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 such risks into account and instead foster upskilling of workers. Hybrid Intelligence may thus, in the long run, lower costs and improve performance and job satisfaction, as well as prevent management from creating unintended organization-wide deskilling.
Teaser
As AI systems automate knowledge work, the future of human expertise depends on how we design collaboration — not replacement. This conceptual paper outlines how Hybrid Intelligence can turn automation from a deskilling threat into an opportunity for shared upskilling and reskilling.
Short Intro
Artificial intelligence is transforming how professionals think, learn, and act. While automation can outperform humans in speed and precision, it risks deskilling experts who gradually lose the competencies that once defined their expertise. This article, authored by Jacob Sherson and the Center for Hybrid Intelligence, argues that the success of AI should not only be measured by efficiency, but by how it enhances human competence and creativity.
The Research Challenge
Traditional approaches to AI adoption often emphasize accuracy, efficiency, and cost reduction, but neglect the human learning dimension. The challenge is to prevent the slow erosion of expertise that occurs when decision-making and feedback are delegated entirely to machines.
The paper therefore proposes a conceptual shift — from technology-oriented success metrics to partnership-oriented measures, where success is defined by how well AI systems support human understanding, engagement, and agency.
The HI Approach
The article introduces a conceptual progression of human–AI collaboration:
- Human-in-the-loop (HITL) – humans provide data for AI learning,
- Human-on-the-loop (HOTL) – humans oversee and validate AI decisions,
- Human-out-of-the-loop (HOOTL) – AI operates autonomously, and
- Hybrid Intelligence (HI) – humans and AI share agency and competence.
These four interaction modes illustrate how automation can either deepen deskilling or, when guided by Hybrid Intelligence principles, enable adaptive upskilling and reskilling.
HI provides a way to maintain human cognitive engagement through reflective feedback loops and shared learning processes, ensuring that humans remain active participants in defining, supervising, and improving AI outcomes.
Findings
The paper proposes a shift from measuring AI’s success by efficiency to measuring it by competence development. It argues that the true value of automation lies in how it supports human growth — promoting metacognitive awareness, contextual judgment, and continuous reskilling.
Hybrid Intelligence thus serves as a conceptual bridge that reframes automation as an ongoing co-evolution between humans and machines, not a substitution.
Impact
This perspective reframes AI adoption as a learning challenge rather than a purely technical one. By embedding Hybrid Intelligence principles into digital transformation, organizations can avoid long-term deskilling while cultivating reflective, adaptive teams.
The work extends the Hybrid Intelligence narrative to workforce development — emphasizing partnership, growth, and shared agency as the foundation for a sustainable future of work.
Keywords: Hybrid Intelligence, deskilling, upskilling, reskilling, automation, learning, human–AI collaboration, cognitive engagement
Suggested citation: Rafner, J. F., Dellermann, D., Hjorth, H. A., Verasztó, D., Kampf, C. E., Mackay, W. E., & Sherson, J. (2022). Deskilling, upskilling, and reskilling: A case for hybrid intelligence. Morals + Machines, 1(2), 25–39. https://doi.org/10.5771/2747-5174-2021-2-24