Preparing the Next Generation for Careers in the AI Era and Training for the Right Future
- 3 days ago
- 7 min read
Written by Rosie Hewat, Founder & CEO of Rosie’s People
Rosie Hewat is a Board and Executive Advisor and the Founder & CEO of Rosie’s People, a leadership and organisational advisory platform. She works with founders, boards, and senior leaders navigating complexity, scale, and high-stakes decision-making across global and regulated environments.
Governments are increasingly investing in getting young people into work, and rightly so. In the UK, recent reforms have focused on expanding apprenticeships, funding training pathways, and incentivising employers to bring younger workers into the labour market. Financial support for hiring, new apprenticeship routes, and targeted employment schemes all point to a clear intention: strengthen workforce participation and prepare the next generation for the future of work. This is a positive and necessary shift, but it raises a more complex question: What exactly are we preparing people for?
In my previous article, I explored why many AI productivity tools fall short of delivering real automation, despite being highly effective at generating output. The distinction between output and automation matters because it shapes how organisations make decisions about roles, productivity, and workforce structure. This article builds on that idea.
If we misunderstand what AI can actually replace, we risk making workforce decisions (and indeed, training investments) based on assumptions rather than reality.
The AI reality: Exposure is not the same as transformation
Recent research published by Anthropic in March 2026 highlights which jobs and tasks are most exposed to AI capabilities. The findings suggest that many white-collar roles, particularly in areas like administration, finance, and computing, have a high proportion of tasks that could theoretically be performed by large language models. But there is an important nuance.
The research also shows a clear gap between what AI can theoretically do and what is actually being used in real-world workflows. This distinction is critical. Exposure does not equal automation. Capability does not equal deployment. And deployment does not equal effective integration.
We are still in a phase where AI can assist, accelerate, and augment, but in many cases, it has not yet structurally replaced how work flows across organisations. And yet, decisions about jobs, hiring, and training are already being influenced by these projections.
The workforce is already shifting, and people are feeling it
Outside of research papers and policy announcements, the lived experience tells its own story. I know individuals who have built careers in areas such as customer service, marketing, and copywriting, roles that are already being reshaped by AI adoption, who have suddenly found themselves either made redundant or effectively locked out of the market when trying to transition into new roles. Many of them are highly capable, with years of experience, yet are now competing in a landscape that has shifted faster than expected.
The issue is not a lack of talent. It is a mismatch between how quickly roles are evolving and how slowly pathways are adapting.
At the same time, many graduates are struggling not only to secure roles aligned with their degrees but also to access the entry-level experience that would allow them to build momentum in the first place.
In that sense, the UK government’s investment in apprenticeships and early-career pathways, including significant funding aimed at expanding access to training and employment, is both timely and necessary. It creates an important bridge into the workforce.
But a deeper question sits beneath this: not just how we create opportunities, but whether those opportunities remain relevant.
We are still training for stability in an unstable system
Historically, apprenticeships were designed around clearly defined roles. Whether it was carpentry, electrical work, mechanical fitting, or other skilled trades, the pathway was relatively linear: learn the craft, build experience, and progress within a stable professional identity. Even as technology evolved, these roles adapted incrementally rather than fundamentally changing overnight.
Today, that stability no longer exists in the same way.
If you take roles that are now being reshaped, such as marketing or customer service, the core title may remain, but the required skill set has already shifted significantly. A marketer today is expected to understand data, automation tools, AI-assisted content generation, and multi-channel digital systems. A customer service professional is no longer only resolving queries; they are working alongside AI systems, managing escalations, and navigating increasingly complex interactions.
The title stays the same, but the role does not. And looking ahead, the picture becomes even less clear.
In areas like finance, for example, roles such as quantitative analysts, or ‘quants’, have long existed in specialised environments such as high-frequency trading. What is changing is their expansion into broader business contexts, where demand for data-driven decision-making is increasing faster than traditional training pathways can support. In effect, the capability is becoming mainstream, but the pathway to it is not. These roles sit at the intersection of mathematics, data science, and financial modelling and are becoming increasingly valuable.
We are not only dealing with roles that are changing. We are trying to prepare people for roles we do not yet fully understand. Meanwhile, individuals entering the workforce today are being asked to make decisions based on partial visibility, choosing degrees, apprenticeships, and career paths in a landscape where roles are shifting, entry points are becoming more competitive, and future opportunities are still being defined.
This is not about panic. It is about uncertainty.
Where traditional apprenticeships followed a linear path, many modern careers are becoming iterative, requiring continuous adjustment rather than predictable progression.
Investment is rising, but alignment is unclear
The UK government is not alone in trying to respond. Across the world, governments are increasing investment in skills and workforce readiness. In the United States, reskilling initiatives are expanding across digital and AI-related fields. The European Union continues to push forward its Skills Agenda, with a strong emphasis on lifelong learning. Singapore’s SkillsFuture programme remains one of the most advanced examples of continuous workforce development. The direction is clear.
Governments recognize that work is changing, and they are trying to respond. But recognition alone is not enough.
Are we aligning what we teach with how work is actually evolving?
Regulation, risk, and the changing conditions of work
Alongside technological change, regulation is also reshaping how organizations respond. In the UK, the Employment Rights Act 2025 introduces stronger worker protections, increases the cost of workforce restructuring, and raises the level of accountability placed on employers. These changes are necessary, but they also mean that organizations must approach change more carefully, at the same time as they are under pressure to adapt more quickly.
At the same time, global instability is reshaping how work is structured. Organizations are increasingly having to think about resilience, not just efficiency. Roles that can be performed remotely are often more adaptable in uncertain environments, while those tied to specific locations may face greater disruption.
Work is becoming less defined by location and more defined by adaptability. And that has direct implications for how we train.
This creates what could be described as a compliance paradox: organizations are under pressure to adopt AI-driven efficiencies, yet the regulatory cost of restructuring roles or making mistakes in that transition is increasing. The result is a widening gap between the speed of technological change and the pace at which organizations can safely adapt.
In practice, this creates a holding pattern where organizations hesitate, adapt cautiously, and the workforce is left navigating uncertainty with limited visibility.
But what should we actually be training for?
If the nature of work is evolving, then training cannot remain anchored to static definitions of roles. It must shift towards capability, including the ability to navigate ambiguity, exercise ethical judgment, and synthesize information across disciplines in ways that AI still cannot reliably replicate.
AI literacy is part of this, but not in isolation. It is not enough to use tools. Individuals must understand their limitations, question their outputs, and apply them within broader systems.
More importantly, the value of human judgment is increasing. As AI becomes more embedded in workflows, the ability to interpret, assess, and make decisions in uncertain or complex situations becomes more critical, not less.
From a workforce strategy perspective, this is where many organizations are already struggling, not with access to technology, but with aligning it to real-world structures. In our work at Rosie’s People, we see this consistently. Organizations are not struggling because they lack access to AI tools. They are struggling because they lack clarity on how those tools should be applied within their workforce.
This is why we focus on alignment over mere tool access at Rosie’s People. The challenge is not the technology itself, but the lack of clarity around how it integrates into human structures.
Preparing individuals for the future of work is not about predicting specific roles. It is about equipping them to operate effectively in environments defined by change.
Conclusion
The question is not whether we should invest in the next generation. We absolutely should. The question is whether we are aligning that investment with the reality of how work is evolving. Are we preparing the next generation for the jobs we understand, or the world they will actually inherit?
This article draws on a combination of industry research, policy developments, and real-world workforce observations.
Read more from Rosie Hewat
Rosie Hewat, Founder & CEO of Rosie’s People
Rosie Hewat is a Board and Executive Advisor and Founder & CEO of Rosie’s People, a leadership and organisational advisory platform. A former Group Chief People Officer and Non-Executive Director, she has supported leadership teams and boards operating in high-growth and regulated environments. Rosie is also a trustee and an Executive Contributor to Brainz Magazine, where she writes on leadership, governance, power, and organisational risk.
References:
Anthropic (March 2026), Labor Market Impacts of AI
UK Government, Apprenticeship & Skills Reform Announcements (2025–2026)
UK Employment Rights Act 2025 Overview
McKinsey & Company, The Future of Work in the Age of AI
Gallup, State of the Global Workplace
