Glass Half Full: AI is the beginning for job searchers

By the time I was 22, I had three unfinished degrees and had tried my hand at 16 different jobs. It felt like a losing game - companies wanted five years of experience for an entry-level role. Today, that reality has only intensified. 

The skills gap is widening and, with the rapid development of technologies like AI, the workforce landscape is shifting beneath our feet. Some research indicates that by 2030, 39% of the global workforce will need reskilling. This means the goalposts aren't just shifting, they're moving further away just as we begin to run toward them. While this might sound bleak I believe that, if done right, AI is a solution.

We are at a nexus in time, with the transformative power of AI changing the way we live, work and learn. 

For job seekers, AI has the ability to lay out new opportunities. Like many young people, I did not lack motivation, rather, I lacked visibility into what I could pursue with my unique skills. That's where AI comes in. It translates scattered interests into tangible pathways, mapping how a philosophy student could enter product design, policy, or technology without abandoning their foundations. Instead of treating careers as rigid ladders, AI makes them legible as networks, showing the adjacent skills, short courses, and entry points that turn potential into employment. For a generation told to specialise early or risk falling behind, AI offers permission to explore without penalty, and guidance without gatekeeping.

As a young woman in tech, I have witnessed the potential AI has in dismantling one of the industry's oldest barriers, the myth of the "technical insider." Historically, tech has rewarded those with early exposure, often men encouraged into computing from childhood, while women have been made to feel like late arrivals in a conversation that began without them. AI lowers barriers, transforming how technical skills are acquired. AI can turn intimidation into accessibility. 

I do not specialise in one narrow technical field. I am what's commonly described as a generalist. For a long time, that felt like a liability. In rooms full of engineers, policy experts, or marketers, I often felt completely out of place, because I didn't have a single domain or technical skill I could claim.

What I've come to know, however, is that the future of work increasingly rewards those who can connect dots rather than sit inside one. AI has amplified that ability for me. It's allowed me to move faster across disciplines, pressure-test ideas, interrogate unfamiliar concepts, and build working knowledge at an exponential pace that would have been impossible five years ago.

Whilst at times, I still wrestle with the insecurity of not having one clear technical label, AI has shown me that depth and breadth are not opposites. When used well, it enables us generalists to build depth quickly, and then translate between worlds in ways specialists often cannot.

AI represents a second chance in an economy that rarely offers one. Historically, reskilling requires time, money, and institutional access, luxuries not equally distributed. Women approaching retirement age are disproportionately likely to have experienced career interruptions and are more likely to work in roles vulnerable to automation. But AI allows someone to build digital literacy at their own pace, without the financial barriers that 52% of mature-age students cite as a barrier. AI has the potential not just to diversify who enters tech, but also who stays, who leads, and whose perspectives shape the systems we all rely on.

Yet access to AI is not evenly distributed, and without intervention, its benefits risk reinforcing the very inequalities it has the potential to solve. And this is something I see played out in my own life with my Mum. She's a technically skilled dressmaker and has spent her whole life mastering her craft. The opportunities AI could unlock for her, from digital patternmaking to launching an online business or learning new industry software, are enormous. But potential alone is not empowerment. 

Where is the structured support and accessible training? Or the practical guidance that bridges craft to digital capability? Instead of feeling empowered by possibility, she feels blocked by the invisible barrier of "not knowing where to start," and this is the critical risk. Without intentional infrastructure, AI will reward those already fluent in its language and leave behind those with deep skill but limited digital access.

There is a deeper, systemic risk, that AI itself reflects the biases of the industries that built it. If women are not active participants in adopting and shaping AI, we risk becoming passive recipients of systems that do not account for their realities. Access, therefore, is not just about tools; it is about inclusion, representation, and intentional design.

This is why the question is not whether AI will change the future of work, but whether women will be empowered to shape it. Every major technological shift has created both displacement and opportunity, yet women have too often been concentrated in the roles most vulnerable to disruption, while underrepresented in creating the technologies that define what comes next. The difference has never been the technology itself, but the systems built around it.

Policymakers and tech leaders must ensure AI is deployed as a tool for access, not exclusion, embedding equitable training, mentorship, and pathways into its adoption. 

Government and industry have a rare opportunity to correct historical imbalances, enabling women to not only participate in the digital economy, but lead it. 

If we treat AI not as a shortcut for efficiency, but as infrastructure for capability, it will not narrow opportunities for job seekers. It will expand it.