
cate human aspects like empathy or creativity.
Morandini, Fraboni, De Angelis, Puzzo, Giusino, & Pietrantoni
51
Tab l e 1. Opportunities and limitation of the use of AI in Transversal Skills and Competences
Transversal
Skills and Competencies
(TSC)
Opportunities and limitations of the use of AI
in the acquisition or improvement of the TSC
Core skills
Working with num-
bers and measures
Opportunities: AI-powered calculators and data analysis tools; adaptive learning systems for STEM educa-
tion; visualization tools for complex numerical concepts.
Limitations: may reduce mental math abilities if overused; over-reliance on AI for basic calculations.
Working with digi-
tal devices and ap-
plications
Opportunities: personalized tutorials and guides; predictive text and autocomplete features
Limitations: constant upskilling due to rapid changes in AI technologies; privacy concerns tracking user
behavior.
Mastering lan-
guages
Opportunities: AI-powered language learning apps with personalized lessons; real-time translation and in-
terpretation tool.
Limitations: not capturing cultural nuances and context; risk of over-reliance on translation tools instead
of true language mastery.
Thinking
skills
Processing infor-
mation, ideas, and
concepts
Opportunities: mind mapping and concept visualization tools; intelligent summarization of complex texts.
Limitations: potential for AI bias in information processing; risk of reducing critical thinking if AI is relied
upon too heavily.
Dealing with prob-
lems
Opportunities: simulation tools for problem-solving practice; AI-powered decision support systems.
Limitations: not accounting for unique or unprecedented problems; risk of becoming dependent.
Planning and or-
ganizing
Opportunities: AI-powered project management tools; smart scheduling assistants; predictive analytics for
resource allocation.
Limitations: not accounting for human factors and unexpected changes; loss of organization skills if over-
relied upon.
Thinking creatively
and innovatively
Opportunities: genAI for exploring new design possibilities; collaborative AI systems for brainstorming.
Limitations: risk of homogenizing creative output; stifle unique human creativity if overused.
Self-man-
agement
skills
Working efficiently
Opportunities: productivity trackers and analytic; task prioritization systems; automated routine task han-
dling.
Limitations: pressure to constantly optimize and quantify work; loss of personal time management skills.
Taking a proactive
approach
Opportunities: trend analysis and forecasting tools; intelligent reminders and nudges.
Limitations: reduction of intrinsic motivation if external prompts are overused.
Maintaining a posi-
tive attitude
Opportunities: positive affirmation and mindfulness tools.
Limitations: risk of replacing genuine human support and connection.
Demonstrating will-
ingness to learn
Opportunities: Adaptive learning systems that adjust to individual progress; gamification elements to in-
crease engagement.
Limitations: risk of reducing intrinsic motivation for learning.
Social and
communica-
tion skills
Following ethical
code of conduct
Opportunities: Automated checks for bias and ethical considerations in decision-making.
Limitations: not grasping complex ethical nuances; risk of oversimplifying ethical decision-making.
Communicating
Opportunities: writing assistants for clear and effective communication; real-time speech analysis.
Limitations: not capturing subtle human communication cues and context; risk of over-reliance.
Supporting others
Opportunities: coaching and mentoring systems.
Limitations: lack ok genuine human empathy and connection; risk of depersonalizing social support.
Collaborating in
teams and networks
Opportunities: collaboration platforms with smart features; automated coordination and task distribution.
Limitations: risk of reducing face-to-face collaboration skills.
Leading others
Opportunities: systems for team performance analysis; automated feedback collection and analysis.
Limitations: impossibility to replicate human inspiration and charisma; risk of data-driven decisions in
leadership.
Physical and
manual
skills
Manipulating and
controlling objects
and equipment
Opportunities: simulation and training systems (e.g., augmented reality).
Limitations: may not replace hands-on physical practice; not accounting for individual physical differences
and limitations.
Responding to
physical circum-
stances
Opportunities: situational awareness training (e.g., virtual reality simulations).
Limitations: lack of preparation for unpredictable real-world scenarios; risk of over-reliance on technology
in critical situations.
Impact of Artificial Intelligence on Workers’ Skills
52
THE DUAL NAT URE OF AI’S IMPACT ON WORKERS’ SKILLS
The incorporation of AI into the workplace can provide opportunities for workers to acquire and
develop a wide range of skills (i.e., transversal skills). However, it is important to recognise that there
might be a “dark side” of AI when applied uncritically, so there are pitfalls and shortcomings to pay
attention to. For instance, individual workers may approach learning and development processes with
different attitudes and motivations, and may have varying mental models towards the changes ex-
pected using AI at work. Moreover, there might be differences in experiencing AI based on personal
disadvantages or socioeconomic challenges. For example, Smith and Smith (2021) provide first-hand
empirical evidence on how AI technology can assist and frustrate the lives of disabled people, both
at the same time. In general, artificial intelligence is not always exempt from bias - such as ethnicity-
based or gender-based (e.g., Ntoutsi et al., 2020). Suppose that organisations do not take these ele-
ments and differences into consideration, thus applying AI-based skill development strategies with-
out regard for the vulnerabilities and needs of their workforce. In that case, these strategies may ex-
acerbate existing inequalities within the organisation and society at large. Therefore, organisations
must carefully consider the impact of AI on learning and development, ensuring that any imple-
mented strategy considers the diverse needs and perspectives of their workforce (Zajko, 2022). AI
should be thoughtfully implemented and consciously managed to provide every worker with equal
opportunities for learning and development of 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 automatically lead to a systematic improvement in employees’ skills. The use of AI in
the workplace may enhance workers’ skills but, if not properly managed, it may also limit work pac-
ing and reduce employees’ autonomy (Alsheibani et al., 2019; Bérubé et al., 2021; Nylin et al., 2022).
In other words, poorly implemented AI in the workplace can create performance constraints (e.g.,
need to troubleshoot the AI). This, in turn, might create a non-favourable environment to acquire
employees’ job-relevant skills, causing delays in this process. Indeed, AI in the workplace introduces
increased complexity and more need for interaction and adaptability.
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 decision-making AI systems
on work is similar (albeit to a lesser extent), while using AI to give instructions leads to a faster work
pacing, greater autonomy, and less learning. For low-skilled jobs, using AI to make decisions has no
impact on work, while using AI to give instructions increases work pacing. These findings highlight
the complexity of the relationship between AI introduction and workers’ skills, as well as the need to
consider the specific context and skill level of the job when implementing AI systems. Therefore, it is
important to assess not only whether AI can promote the development of workers' skills, but also
the organizational configurations that optimize its use and the reasons why it is effective. This in-
cludes identifying the moderating and mediating factors that can facilitate the effective introduction
of AI in the workplace.
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53
UPSKILLING AND RESKILLING IN ORGANISATIONS
“Upskilling” and “reskilling” are the processes that develop and retrain employees’ skills. Even
though they are similar concepts, in that they both involve learning new skills, there is a key differ-
ence between the two. The term “upskilling” typically refers to the process of improving existing
skills that are directly relevant to an employee's current job or industry. The aim of upskilling is either
to advance one’s career or to become more effective in the current position (Moore et al., 2020). “Re-
skilling”, on the other hand, involves learning completely new skills outside one’s current field. The
aim of reskilling is usually to move an employee to another occupation or industry (Sawant et al.,
2022).
Upskilling employees can help organisations to foster a culture of continuous learning and develop-
ment. While improving work engagement and employees’ motivation, it also supports organisations
in attracting and retaining top talent. In addition, a culture of continuous learning and development
can help organisations 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 unemployment as AI takes over mundane tasks previously done by humans. Although a
technological revolution may be imminent, its scale and timeframe are currently unknown. In an in-
creasingly 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 thrive in their employment (Jaiswal et al., 2022).
Reskilling is an important process for organisations that introduce AI systems, as it can support em-
ployees adapting to technology-related changes. According to Makarius et al. (2020), reskilling helps
employees develop the knowledge and skills they need to work effectively with the technology - but
in a new role. This includes training in areas such as data analytics, machine learning, and program-
ming. Reskilling can help organisations to improve their overall competitiveness, as employees with
the right skills are better equipped to drive innovation and create value for the company (L. Li, 2022).
Therefore, reskilling can cushion the negative impact of AI on the workforce. For example, some
employees may be concerned that the introduction of AI threatens their job security (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).
As the introduction of AI into the workplace keeps transforming the nature of work and the skills
required to perform it, it is increasingly important for both workers and organisations to address the
gap between their current skillset and the necessary skills to navigate these changes successfully.
Identifying and understanding this skill gap is a crucial first step in developing effective strategies for
upskilling and reskilling the workforce. Organisations can then develop strategies to bridge the identi-
fied skill gap, ensuring that employees have the necessary skillset to use AI effectively. This ensures
that all workers can benefit from the advantages of AI (Kar et al., 2020).
Impact of Artificial Intelligence on Workers’ Skills
54
SKILL GAP ANALY SIS
There are several ways in which organisations can assess the skill gap within their workforce. It is
crucial to consider both external factors (such as industry trends and market demands) and the spe-
cific capabilities of adopted AI systems to identify the skills needed to effectively leverage AI in or-
ganisations. Skill gap analysis is a technique that helps organisations achieve such objective. A skill
gap analysis is a process used to identify the skills that are needed in a specific job or industry, com-
paring them to the skills that are currently possessed by workers in that field (Hay, 2003; Reich et al.,
2002). This analysis can identify discrepancies between the skills required for a job and the skills that
workers currently have, helping organisations make informed decisions on employees’ training and
development. By identifying skill gaps, organisations can easily tailor training programs to address
specific needs, while individuals can target their own learning and development efforts to improve
their job performance, ultimately advancing their careers.
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, as well as the skills that workers currently have, (2) analysing job postings and job de-
scriptions 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 in-
dustry standards or benchmarks to determine the extent of any skill gap.
A study conducted by McGuinness and Ortiz (2016) aimed to identify the key factors that deter-
mined the correct identification of skill gaps in an Irish company. The skill gaps were identified by
disseminating a survey to managers and workers of the company. By cross-referencing the data, the
level of agreement in the perception of skill gaps within the organisation was assessed, measuring
their impact on firm-level performance. Based on average responses, the areas with the most severe
skill gaps were identified (i.e., information technology and communications, technology, and manage-
ment). The analyses and results support the added value of the skill gaps identification methodology,
as well as the importance of upgrading and retraining in areas that can be improved for business per-
formance.
In a 2019 study, Aiswarya and colleagues used interviews and focus groups to conduct 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 programs, allowing trainers and managers to bridge gaps in cor-
responding transversal skills, and to improve their work performance.
A skill gap analysis conducted in the EU project FIT4FoF (www.fit4fof.eu) identified over 100 new
job profiles across six technological areas, including data analytics, cybersecurity, collaborative robot-
ics, and human-machine integration. This analysis addressed new job market scenarios in advanced
manufacturing. Examples of these new roles include Robotics Technicians, who install, maintain, and
program industrial robots and automated systems, and Human-Machine Interaction Designers, who
develop intuitive user interfaces and interactions for AI systems.
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55
MEASURES FOR UPSKILLING AND RESKILLING
Establishing innovative methodologies is crucial to implement new skills and minimise skill gaps. In
other words, once the need for skills has been defined, a long-term plan and methodology should be
implemented to develop new skills and minimise the gaps between organisations and its current
workforce. This should include tools that provide solutions for attracting new talent, organised train-
ing programs for current workers, and redesign of work processes. Several training and development
solutions are available to support and guide upskilling and reskilling of workforce skillsets (Ceschi et
al., 2022).
ADDIE MODEL OF TRAINING.
Skill, knowledge, and performance gaps can be bridged by designing meaningful training programs
with tools like ADDIE (J. Li, 2016). One of the most used methods to develop new training pro-
grammes is called Instructional Systems Design (ISD). There are several ISD models, but most are
represented by the acronym ADDIE (Analysis, Design, Development, Implementation and Evalua-
tion). Each letter represents a phase, ordered in a logical sequence to ensure a practical approach to
training programme design. A study conducted by Guevarra and colleagues (2021) showed the devel-
opment of a training programme based on the ADDIE model that aimed to improve health workers’
decision-making skills in evaluating data generated by technological tools. The programme involved
128 Filipino public health workers who were asked to rate the clarity and relevance of the objectives,
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 decision-making
capacity, the results demonstrated the value and reliability of the ADDIE model in developing a
training programme to improve staff capacity.
TRAINING FOR SKILFUL HUMAN-AI TEAMING
In some sectors, for example healthcare, the adoption and scale-up of artificial intelligence can help
alleviate the shortage of human resources. AI has transformed some areas, such as patient care, ad-
ministrative tasks, and clinical decisions. Rizvi and Zaheer (2022) analysed how healthcare profession-
als can be trained to provide services supported by AI. In healthcare, AI can reduce human errors
due to human fatigue, support and replace labour-intensive tasks, minimise invasive surgeries, and re-
duce mortality rates. There is an urgent need to train healthcare professionals, leading to skilful inter-
actions between medicine and machines.
In the IT sector, hackathons are adopted to facilitate education by offering participants the oppor-
tunity to learn new skills and technologies. Hackathons can facilitate both upskilling and reskilling by
providing participants with the opportunity to learn new skills and knowledge, applying them in a
real-world setting (Medina Angarita & Nolte, 2020). A hackathon is an event where people work to-
gether on a project over a relatively short period of time. Participants often work in teams at hacka-
thons, allowing them to learn from each other and share their expertise. This kind of collaborative
learning environment can help to understand how to work in teams with AI tools and systems.
LIFELONG LEARNING AN D “HANDS-ON” LEARNING MODULES
Industry 4.0 systems and related technologies are widely adopted due to the efficient implementation
of lifelong learning and training initiatives addressing upskilling and reskilling challenges. Oliveira and
colleagues (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 relying on solid hands-on
practical experimentation on upskilling in emergent ICT technologies. Feedback from participants
shows that these short hands-on learning modules strongly contribute to qualifying the workforce
and undergraduate students in emergent ICT.
Impact of Artificial Intelligence on Workers’ Skills
56
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 to use AI systems. As Japanese companies have traditionally empha-
sised lifelong employment, they have created a valuable culture of training programmes for their em-
ployees. One of the most well-known programs is the Quality Circle, which can be successfully used
for employees’ AI-related upskilling and reskilling. This programme aims to involve employees in de-
cision-making, leading the company towards a more participatory culture, and trains employees to
think critically, fostering problem-solving skills when performing AI-based tasks at work. The circles
usually meet four hours a month during working hours. A team leader, who is usually a trained mem-
ber of the management team, helps train the circle members and ensures that everything runs
smoothly. Members receive recognition when their suggestions for improving production are ac-
cepted (Lawler et al., 1984).
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
mitigate this problem, upskilling and reskilling processes seem to be crucial for training and support-
ing employees (Giabelli et al., 2021). However, like any major change, implementing these processes
comes with costs and benefits for both organisations and 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 that face skill mismatch. First, they increase productivity: by providing their employees
with needed skills to do their jobs effectively, companies can increase the efficiency of their work-
force (Zapata-Cantú, 2022). Second, they foster competitiveness: upskilling and reskilling can help
organisations remain competitive by ensuring that they have a skilled and adaptable workforce that
can meet the changing needs of the business (Ponce Del Castillo, 2018). Thirdly, they improve em-
ployee 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).
However, implementing upskilling and reskilling programmes can require a significant investment in
terms of time and resources. According to Abe and colleagues (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 and
workshops. In addition, upskilling and reskilling involve time costs: employees may have to be absent
from work to attend training, causing interruptions to business, 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 resistance to change and, possibly, lower participation (Aguiar et al.,
2022).
Morandini, Fraboni, De Angelis, Puzzo, Giusino, & Pietrantoni
57
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. Table 2 outlines the main costs and benefits for organizations planning to imple-
ment up-skilling and reskilling interventions, along with some examples related to these efforts.
Tab l e 2. Costs and benefits of upskilling and reskilling for organisations
Examples of programs
Benefits
Costs
Upskilling
Food producers in small busi-
nesses learning to use AI for
quality control; Customer service
reps training to use AI chatbots
alongside their work.
-Improved efficiency and
productivity through aug-
mented work processes
-Increased employee value in
an AI-integrated workplace
-Investment in AI-specific train-
ing programs and courses (e.g. A
literacy courses)
-Time spent by employees learn-
ing new tools
Reskilling
Administrative assistants transi-
tioning to AI systems manager;
Manufacturing workers retrain-
ing to maintain and oversee an
AI robotic systems.
-Creation of high-value
roles within the organization
-Improved organizational
adaptability
-rPotential for innovative
applications
-Time and financial investment
-Potential loss of traditional skill
sets as employees fully transi-
tion
-Risk of skill mismatch if AI
adoption does not progress as
anticipated
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 (Hammer & Karmakar, 2021). Without adequate support from the organi-
sation, including training, resources, and support for learning, upskilling and reskilling efforts may be
difficult or unsuccessful. Therefore, to be effective, upskilling and reskilling efforts must be well-
planned and well-supported by both the organisation and the individuals involved.
At a broader level, countries with poor education and structures may be reluctant to invest in up-
skilling 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 pro-
duction in which the practises of skills development occur, which is crucial for understanding the
outcome of skills policies and practices (Hammer & Karmakar, 2021; Petersen et al., 2022). Up-
skilling and reskilling might be difficult in some countries with poor education, with firms’ reluctance
to invest in training, and with people relying on informal skilling (Hammer & Karmakar, 2021;
Ramaswamy, 2018). In developing countries, 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,
as well as the lack of recognition of informally gained skills, are likely to be persistent challenges for
upskilling and reskilling. Organisations need to find strategies to reverse these 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 workers but could not change em-
ployment conditions for the majority. Public policies are needed to ensure that emerging technologies
are used responsibly to complement rather than replace labour, which would have negative distribu-
tional effects. Public sector investment in skills initiatives is much lower in developing countries like
Impact of Artificial Intelligence on Workers’ Skills
58
India than in developed countries like Germany. India has tried to change this through public-private
partnerships in Industrial Training Institutes and industry-led vocational training programmes. In Eu-
rope, governments are working with technology companies to solve employment problems and fill
skill gaps, as the skill demand will change in the near future for different occupations. European poli-
cies 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 can benefit from technological advances and to prevent further gender inequalities in the
labour market. In this context, it is important to consider 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 digital divide in access to technology and the internet can negatively affect reskilling. 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 of internet services and devices decreases. In those countries where gender inequal-
ity is particularly pronounced, low levels of literacy, education, and skills are likely to prevent women
and other socially disadvantaged groups from leveraging new technologies. The number of jobs re-
quiring extensive knowledge in science, technology, engineering, and mathematics has increased over
the last decade, and advances in AI will require even more expertise in STEM. According to Billion-
niere and Rahman (2022), it is important to build capacity and widen participation in computing
through training women with the emerging technology gateway. A gender-sensitive approach to up-
skilling and reskilling 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 can 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 for both upskilling and reskilling. Older individuals who lack proficiency in
using new technologies may be at a disadvantage when it comes to finding and maintaining employ-
ment, as many jobs now require mastering digital skills. This can lead to age discrimination in the
workplace and contribute to age-related inequalities 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 supporting older workers 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 work-
force 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 of new technol-
ogies by all employees, regardless of age. This can include providing training and resources, offering
flexible work arrangements, and promoting a culture of inclusivity and continuous learning. In a sys-
tematic review, Longoria and colleagues (2022) highlight the importance of considering inclusive and
accessible ICTs design in engineering and design programs. Addressing ICTs design with a diverse
approach might foster students’ innovation capabilities and sensibility towards vulnerable populations
throughout the design process. There are plenty of possibilities to bridge this gap using technology
as a responsive, empathetic, and learning tool to address the age-related digital divide, ultimately en-
hancing workers’ skills to maximise organisational performance and productivity.
Morandini, Fraboni, De Angelis, Puzzo, Giusino, & Pietrantoni
59
CONCLUSIONS
AI is a complex and multifaceted field that encompasses a wide range of disciplines, including com-
puter science, mathematics, engineering, 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 organisations has - and will continue to have - an impact on the nature of work in the
coming decade. A thorough and critical examination of the literature has revealed that AI has the ca-
pacity 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 in-
troducing AI into organisations. In particular, the focus should be on upskilling and reskilling em-
ployees because 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.
The importance of investing in human capital is a crucial aspect to successfully integrate AI into
companies and maximise its potential benefits for organisations and employees. The adaptation pro-
cess involves and combines several organisational strategies. Firstly, capturing the soft skills needed
by workers is critical to address the current skill gap in the workplace. Organisations can then help
workers identify the skills needed for AI adoption and develop new skills. Then, organisations need
to provide training and development opportunities, ensuring that workers’ attitudes and mental mod-
els towards AI are open and prepared for the challenges of the evolving labour market.
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, helping to ensure that the benefits of AI
are shared equitably across all stakeholders. Hence, practitioners and stakeholders need to invest in
upskilling and reskilling workers, as these processes will likely create a more adaptable and skilled
workforce that can meet the challenges and opportunities of the future.
Impact of Artificial Intelligence on Workers’ Skills
60
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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.
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.
Impact of Artificial Intelligence on Workers’ Skills
68
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.
Prof. Luca Pietrantoni is a Full Professor of Work and Organisational
Psychology at the University of Bologna. He is currently leading a re-
search team at the University of Bologna on Human Factors in three EU
projects on AI at work (TUPLES, Edge AI) and Human-Robot Collabo-
ration (SESTOSENSO). His main research interests include adoption of
technology in organizations, human-machine interface and health and
safety at work. He has published in international academic journals, in-
cluding Accident Analysis, Prevention and Risk Analysis, and Frontiers in
Psychology.
