Our skills system is entering a period of profound transformation, shaped by rapid advances in artificial intelligence (AI), automation, and wider technological change. Through our FE News live series, Learning for a changing world, we’ve been bringing together sector voices to explore how education and skills provision must evolve to support people to thrive in this shifting landscape.
In our third episode, AI, automation and the skills reset, we explored how AI is reshaping work and redefining the workplace skills learners need. I was joined by guest speakers Professor Deirdre Hughes, a global careers advice and guidance expert whose work includes the OECD, and Kieron White, Co-Founder & CEO at Leading AI, for a timely discussion on what this means for learners, employers and the system as a whole.
While the pace and scale of change can feel uncertain, the conversation highlighted three key priorities that stand out when thinking about how we respond effectively.
1. Preparing for uncertainty means prioritising adaptability, not prediction
One of the clearest themes from our discussion was that while AI is already influencing every sector, the long-term picture remains uncertain. We are seeing change across roles and industries, but often at the level of tasks rather than whole jobs.
What we do know is that AI is accelerating transformation in how work gets done. Organisations are already exploring how AI can improve productivity, automate routine tasks, and create new roles in areas such as AI strategy, governance, and implementation. At the same time, there are growing concerns around reduced entry-level opportunities, as AI begins to take on more foundational tasks.
What we don’t know is exactly how this will evolve. As we noted in the discussion, we are heading into a period of exponential change, and that means being ready to be surprised. Trying to predict specific jobs of the future is increasingly difficult – and perhaps less useful than focusing on the capabilities that will enable people to adapt.
Essential skills such as curiosity, problem solving, critical thinking, and adaptability came through strongly in our conversation. These are not new, but their importance is growing in an AI-driven world where individuals will need to continually reskill and navigate change.
These skills are also complex and take time to build, so they cannot be treated as an afterthought. To prepare learners effectively, they must be meaningfully embedded into qualifications and given space to develop.
This will also support learners to become confident adopters of appropriate AI by building confidence through practical skills that can be applied across different roles and sectors.
2. Human and technical skills must be developed in combination, not in isolation
A second key takeaway was the need to move beyond the idea of a simple divide between “human” and “technical” skills. This is increasingly a false binary, as the future of work depends on how these skill sets complement each other.
AI is highly effective at scale, consistency, and processing information, but it relies on the likes of human judgement, context, and oversight. One of the most critical emerging workplace skills is the ability to evaluate AI outputs – to question, interpret, and decide whether they are accurate and appropriate.
This requires a combination of technical familiarity with AI tools and strong human capabilities such as ethical reasoning, critical thinking, and accountability. It also introduces new ways of working, where individuals may “manage” multiple AI systems or agents alongside human colleagues.
From a system perspective, this has clear implications. There is a strong case for embedding AI capability across qualifications, ensuring learners have a baseline understanding of how to use and interact with AI responsibly. Frameworks such as AI competency models can help define what this looks like in practice.
But just as importantly, we need to focus on how these skills are applied together. For example:
- using AI tools while maintaining critical oversight
- collaborating effectively with automated systems
- providing clear instructions and reducing ambiguity when working with AI
- understanding the ethical and governance implications of AI use.
At NCFE, we’ve seen first-hand the value of experimentation in building these capabilities. Giving colleagues time to explore AI tools, test ideas and iterate – as we did through internal “agentathons” – proved just as important as formal training.
This balance between technical confidence and human judgement will be central to future success.
3. The skills system must become faster, more flexible and more connected
The final priority is the need for system-level change. Throughout the discussion, there was clear consensus that the current skills and qualifications system is not yet keeping pace with technological change.
Part of this is structural, as qualification development is necessarily rigorous and takes time – but this can create challenges in rapidly evolving contexts like AI. There is a risk that by the time new content is embedded, it is already outdated.
There are also broader issues around how we define and assess skills. Traditional, static qualifications sit increasingly uneasily alongside a labour market that is constantly evolving. As Professor Hughes noted, we need to move towards models that capture applied, evolving competence rather than one-off achievement.
Careers guidance also emerged as a critical connecting thread. In a more complex and uncertain labour market, high-quality careers advice – supported by both AI and human expertise – is essential in helping individuals navigate pathways and make informed decisions. Without it, there is a risk that inequalities widen, particularly where access to high-quality support varies.
To respond effectively, several shifts are needed, including:
- embedding AI across education and training, rather than treating it as a niche specialism
- improving the pace and agility of qualification development, potentially through more modular or adaptable approaches
- strengthening careers guidance as a lifelong entitlement, supporting individuals at all stages
- encouraging experimentation and collaboration between educators, employers and policymakers.
There is also an opportunity to rethink assessment itself. Building AI collaboration into assessment could help us better reflect real-world practice, while also providing a clearer understanding of what effective use of AI looks like.
Looking to the future
AI and automation are already reshaping workforce skills in the present, not a distant future, and demand a proactive response. To keep pace, we need to prioritise adaptability, combine human and technical skills, and build a more agile, connected system.
This means investing in relevant skills, strengthening careers guidance, and ensuring flexibility, while maintaining trust, equity and human oversight. Ultimately, success depends on preparing people to adapt and thrive in a continuously evolving, AI‑enabled world.
To support this, we are launching two new workplace AI qualifications. Learners taking the L2 Award or Certificate in Artificial Intelligence for the Workplace will develop practical AI literacy for use within the workplace, ethical awareness around the use and governance of AI, and will build confidence and employability skills in the use of AI.
In addition, the qualification units will be available as standalone options, providing flexible opportunities for CPD and upskilling for individuals and employers looking to build a foundational understanding of AI in the workplace without committing to a full qualification.
You can find out more about our new Level 2 AI in the Workplace qualifications here.
