The most valuable asset in most organisations is also the most mobile and the least measured. Human capital — the skills, knowledge, and capabilities embedded in your people — determines competitive advantage, innovation capacity, and organisational resilience. Yet most organisations have no rigorous framework for measuring it.
This gap has become acute in the AI era. AI is changing what human capital is worth. Some skills are being augmented by AI tools — knowledge workers with strong analytical capability plus access to AI are far more productive than either alone. Some skills are being displaced — routine analytical work, document processing, basic coding can increasingly be handled by AI systems. And entirely new skills are being created — prompt engineering, model evaluation, human-AI collaboration design did not exist three years ago.
The organisations that will thrive in this transition are those that can measure human capital reliably enough to understand what is happening to their workforce, where gaps are emerging, and how to allocate training and hiring budgets effectively. This requires moving beyond intuition and anecdote to systematic measurement frameworks.
The OECD defines human capital as "the knowledge, skills, competencies and other attributes embodied in individuals that are relevant to economic activity." This formal definition is useful because it breaks human capital into measurable components:
Traditional human capital measurement focused on the first two categories: education level, tenure, and role. The OECD methodology acknowledges that all four matter.
The SNA 2025 (System of National Accounts) revision, adopted by the ONS and statistical agencies globally, represents a watershed. For the first time, national accounting frameworks are attempting to measure human capital as capital formation rather than as flow expenditure. This is not yet fully operationalised, but it signals that human capital measurement is moving from optional to essential.
Human capital is the most valuable asset in knowledge-intensive organisations, yet it is the least rigorously measured. The OECD framework provides practical structure: education and experience (easy to measure), skills and competencies (harder but necessary), and engagement and motivation (hardest but most predictive of outcomes).
For practical measurement in organisations, I recommend breaking human capital into four components:
This is the most straightforward to measure and has been the historical focus:
These remain valuable predictors of capability, but they are increasingly insufficient on their own. A person with 10 years of experience in traditional financial modelling may be less valuable than a person with 3 years of experience who is fluent in AI-augmented tools and can evaluate model outputs reliably.
This is where the measurement becomes more complex and more urgent in the AI era.
For knowledge workers, relevant technical skills include:
The challenge is that AI literacy cannot be measured by looking at a resume. A person who has been in their role for 20 years may be brilliant at their core technical work but have zero practical AI literacy. A person with 2 years' experience may have high AI literacy because they learned it immediately upon entering the role.
| Skill Category | Measurement Approach | Data Source |
|---|---|---|
| Core domain skills | Assessment against role requirements; peer evaluation; work output quality | Work samples, peer review, customer feedback |
| Tool fluency | Tool usage logs; task completion speed in tool; support ticket volume | Tool usage data, task metrics |
| Data literacy | Ability to formulate analysis questions; interpret statistical results; identify data quality issues | Work samples, project outcomes, assessment exercises |
| AI literacy | Self-assessment + practical evaluation; ability to assess model outputs; understanding of failure modes | Assessment framework, practical exercises |
This is the intangible knowledge that sits at the intersection of the person and the organisation:
This type of capital is difficult to measure but extraordinarily valuable. A mid-level manager with five years of institutional knowledge can be worth significantly more than a newly hired external hire with superior credentials because they understand the organisation's culture, decision-making patterns, and politics.
This is the hardest to measure but increasingly important as organisations navigate change:
During periods of significant organisational change — like the AI transition — engagement and learning orientation become critical. An organisation where people are motivated to learn new skills will transition through AI adoption faster than an organisation where people are defensive about change.
One of the most significant implications of AI for human capital is the "hourglass" effect: the compression of mid-tier occupations while senior and junior roles expand.
Here is what is happening:
Mid-tier roles (junior to mid-level accountants, junior to mid-level lawyers, junior to mid-level analysts) are being compressed because these roles are primarily routine analytical work. A junior analyst spending eight hours analysing financial documents can be replaced by an AI system in five minutes. The value of that human time has been dramatically reduced.
Senior roles are expanding because the work that remains is judgment-intensive. A senior strategist who can assess the outputs of AI systems, challenge their reasoning, and apply contextual business judgment is more valuable than ever. AI augments their work by handling the routine analysis, freeing them to focus on judgment and decision-making.
Junior roles are evolving but remain. The entry point to a profession changes. Rather than hiring junior analysts to do routine work (because AI handles it better and cheaper), organisations are hiring junior AI-augmented professionals who can work effectively with AI tools from day one.
This creates a measurement challenge: if you measure human capital by the traditional metrics (education, experience, role level), you will be systematically misvaluing your workforce during the transition.
Organisations are laying off mid-tier workers while hiring for new roles that did not exist 18 months ago. The challenge is that mid-tier workers often lack the AI literacy and technical skills required for new roles, creating a genuine skills mismatch. This is not about laziness or unwillingness to learn — it is about the rate of change outpacing the capacity of traditional training programmes.
For organisations, here is how to systematically measure human capital in the AI era:
Conduct a comprehensive assessment across your workforce:
Education and experience audit:
AI literacy assessment:
This generates a baseline. Perhaps 20% of your workforce has high AI literacy, 40% has moderate literacy, and 40% has minimal literacy.
Skills gap analysis:
Engagement and adaptability assessment:
Track changes over time:
Individual level:
Team/functional level:
Organisational level:
Use the measurement to inform:
Hiring: If you are short of AI literacy, weight AI literacy heavily in hiring decisions. If you have low engagement, invest in leadership and culture before hiring more people.
Training: If you have a 40% AI literacy gap, you need a training programme that gets 25-30% of people to AI fluency within 18 months. This is not optional onboarding — this is strategic capital investment.
Retention: Identify your highest-capability people (those with strong technical skills, high AI literacy, and high engagement) and ensure they are being developed and rewarded appropriately. The cost of losing one of these people is very high.
Role redesign: Use the hourglass insight to think deliberately about role redesign. How are junior roles changing as AI takes on routine work? Are you offering junior people development paths that are attractive despite the loss of routine work?
If your organisation is preparing for PE exit or M&A, human capital measurement becomes essential. PE buyers increasingly assess:
A business that can demonstrate that it has high-capability, engaged, AI-literate people is more valuable than a business with equivalent financials but a team that is less capable or less engaged.
| Measurement Dimension | Weak | Strong |
|---|---|---|
| AI literacy distribution | < 20% of team has high AI literacy | > 60% of team has functional AI literacy |
| Skills future-readiness | Many people have skills that are at risk of automation | Most people have skills that are complementary to AI |
| Engagement level | Survey shows 30-40% engagement score | Survey shows 70%+ engagement score |
| Human capital retention | High capability people are leaving; 20%+ annual attrition of senior people | Low attrition of senior people; evidence of internal development |
The single most important intangible asset in your organisation is the collective capability, knowledge, and engagement of your people. Yet most organisations have no systematic way to measure it.
Human capital is not a cost item to be minimised. It is capital to be measured, managed, and developed for return. Organisations that measure human capital systematically will identify capability gaps, allocate training budget effectively, and retain their most valuable people.
The AI transition will differentiate organisations sharply. Those that measure human capital, identify gaps, and invest systematically in development will emerge with stronger teams and higher productivity. Those that treat human capital as overhead will find themselves with outdated capability in an increasingly technology-intensive environment.
The measurement frameworks exist. The OECD methodology is available. The challenge is not conceptual — it is execution. The organisations that measure human capital effectively will also outcompete those that do not.
David Stroll is Co-Founder and Chief Scientist at Opagio, specialising in productivity measurement frameworks and the economics of intangible capital. His work draws on SNA 2025, OECD, and ONS methodologies. He has published research on intangible asset data collection with ESCoE and the ONS, and holds a PhD in economics.
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