By Ciara Harrington
Ciara Harrington explores the lack of confidence within UK organisations in the return on investment generated by learning initiatives and how they can rebuild trust.
According to the British Chambers of Commerce, nearly a quarter of UK firms have reduced staff training, while more than half have kept workforce development investments flat. This is occurring despite persistent recruitment difficulties and widespread skills shortages across sectors. The pattern suggests more than short-term cost pressure; it reflects a deeper lack of confidence in the returns generated by learning investments.
Until organisations can trust and demonstrate that learning initiatives reliably build the skills required to execute strategy, enable AI adoption, and adapt as priorities shift, training budgets will remain constrained. Rebuilding that trust depends on improving the quality of skills data and clearly demonstrating how learning investments strengthen workforce efficiency, agility and AI readiness. Boards and senior leaders need visible, actionable evidence that learning translates into job-relevant skills that evolve in step with the business.
More access doesn’t equal more trust
Historically, technology and governance constraints limited access to high-quality learning. Today, the opposite is true. Businesses have greater access to learning content, and many have invested heavily in content marketplaces to expand that access. Yet boards and senior leaders still do not prioritise learning initiatives within enterprise budgets, largely because they lack the data and metrics to have the confidence that the content employees consume translates into improved performance. When that assurance is missing, confidence in the value of learning erodes.
This mistrust is intensified by the rapid pace of AI adoption. As AI tools become embedded in day-to-day workflows, leaders need clear visibility into which teams have the skills to adopt them safely and productively, where critical gaps exist, and how quickly those gaps can be closed. Without organisation-wide confidence that learning produces measurable, job-relevant skills, investments will continue to be deprioritised in favour of short-term fixes. For businesses scaling AI across their operations, this is becoming an acute risk, and it underscores the need to understand what is driving the breakdown in trust in skills and learning data.
Roots of the skills confidence gap
A primary driver of executives’ lack of confidence in learning investments is the disconnect between how learning is supplied and how its value is demonstrated. Broad content marketplaces optimise for access and scale, offering large volumes of third-party material, but they are not designed to help enterprises create, govern or operationalise their own institutional knowledge. As a result, internal teams are left to shoulder the burden of curation, validation and pathway design, while leaders lack clear visibility into skills gaps or the business impact learning initiatives could deliver.
This challenge is compounded by inconsistent skills data. Unverified and unpredictable content weakens AI-driven recommendations, simulations, and skill assessments, raising governance and trust concerns as leaders struggle to rely on data that is incomplete or unreliable. In an AI-enabled operating model, this becomes an executive-level risk rather than a learning problem. When data cannot be trusted to support better decisions or deliver measurable outcomes, executives naturally hesitate to invest.
Restoring trust in learning investments
To address declining confidence in skills and learning, strong governance and trust in skills data are essential. The shift from prioritising content volume to building confidence in capability is already underway, but it requires a fundamental reset in how leaders think about learning and execution. Learning must be trusted and skills data must be reliable enough to inform staffing, redeployment, and workforce planning decisions.
Business leaders should begin by assessing the quality of their skills data and clarifying the needs of their workforce. Employees benefit from learning that is relevant, targeted, and clearly linked to career progression, which means organisations must strengthen alignment between skills, technology and business priorities.
Leaders must also manage skills as a supply chain. In a skills supply chain model, skills are treated like any other critical business input: measured, governed and aligned to execution. This approach tightly links skills to compliance, safety and AI-enabled work, giving leaders a clear, evidence-based view of both current and future workforce capabilities across humans and AI systems. By accurately assessing existing skills and emerging requirements, organisations can set a more credible roadmap for which capabilities to prioritise.
Rooted in reliable skills data, skills supply chains optimise for outcomes, confidence and accountability. Employees receive more targeted development that accelerates career progression, while leadership gains greater confidence in staffing, redeployment, and succession decisions. With a trusted view of skills in place, organisations can apply AI-enabled decision-making with greater assurance and manage workforce-related risk more effectively.
Solving the skills confidence crisis
The UK faces a skills confidence problem, but it also has the tools to restore trust. Boards invest when they can clearly see the connection between learning and business outcomes. Moving forward requires a stronger understanding of skills data, clarity on which roles matter most, and the ability to make workforce learning actionable in everyday decision-making.
Through a skills supply chain, learning shifts from being viewed as a cost to becoming targeted and specialised for specific workforces and outcomes. In doing so, learning becomes a core driver of efficiency, agility, and readiness for the AI era.
Ciara Harrington
