Skills, training and redeployment
What the evidence says about retraining the people already on the floor, which is the manifesto's central claim and one we should be held to.
10 works. Metadata and abstracts from OpenAlex, harvested 2026-10-14. We have not summarized, graded or characterized this research ourselves, and the abstracts below are the authors' own.
The Impact of Artificial Intelligence on Workers’ Skills: Upskilling and Reskilling in Organisations
Aim/Purpose: This paper examines the transformative impact of Artificial Intelligence (AI) on professional skills in organizations and explores strategies to address the resulting challenges. Background: The rapid integration of AI across various sectors is automating tasks and reducing 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. 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...
Deskilling, Upskilling, and Reskilling: a Case for Hybrid Intelligence
Advances in AI technology affect knowledge work in diverse fields, including healthcare, engineering, and management. Although automation and machine support can increase efficiency and lower costs, it can also, as an unintended consequence, deskill workers, who lose valuable skills that would otherwise be maintained as part of their daily work. Such deskilling has a wide range of negative effects on multiple stakeholders -- employees, organizations, and society at large. This essay discusses deskilling in the age of AI on three levels - individual, organizational and societal. Deskilling is furthermore analyzed through the lens of four different levels of human-AI configurations and we argue that one of them, Hybrid Intelligence, could be particularly suitable to help manage the risk of deskilling human experts. Hybrid Intelligence system design and implementation can explicitly take...
Robotics in Manufacturing: A Review of Advances in Automation and Workforce Implications
The integration of robotics in manufacturing processes has undergone significant advancements, reshaping the landscape of industrial production and introducing transformative changes. This review explores the latest developments in robotics within the manufacturing sector, shedding light on the technological breakthroughs and their implications on the workforce. The recent surge in robotics adoption in manufacturing has been driven by the pursuit of increased efficiency, precision, and cost-effectiveness. From traditional robotic arms to cutting-edge collaborative robots (cobots) that work alongside human operators, the manufacturing industry has witnessed a paradigm shift. Advanced robotic systems equipped with artificial intelligence (AI) and machine learning algorithms enable adaptive and autonomous decision-making, further enhancing their capabilities. Automation in manufacturing...
Adapting to digital transformation: Determinants of training motivation in response to digital automation among workers in six EU countries
Research background: The increasing automation of work tasks is transforming labour markets, creating both challenges and opportunities for workers. Reskilling and upskilling through training are essential for maintaining employability in the rapidly changing digital economy. While automation may complement certain job roles, it substitutes others, leading to skills mismatches and heightened concerns about job security. Previous studies have provided inconsistent findings regarding the influence of automation on workers' training motivations, lacking detailed distinctions between task complementarity and substitution effects, as well as differentiations in types of job insecurity. Purpose of the article: This article examines the key determinants of workers' motivation to participate in training in response to automation. It specifically addresses the gaps in literature by clearly...
A processual approach to skill changes in digital automation: The case of the platform economy in the service sector
We introduce the “processual approach” to skill changes in the current wave of digital automation, which imposes comprehensive and complex impacts on skills. The approach conceptualizes work as a set of processes, each consisting of a sequence of events. In each event, a worker and/or machine make judgments and take actions to move to the next event. The processual approach asks whether and how machines influence workers' judgments or actions during each event and interrupt or transform relationships between judgments and actions. The approach enables micro-to-middle-range, inductive theorization of skill changes. To further refine the approach and demonstrate how to apply the approach, we study the case of taxi and ride-hailing, finding that service skill changes emphasize the repositioning and refocusing of skills and the interruption of workers' micro-adaptations rather than the...
Artificial Intelligence's Impact on Employment: Challenges, Potential Consumers, and Policy Responses Through Automation and Workforce Rehabilitating
The employment market has undergone an enormous shift as a result of the rapid progress of artificial intelligence (AI). This transformation presents both significant challenges and opportunities. This paper examines the ways in which AI-driven automation is transforming a variety of industries, resulting in employment displacement in sectors that rely on routine tasks and the creation of new roles in technology, data management, and AI maintenance. Addressing the growing disparities between those who benefit from AI and those who are left behind, as well as managing large-scale employment displacement, are the primary challenges. This is particularly true for low-skilled workers. AI, however, also offers chances to boost creativity, productivity, and the creation of highly skilled jobs in cutting-edge industries. In order to alleviate adverse consequences, this investigation...
The Impact of Artificial Intelligence on Workforce Automation and Skill Development
This study explores the profound impact of artificial intelligence (AI) on workforce dynamics, focusing on automation trends and the imperative for skill development, exemplified by case studies of InnovateTech Manufacturing and SkillCraft Solutions. The findings underscore the vulnerability of routine tasks to automation, prompting a nuanced understanding of sector-specific challenges. Soft skills emerge as pivotal in an AI-centric job market, with a growing emphasis on adaptability and technological literacy. The study used mixed approach and using secondary as well as primary data with advocates for a holistic approach to workforce development, encouraging continuous learning, strategic automation, and collaboration across industries. Recommendations address the need for reskilling programs, industry-academia collaboration, and policies that safeguard workers in the evolving AI...
THE DYNAMICS OF TASK AUTOMATION AND WORKER ADJUSTMENT IN LABOR MARKETS: AN AGENT-BASED APPROACH
Artificial Intelligence and associated technologies are rapidly automating routine and non-routine tasks across industries and can severely disrupt labor markets. This paper presents an agent-based, evolutionary model of labor market dynamics where workers adjust to technology shocks induced by automation. Firms produce a homogeneous service by combining the outputs of tasks performed by workers while stochastically adapting to automation of tasks causing displacement of workers. We develop a model that includes: (i) the description of occupation mobility as a directed graph where nodes represent occupations, and the directed edges represent the mobility pathways along which displaced workers can get retrained and redeployed (ii) explicit microfoundations of the processes of job matching and wage setting between firms and heterogeneously skilled workers. The model focuses on the...
Resilienz statt Reskilling: Wie KI den Arbeitsmarkt verändert und wie wir darauf reagieren müssen
Generative artificial intelligence (AI) reshapes labour markets by altering tasks and occupations, demand for skills, and power relations between workers and employers. Unlike past waves of technological change, AI now challenges high-skilled, white-collar professions that were once considered secure. Evidence shows that displacement occurs in tasks such as translation and writing, while complementary skills benefit, and new occupations emerge. To support a more sustainable transformation of the labour market in light of the ever-increasing capabilities of AI, building a resilient labour force that can adjust to an evolving technological landscape will become more important than reskilling programmes for people in occupations with a high automation risk.
Reskilling and Policy Responses to AI-Driven Labor Market Transformation
As the Human Machine Interface evolves, AI is steadily reshaping the labor market by eliminating low-skill tasks while generating demand for high-skill, technology-driven jobs. This article examines both the benefits and drawbacks of AI for employment. Automation is already displacing jobs in manufacturing, logistics, retail, and customer service, creating pressure on untrained laborers while intensifying demand for highly skilled workers. Many displaced employees struggle to transition into new roles, and without effective support, they risk relying on shrinking welfare systems. Wage suppression may temporarily mask these issues but offers no long-term solution. Although automation fosters new opportunities in fields such as data science, machine learning, cybersecurity, and renewable energy, these roles are largely inaccessible to unskilled workers. Developing countries face...
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