From Skills Intelligence to Career Action: What England Can Learn from Singapore

How do you turn better understanding of the skills an economy needs into better decisions by people, employers, and training providers?

This question is important in England as policy shifts from simply expanding skills provision towards building a more demand-led and joined-up system. Skills England is strengthening national and sectoral intelligence; Local Skills Improvement Plans are intended to align provision more closely with local employer demand; and the new UK Standard Skills Classification provides a common language for skills and occupations. Careers, training and employment policies are increasingly being brought together to improve how people move between education, work and further learning. The challenge is less whether England has the individual ingredients and more how effectively they work together. How does intelligence about what the economy needs reach the people making decisions about study, training, recruitment and investment? And how do we know whether these connections are improving outcomes?

Singapore provides a useful lens through which to explore those questions. It has spent more than a decade connecting skills intelligence with career development, training, funding and employment. Its experience does not provide a finished model for England to copy, and the evidence is not yet strong enough to conclude that its approach has delivered all its intended outcomes. As England considers how to connect its own system, Singapore highlights an important question: what happens after skills intelligence has been produced? Its experience offers lessons about how different parts of a skills and careers system can be connected, and how intelligence can be translated into decisions, action and, ultimately, better outcomes.

From Intelligence to a Shared Language

A clear learning from Singapore is that skills intelligence is more valuable when it becomes a shared reference point for decisions across the system.

Singapore has a substantial skills intelligence infrastructure. Its sector-based Skills Frameworks bring together information about industries, occupations, job roles, career pathways, skills and training, providing a common reference point for individuals, employers, training providers and government. Its Jobs-Skills infrastructure increasingly examines change at task as well as occupation level, including recent work exploring the potential impact of AI across thousands of work tasks. The ambition is therefore not simply to describe labour-market change, but to create information that different actors can use to respond.

England is developing infrastructure with comparable ambition through Skills England, the Standard Skills Classification, Skills Needs Assessments, UK Skills Explorer, LSIPs and the Local Skills Dashboard. Its 2026 work is beginning to make the next connection too, mapping pathways from courses to occupations and identifying where provision needs to change in response to skills demand.

England does not simply have an information deficit. The policy question is how effectively intelligence travels through the system and informs decisions by individuals, employers, providers and policymakers. This is where Singapore’s use of common frameworks is particularly relevant. Its Skills Frameworks are intended as a shared reference point for students, parents, teachers, career guides, employees, employers, education and training providers and government.

England’s Standard Skills Classification has some of the same potential. By linking skills, knowledge and tasks to occupations, it can provide a more consistent language across employers, educators, careers professionals and policymakers. But a common language only creates value if it can be translated into choices.

A skills taxonomy can describe the economy and still mean very little to a sixteen-year-old deciding between an apprenticeship and college, an employer redesigning a role, or a training provider deciding what to develop next. For employers, it might inform recruitment and workforce development; for providers, what provision to develop; and for careers professionals, how to support exploration of occupations and pathways.

Career Development as Infrastructure

Singapore’s Education and Career Guidance approach is deliberately developmental: career awareness begins during primary education, exploration becomes more prominent through secondary education, and planning develops through post-secondary choices. MySkillsFuture allows young people to explore education and career pathways, industries, occupations, interests and strengths, while specialist Education and Career Guidance (ECG) counsellors provide more individual support.

England already has substantial infrastructure through the Gatsby Benchmarks, statutory careers guidance, Careers Hubs, careers leaders, employer engagement and the National Careers Service. Gatsby Benchmark 2 also recognises that learners need access to good-quality, current careers and labour-market information (LMI), alongside support to use it effectively.

There is an important distinction between LMI provision and LMI capability. LMI provision asks whether accurate information is available; LMI capability asks whether people can find, interpret and apply it to their circumstances, explore alternatives, make informed decisions and act on them. These require different forms of support. In April to June 2026, an estimated 981,000 young people aged 16 to 24 across the UK were not in education, employment or training. Better labour-market intelligence alone does not change these outcomes. The challenge is whether people can use information about opportunities, pathways and employer demand to make meaningful choices.

A young person may learn that clean-energy occupations are expected to grow. Career development makes that information actionable: which occupations are involved, what do they entail, what routes lead into them, are opportunities available locally, and do they fit the individual? Employer encounters and workplace experiences can make these possibilities more tangible.

The same principle applies to adults facing changes in their occupations, including through AI. Knowing that tasks are likely to change does not, by itself, show what to do next. Singapore has sought to connect skills intelligence with career development so that people can navigate changing opportunities over time. For England, the consideration is how its existing careers infrastructure can work more closely with skills policy to help people respond to changing labour-market conditions, rather than operating alongside it as a separate system.

From Intelligence to Action

What happens between understanding an opportunity and acting on it?

Singapore’s SkillsFuture system attempts to make that connection explicit. SkillsFuture programmes combine training with career support and, in some cases, employment facilitation. The SkillsFuture Career Transition Programme, for example, combines industry-relevant training with employment facilitation and career advisory support. By July 2025, around 330 programmes were available across 21 sectors, with around 15,000 people completing programmes between June 2022 and June 2025; 51% were reported to have found a new role within six months. 

Financial support is another part of this architecture. SkillsFuture Credit gives Singapore citizens aged 25 and over an individual training credit, alongside subsidies and additional support for mid-career learners. By early 2026, more than half of eligible Singaporeans aged 30–75 had used their SkillsFuture Credit. 

England is moving in a similar direction. There are multiple routes through which people can act on career decisions. The Lifelong Learning Entitlement, due to begin in January 2027, is intended to provide eligible learners with greater flexibility to study modules and qualifications over time. The consideration for England is not to reproduce these mechanisms, but to make the journey from decision to action more coherent: accessing the right route and funding, developing skills, and entering or progressing in work. For individuals, these are not separate policy instruments but one journey from deciding what to do to being able to do it.

What Does the Evidence Tell Us and What Remains Uncertain?

There is evidence that Singapore’s system is being used and has expanded substantially. Participation in SkillsFuture-supported training increased from around 418,000 in 2016 to more than 600,000 in 2025, while Workforce Singapore’s employment support increased from around 127,000 people in 2017 to 355,000 in 2025. SkillsFuture Career Transition Programmes have also reported employment outcomes among participants. An earlier, narrower Ministry of Education measure found a 55% employment outcome among almost 4,300 previously unemployed participants tracked to December 2024. Singapore’s Ministry of Education itself notes that training cannot guarantee employment. The growth and completion figures tell us more about participation than about whether the wider system is working as intended. They do not by themselves demonstrate improvements in long-term progression, productivity or resilience.

The proposition is that better intelligence about changing skills demand can improve decisions by individuals, employers, providers and government: better intelligence → better understanding → better decisions → better provision and investment → better employment and progression outcomes.

The evidence is considerably stronger at some points in this chain than others. Singapore can demonstrate substantial investment in skills intelligence and infrastructure, increasing participation and examples of employment outcomes. It is harder to establish how far improvements in individual outcomes are attributable to the integration of skills intelligence, careers, training and employment support, rather than to individual programmes, wider labour-market conditions or other factors.

This is an important consideration for England. The question should not simply be how many people participated in training, but increasingly what happened afterwards: did they complete it, progress, change occupation or move into employment, and what can the system learn when they did not? The ambition should be to create a feedback loop: forecast → translate → advise → train → employ → measure → learn → update. This turns an information pipeline into a learning system, where evidence about what happens to people feeds back into future skills intelligence, advice and provision.

Even Singapore Is Still Working on the Joins

Singapore’s own system continues to evolve. In 2026, SkillsFuture Singapore and Workforce Singapore were brought together within a new Skills and Workforce Development Agency, a statutory board under the Ministry of Manpower, jointly overseen with the Ministry of Education. This does not demonstrate that the previous model failed. Rather, it shows that integration is an ongoing policy problem. Even where substantial infrastructure exists, policymakers still need to examine whether organisational boundaries, information flows and processes are helping or hindering the outcomes they want.

Singapore is therefore best understood not as a finished model for England to replicate, but as an evolving system from which England can learn. Its experience demonstrates what sustained investment in skills intelligence and institutional infrastructure can make possible, while also highlighting how difficult it is to establish whether greater integration ultimately produces better career and employment outcomes.

What Can England Learn: From Policy Borrowing to Policy Learning

International comparison needs to be used carefully. The practice of policy borrowing has long been criticised for treating policies as though they can simply be transferred from one national context to another. Policy learning offers a more useful approach. Raffe (2011) argues that international experience needs to be interpreted in context. Singapore’s institutions, governance arrangements, labour market, demographics and political priorities are different from England’s. The value of comparison lies not in identifying institutions to reproduce, but in examining how Singapore has approached problems England is also trying to solve. Three considerations stand out.

1. Make skills intelligence usable at the point of decision. The UK Standard Skills Classification provides important infrastructure, but its value depends on whether employers, educators, careers professionals and individuals can translate it into practical choices. National intelligence needs to connect occupations and skills with routes, location, cost and opportunity, alongside local labour-market knowledge.
2. Treat career development as infrastructure, not an optional extra. Information is only useful when people can interpret it and connect it to their circumstances. England already has substantial careers infrastructure; the consideration is how it can work more closely with skills policy to help individuals act on changing labour-market opportunities.
3. Create a feedback loop, not just an information pipeline. The chain should not end at participation in training. Systems should track what happens after training, including completion, employment, progression, occupational mobility and further learning. This evidence should inform future skills priorities, provision and support.

England has repeatedly restructured its skills architecture, moving from Local Enterprise Partnerships and Skills Advisory Panels to Local Skills Improvement Plans and Skills England. The priority should not be further institutional proliferation, but stronger integration across the structures already in place. The critical issue is how they connect: skills intelligence must translate from economic analysis into occupational demand, skills provision, education, training and careers, with outcome evidence feeding back. Singapore’s lesson is not to reproduce its institutional model, but to strengthen the coordination, information flows and feedback mechanisms that connect England’s existing system.

By Ladi Mohammed, Founder & CEO at Global Educational Travel and Tours (GETT) and Shani Wijemuni, Policy Officer at Global Educational Travel and Tours (GETT)