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These days, gen Z appears to be pivoting towards skilled trades, perhaps driven by a desire for “AI-proof” job security. Many young workers now view blue–collar careers as more stable than office jobs in the face of rapid change.

It’s not just the youngest workers. A growing sense of unease about AI is reshaping how many people think about work. Within younger groups, this shift is showing up in hard numbers. In the UK, hiring of gen Z workers (those born in or after 1997) in construction and trade roles rose by 16.8% in the year to January 2026. The result is what some are calling the “toolbelt generation”.

But elsewhere in the workforce, many professionals are taking a pragmatic approach. Instead of competing with automation, they are learning how to work alongside it. Building fluency with AI tools is increasingly seen as a form of career insurance.

The goal is to move into roles designing, managing or directing AI systems. In that model, technology becomes a force multiplier (that is, it increases productivity) , rather than a threat.

This shift is also driven by economics. AI-related skills command a clear premium in the jobs market. Beyond pay, there are other benefits. AI systems are particularly effective at handling repetitive, process–heavy tasks. When those functions are automated, employees can redirect their energy towards strategy, creative problem–solving and higher–value decision–making.

Many find that this shift not only improves productivity but also makes their work more engaging and meaningful.

Importantly, entering the AI space does not always require a computer science degree. Through online learning, bootcamps or just practical experimentation, workers can gain expertise in areas such as prompt engineering, workflow automation or AI application. The barrier to entry is lower than many assume, especially for those who already understand a specific industry.

Industry knowledge is, in fact, a major advantage. Organisations increasingly want people who can bridge domain expertise with technical capability. A healthcare professional who knows what patients need as well as understanding AI tools; a finance specialist who can apply machine learning to risk analysis; or a tradesperson who uses smart systems for efficiency can all bring unique value.

These hybrid profiles are becoming central to how companies integrate AI, creating interdisciplinary roles that did not exist a few years ago.

The flip side: risks and challenges

AI is creating opportunity, but it also brings risks and trade–offs. One of the most immediate challenges is the pace of change. Keeping skills current can feel like trying to hit a moving target. Over time, constantly doing more can lead to fatigue and burnout, particularly in highly competitive environments where staying relevant is tied to job security.

There is also an upfront cost. Transitioning into AI, especially into more technical or advanced positions, can require an investment of time and money before any financial return materialises.

And AI is said to be contributing to a hollowing out of traditional career ladders. Many entry–level roles, once considered stepping stones into industries such as finance or marketing are being automated or cut back. As a result, entry pathways into certain professions may narrow before new ones are established.

Finally, working in AI often means grappling with complex ethical and safety questions. Workers must consider issues such as data bias, privacy, transparency and accountability. Decisions made during system design and deployment can have wide–reaching consequences. Navigating these responsibilities requires sound judgement and a clear understanding of these consequences.

Looking ahead

In many sectors, AI is unlikely to eliminate entire professions. Instead, it will reshape them. Tasks will be automated, workflows will evolve and job descriptions will shift. For most professionals, the practical response is not to abandon their field, but to integrate AI into it.

At the same time, technical fluency alone will not be enough. As automation takes over routine and rules–based work, human skills become more important. Critical thinking, judgement, empathy, communication and complex problem–solving remain difficult to replicate with algorithms. The more advanced the technology becomes, the more valuable distinctly human strengths appear to be.

There is also a widening gap across industries. AI is generating new, high–paying roles in areas such as engineering, data science and AI strategy. However, in positions where automation only partially replaces tasks, productivity may increase while wages do not. In some cases, partial automation can stifle pay or reduce opportunities for promotion.

Retraining and career pivoting in the AI age is becoming a mainstream response to structural change. AI is reshaping how work is done across sectors, while opening up new roles that are centred on oversight, integration, strategy and innovation. For many professionals, the question is not whether change is coming but how proactively they choose to respond.

The most resilient path forward is rarely about abandoning your field entirely. More often, it involves layering AI fluency on top of existing expertise. A finance professional who understands automation tools, for example, is better positioned than someone relying on legacy skills alone. In this sense, the objective of retraining is to move closer to the decision–making layer of work.

Ultimately, the AI era is not about a binary choice between optimism and fear. It is about positioning. Retraining and career pivoting are becoming central strategies for navigating this shift with intention rather than reacting after the fact.

The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.