As more routine tasks within the company are taken over by artificial intelligence (AI), it is becoming increasingly important for employees to upskill, or learn new skills, to understand and effectively use AI in the workplace (Jaiswal et al., 2022). The decreasing demand for manual labor further requires employees to concentrate on tasks that require higher-order thinking and emotional skillsets, such as soft skills, which do not follow patterns and are therefore, difficult to automate (Huang & Rust, 2018).

For individuals to ensure their competitiveness and employability, there is a crucial need for upskilling in the following competences: data analysis skills, digital skills, complex cognitive skills, decision-making skills, and continuous learning skills. Data analysis pertains to the use of advanced statistics and programming to evaluate the data and find information that can aid in decision-making. Digital skills such as automation, cybersecurity, and runtime applications are necessary for more efficient processes with better security at lower costs (Bobitan et al., 2024; Jaiswal et al., 2022). Complex cognitive skills refer to higher-order information processing, such as visualizing and interpreting complex data, to discover innovative ways to solve problems. While decision-making skills have always been essential, upskilling to make evidence-based and unbiased decisions to improve business performance within a shorter time frame has become more critical in a world where there is an excessive amount of available data and rapidly changing trends (Jaiswal et al., 2022). In the end, all these skills can only be trained or retrained if the employees have the initiative to continuously learn and try to improve themselves. Aside from technical skills, it should also be noted that soft skills, such as leadership, communication, and interpersonal skills, remain essential to motivate employees in creating an environment that encourages collaboration and continuous skill development to improve their strengths and capabilities (Bobitan et al., 2024; Jaiswal et al., 2022).

Soft skills, or transversal skills and competences, are defined by Hart et al. (2021) as learned abilities that are essential for effective action in any kind of work, learning, or life activity (Fig. 1). Their model has 6 main categories namely: core skills and competences, thinking skills and competences, self-management skills and competences, social and communication skills and competences, physical and manual skills and competences, and life skills and competences.

Figure 1. Transversal Skills and Competences (TSC) Model

Adapted from Hart et al. (2021)

Core skills and competences refer to the ability to understand, speak, read, and write languages, work with numbers and measurements, and use digital devices and applications. They symbolize the foundation for interaction with others and growing and learning as an individual (Hart et al., 2021). In the context of AI, language proficiency, particularly in English, can help employees navigate and utilize AI tools better because these technologies mostly have their interfaces in English (Benhayoun & Lang, 2021). Being comfortable with the use of numbers and measurements can be advantageous in the use of machine learning algorithms, while those who are knowledgeable in digital devices and applications can manage the AI systems better (Morandini et al., 2023).

Thinking skills and competences refer to the ability to gather, conceptualize, analyze, synthesize, and/or evaluate information obtained from or generated by observation, experience, reflection, reasoning, or communication. This is shown by using information to plan activities, achieve goals, solve problems, tackle issues, and perform complex tasks in routine and novel ways (Hart et al., 2021). As AI takes over repetitive tasks in the workplace, it becomes necessary for employees to be creative in solving problems that AI could not, such as those involving tacit knowledge and higher-order cognition that are acquired through years of experience (Celino et al., 2025; Morandini et al., 2023). When employees can question, prove, and use data to optimize various operational processes, they become valuable assets to the company (Singh & Chouhan, 2023), which also improves their employability.

Self-management skills and competences refer to an individual’s ability to understand and manage their strengths and limitations and use this awareness to handle activities in various contexts. This is made evident by a capacity to act in a reflective, responsible, and structured way in accordance with values, by accepting feedback, and pursuing opportunities for personal and professional development (Hart et al., 2021). By utilizing AI systems to automate time-consuming tasks, employees can focus on activities that require innovation, empathy, or other abilities that are unique to humans. When AI systems are utilized to provide personalized feedback and suggestions on work performance, employees can also be guided in identifying areas where they need improvements and encouraged to take part in upskilling or reskilling programs (Morandini et al., 2023). In this case, employees must be flexible, adaptable, and demonstrate willingness to learn various tasks and positions within the company. Managers, on the other hand, should exhibit social and communication skills, such as motivational and team-building skills, to support the employees (Henderikx & Stoffers, 2022).

Social and communication skills and competences refer to the ability to create a positive and productive interaction with other people which can be done by communicating ideas effectively and empathically, coordinating one’s own goals and actions with those of others, working towards resolutions to differences, building trust and resolving conflicts, preserving the well-being and progress of others, managing activities and offering leadership (Hart et al., 2021). AI systems are often complex and hard to understand so effectively communicating ideas and information with colleagues and other stakeholders ensures that everyone is working towards common goals (Morandini et al., 2023). It is also important that managers display empathy, open-mindedness, and patience in fostering a collaborative organizational culture to keep up with continuous change brought about by rapid digital and technological advancements since not all employees can achieve the same degree of digital readiness. Eventually, this can lead to better team performance allowing continuous progress towards the organization’s goals (Henderikx & Stoffers, 2022; Morandini et al., 2023).

Physical and manual skills and competences refer to the ability to accomplish tasks that require manual dexterity, agility, and/or physical strength. These tasks may need to be executed by hand, or by the use of equipment, tools, or technology that need guidance, movement, or force (Hart et al., 2021). As robots or automated machinery are often involved in AI systems, employees with enhanced physical and manual skills, such as advanced hardware and software proficiency and better hand-eye coordination, are able to use AI tools more effectively and safely (Morandini et al., 2023).

Lastly, Life skills and competences refer to the ability to process and manage knowledge and information to use it as a basis for forming opinions, making decisions, and taking actions related to personal and professional development, and social responsibility (Hart et al., 2021).

In the end, aside from technical skills related to digital readiness, various soft skills are necessary to smoothly transition from traditional offices to AI-integrated workplaces.

Keywords: soft skills, transversal skills, technical skills, hard skills, artificial intelligence, AI, テクニカル スキル, ハードスキル, ソフトスキル

References:

• Benhayoun, L., & Lang, D. (2021). Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps’ identification using text mining. Human Systems Management, 40(5), 639–651. https://doi.org/10.3233/HSM-211179
• Bobitan, N., Dumitrescu, D., Popa, A. F., Sahlian, D. N., & Turlea, I. C. (2024). Shaping Tomorrow: Anticipating Skills Requirements Based on the Integration of Artificial Intelligence in Business Organizations—A Foresight Analysis Using the Scenario Method. Electronics (Switzerland), 13(11), 2198. https://doi.org/10.3390/electronics13112198
• Celino, I., Carriero, V. A., Azzini, A., Baroni, I., & Scrocca, M. (2025). Procedural knowledge management in Industry 5.0: Challenges and opportunities for knowledge graphs. Journal of Web Semantics, 84. https://doi.org/10.1016/j.websem.2024.100850
• Hart, J., Noack, M., Plaimauer, C., & Bjørnåvold, J. (2021). Towards a structured and consistent terminology on  transversal skills and competences. https://esco.ec.europa.eu/uk/about-esco/publications/publication/towards-structured-and-consistent-terminology-transversal
• Henderikx, M., & Stoffers, J. (2022). An Exploratory Literature Study into Digital Transformation and Leadership: Toward Future-Proof Middle Managers. Sustainability (Switzerland), 14(2), 687. https://doi.org/10.3390/su14020687
• Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
• Jaiswal, A., Arun, C. J., & Varma, A. (2022). Rebooting employees: upskilling for artificial intelligence in multinational corporations. International Journal of Human Resource Management, 33(6), 1179–1208. https://doi.org/10.1080/09585192.2021.1891114
• 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. Science: The International Journal of an Emerging Transdiscipline, 26, 39–68. https://doi.org/10.28945/5078
• Singh, A., & Chouhan, T. (2023). Artificial Intelligence in HRM: Role of Emotional–Social Intelligence and Future Work Skill. In P. Tyagi, N. Chilamkurti, S. Grima, K. Sood, & B. Balusamy (Eds.), The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part A (pp. 175–196). Emerald Publishing Limited. https://doi.org/10.1108/978-1-80382-027-920231009

Related Articles:
What are soft skills, and why do they matter? ① Hard skills and Soft skills
What are soft skills, and why do they matter? (Part 2)