Career sustainability depends on how, not how much, AI is used

The study finds that moderate AI use plays a constructive role in career development. At this level, AI helps workers improve efficiency, expand skills, and adapt to changing job demands. Employees benefit from faster learning cycles, improved decision support, and exposure to new forms of problem-solving. These gains support core elements of career sustainability, including employability, flexibility, and long-term growth.

New academic research published in the journal Sustainability shows that while AI can strengthen long-term career sustainability by enhancing skills, adaptability, and learning, excessive reliance on AI reverses those gains and introduces new risks. The findings suggest that the future of work will not hinge on whether AI is adopted, but on how far its use is allowed to expand without strong human-centered safeguards.

The study, titled Rethinking Career Sustainability Through the Lens of AI Affordance: The Exploratory Role of Knowledge Sharing, examines how AI affects career sustainability among expatriate workers in China, a group often exposed to higher uncertainty and digital disruption. 

AI strengthens careers only up to an optimal threshold

The study finds that moderate AI use plays a constructive role in career development. At this level, AI helps workers improve efficiency, expand skills, and adapt to changing job demands. Employees benefit from faster learning cycles, improved decision support, and exposure to new forms of problem-solving. These gains support core elements of career sustainability, including employability, flexibility, and long-term growth.

However, the research shows that these benefits do not scale indefinitely. As AI use intensifies beyond an optimal point, the positive relationship reverses. High levels of AI integration are associated with declining perceptions of career sustainability, driven by growing concerns over job insecurity, role ambiguity, and erosion of personal agency. Workers begin to feel that their skills are being sidelined rather than enhanced, and that career control is shifting away from human judgment toward automated systems.

This inverted U-shaped relationship challenges common assumptions in policy and management debates that more AI adoption automatically leads to better outcomes. The study demonstrates that over-automation can undermine the very adaptability and resilience that sustainable careers require. When AI dominates decision-making, learning, and reasoning tasks, employees face a higher risk of de-skilling and dependency, making careers more fragile rather than more secure.

The findings are particularly significant in the context of expatriate workers. Expats often operate in unfamiliar regulatory, cultural, and organizational environments, which already place pressure on career stability. The study shows that high AI intensity amplifies these vulnerabilities, making expats more sensitive to technological overreach than local employees might be.

Knowledge Sharing Determines Whether AI Helps or Hurts

The study distinguishes between explicit knowledge sharing and tacit knowledge sharing, showing that each plays a different but complementary role in sustaining careers in AI-driven workplaces.

Explicit knowledge sharing refers to formalized, codified knowledge such as manuals, documented procedures, and structured training. The study finds that strong explicit knowledge sharing enhances the early benefits of AI use. When employees have clear access to documented AI-related knowledge, they are better equipped to learn new systems, improve performance, and extract value from AI tools at moderate levels of use. This strengthens the initial upward slope of career sustainability.

Tacit knowledge sharing, by contrast, involves informal learning through experience, mentoring, peer interaction, and contextual problem-solving. The study shows that tacit knowledge sharing becomes critical when AI use intensifies. At high levels of AI integration, tacit knowledge sharing helps buffer the negative effects on careers by preserving human judgment, contextual understanding, and adaptability. Employees who can exchange experiential insights are better able to reinterpret AI outputs, adjust workflows, and maintain a sense of professional relevance.

The research demonstrates that without strong knowledge-sharing practices, AI adoption becomes brittle. Explicit knowledge alone is insufficient when AI use grows complex and pervasive. Tacit knowledge sharing acts as a stabilizing force, allowing employees to navigate uncertainty and retain agency even as automation expands.

This distinction has major consequences for organizations pursuing digital transformation. Investments in AI technology without parallel investments in human learning systems increase the risk of career disruption. The study makes clear that career sustainability in the AI era is not a technological outcome but a socio-technical one, shaped by how knowledge flows between people and systems.

AI as a collaborator, not a replacement

AI delivers sustainable career benefits when it augments human capabilities rather than substitutes them. The most resilient career outcomes emerge when AI supports routine and analytical tasks while humans retain responsibility for judgment, creativity, and contextual decision-making.

This perspective aligns with a broader shift in AI research toward human–AI collaboration. The findings suggest that careers remain sustainable when workers perceive AI as a partner that expands their capacity, not as a competitor that erodes their role. Once that perception changes, career confidence declines even if jobs are not immediately lost.

For expatriates, this balance is especially critical. The study highlights that expats often lack informal networks and institutional protection in host countries, making them more dependent on organizational support. When AI systems are introduced without clear human-centered design, expats face heightened risks of marginalization and career stagnation.

The research also carries implications for human resource management. AI-driven HR systems are increasingly used for recruitment, evaluation, and performance management. The study warns that excessive reliance on such systems can weaken trust and reduce perceptions of fairness, further undermining career sustainability. Human oversight and interpretive flexibility are essential to prevent algorithmic management from becoming a source of long-term career harm.

Policy and organizational implications

The study suggests that policy frameworks focused solely on reskilling or automation readiness are incomplete. Sustainable careers require attention to intensity thresholds, governance, and knowledge ecosystems.

For organizations, the research calls for designing AI adoption strategies that deliberately limit over-automation. Clear role boundaries, continuous learning opportunities, and strong mentoring systems are necessary to prevent AI from crowding out human expertise. Firms that treat AI as a universal solution risk creating fragile career structures that undermine long-term productivity and employee well-being.

The study highlights the importance of inclusive digital transformation strategies for policymakers. Expatriates, gig workers, and cross-border professionals are disproportionately exposed to AI-driven disruption. Targeted upskilling initiatives, portable credentials, and support for knowledge-sharing networks can help mitigate unequal career risks in highly digitalized economies.

Career sustainability emerges as a key ethical dimension of AI adoption, alongside privacy, bias, and transparency. Technologies that erode long-term employability or agency pose social risks even if they improve short-term efficiency.