Generative AI is changing jobs faster than many workers can handle, but new research from Finland suggests the technology doesn’t have to hollow out careers. Under the right conditions, it can deepen engagement, adaptability, and long-term resilience.
Fear of generative AI is easy to recognize. A machine that writes, summarizes, designs, and answers questions may seem less like a tool and more like a replacement waiting quietly behind the scenes. But a new study from the University of Vaasa argues that the picture is more complex and, in some cases, more hopeful.
In his doctoral thesis in information systems science, researcher Zhe Zhu examined how artificial intelligence, and specifically generative AI, is changing both organizational decision-making and employees’ daily work experiences. His conclusion is not that the technology is harmless. That means its effectiveness depends largely on how people and organizations approach it.
Workers who view generative AI as helpful collaborators and trust them without relinquishing their judgment tend to be more engaged in their work and ready to adapt their careers over time. The danger is not just that AI will be misused. Employees may also misjudge what it means for their future.
“As NVIDIA CEO Jensen Huang pointed out, workers are not just being replaced by AI, they are being replaced by people who have learned how to use GenAI to work more efficiently,” Zhu said. “Employees who perceive GenAI more positively are more engaged in their careers and more adaptable.”
More than just software upgrades
Zhu’s paper treats generative AI as more than just productivity features built into office software. The technology is described as part of a broader socio-technical shift that will change the way decisions are made, tasks are shared and employees judge their worth in the workplace.
This is important. Because generative AI can do things that previous workplace systems could not. It can not only categorize information and automate routine steps, but also generate alternatives. You can draft, compare, simulate, and make recommendations. In other words, they can participate in the decision-making process itself, rather than just assisting from the sidelines.
This study brings together four related studies. One mapped previous literature on human-AI interaction in decision-making. The other looks at how organizations move from experimenting with generative AI to incorporating it into real-world workflows. The other two focus on workers, one on work engagement and one on long-term career development.
Both of these represent the same idea. In other words, results depend more on the interactions between systems, organizations, and employees than on raw technical capabilities.
Why do the same tools motivate some employees and make others anxious?
A key part of Zhu’s work focuses on what employees think AI collaboration means for them. In a survey of 395 U.S.-based professionals who use generative AI tools in their work, collaboration with AI is strongly tied to what the survey calls opportunity assessment. Employees who see AI as a source of support, growth, and performance improvement are more engaged in their work.
The threat assessment told a different story. This had a negative impact on work engagement, but was not directly caused by AI collaboration itself. This suggests that employees do not automatically perceive automatically generated AI as a threat. Threat is more easily heightened when other stressors are already present.
One of those stressors is job insecurity. Zhu found that anxiety sharpens both types of evaluations. This has made employees more sensitive to the potential for AI to help them stay relevant or be left behind. On the other hand, perceived ease of use reduces the intensity of both reactions, suggesting that systems that are familiar and easy to use may be perceived as less emotionally charged overall.
The broader point is simple but important. The same technology can arrive in very different ways depending on the context. Employees who feel supported, trained, and accepted may see AI as a way to learn faster and contribute more. Those who feel exposed or replaceable may read the same system as a warning.
Trust but not blind trust
Trust is near the center of the paper. Zhu argues that for employees to use AI meaningfully, they need to have enough trust in it, but not enough to stop questioning it.
This balance is important because generative AI can still produce weak inferences, misinformation, biased output, or overconfident nonsense. Employees who trust it too much may accept bad answers. Employees who don’t fully trust it may miss out on beneficial possibilities.
The same balancing act appears at the organizational level. Zhu’s research argues that successful implementation is less about purchasing sophisticated systems and more about aligning them with your actual goals, workflows, and governance. This paper proposes a framework for moving from experimentation to more integrated use, emphasizing design, rapid testing, user focus, collaboration, and ethical oversight.
“Organizations need to follow a strategic roadmap to align technology with their goals and build an ecosystem with industry and academic partners,” Zhu said. “My research proposes an eight-step framework for organizations to move from experimentation to a more integrated and purposeful use of GenAI.”
This paper positions AI as part of a larger industrial shift. As more workplaces become AI-native, the technology will stop appearing as a separate add-on and begin to become part of the regular process.
“We are in a new industrial revolution,” Zhu said. “While some jobs will disappear, new forms of work and entirely new industries will also emerge, centered around AI infrastructure, data centers and digital services. Rather than fearing technology, employees need to learn how to use it critically and develop their skills alongside it.”
A career shaped by adaptability
The final part of the paper explores career permanence beyond today’s workflows. Using a subsample of 361 expatriate professionals, Zhu investigated whether working with generative AI impacts career persistence. It was so, albeit indirectly.
That mechanism was career adaptability, a set of resources that help people adapt to change. In Zhu’s model, this includes concern for the future, control over one’s path, curiosity about options, and confidence in problem-solving. Generative AI collaboration is associated with stronger adaptability across all four dimensions, which in turn supports career sustainability.
Trust was important here too. Increased trust in AI has strengthened the positive link between AI collaboration and adaptability. Job insecurity also increases the importance of adaptability, suggesting that an uncertain environment increases rather than devalues these internal resources.
This finding counters the narrow view that AI is only a threat to jobs. Zhu’s research suggests that this technology can also act as a force for development, especially when used by workers to explore, learn, and reimagine their roles in a changing labor market.
Practical implications of the research
This paper offers a clear lesson for employers that deploying generative AI is more than just a technology project. It’s a management and design issue. Organizations that want better outcomes need to connect the use of AI to real-world work goals, explain its limitations, protect privacy, build ethical safeguards, and give employees the space to learn the technology rather than feel judged by it.
For workers, this message is not one of blind optimism. Critical use is important. Zhu’s findings suggest that when employees view AI as a tool that can expand their effectiveness, engagement increases, and that using AI to enhance adaptability rather than outsourcing decisions increases career resilience.
As workplaces move rapidly toward AI integration, the most lasting benefit may be informed collaboration, not complete trust or complete resistance.
