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AI is emerging as a contributor to productivity growth, though the timing and distribution of gains remain uncertain. Organisation for Economic Co-operation and Development (OECD) modelling suggests AI could add around 0.4–1.3 percentage points to annual labour productivity growth over the next decade in high-exposure economies. Early signals in the United States are consistent with that upside, where AI adopting sectors such as finance and professional services have been outperforming on productivity.
“The macro question is not just how big the uplift is, but who captures it.”
Diffusion is uneven across countries. Anthropic’s analysis of Claude usage finds high income economies are over represented relative to their working age population, implying a risk that AI reinforces global income gaps if the benefits concentrate where skills, capital, data and compute already sit.
A constraint that is moving quickly from technical to macro-relevant is energy. The International Energy Agency expects global electricity demand from data centres to more than double by 2030 in its base case. Power prices, grid capacity and approvals are therefore becoming part of the AI adoption function, shaping where activity clusters and how fast it scales.
This matters for markets as well as growth. Public sentiment remains cautious across many countries, and the OECD has warned that lower than expected returns on AI investment could trigger broader repricing, tightening financial conditions, weakening private demand, and raising financial stability risks. The practical implication is that strategic decisions need to be robust to multiple paths, from rapid productivity acceleration to slower diffusion, or a period of market adjustment if expectations run ahead of realised gains.
“Demographics are pulling global labour markets in opposite directions.”
In developing economies, the World Bank estimates about 1.2 billion young people will reach working age between 2025 and 2035. Whether this becomes a dividend or persistent underemployment, will depend on job creation capacity and the ability to connect to global supply chains.
In OECD countries, demographic ageing is tightening labour supply and raising the urgency for productivity gains. The OECD reports the old age dependency ratio has risen from 19 per cent in 1980 to 31 per cent in 2023 and is projected to reach 52 per cent by 2060, implying a rising fiscal burden and structurally tighter labour markets unless productivity growth compensates.
The consequence is rising pressure for labour to move globally. Migration may intensify (often of skilled workers), easing bottlenecks in advanced economies but risking ‘brain drain’ elsewhere. A better outcome for developing economies is deeper supply chain integration that creates employment locally while connecting to advanced economy demand, turning demographic momentum into income growth rather than instability. That task becomes more urgent at a time when AI may already be complicating entry level opportunities.
We continue to experience global economic uncertainty driven by geopolitical challenges. In this environment, scenario planning is critical for decision making.
“Rather than asking ‘what will happen’, the more useful question is: what happens if conditions are better or worse than expected?”
The value of scenarios is that they force business to focus on risk and potential exposure.
Scenario planning also helps prevent ‘headline capture’. Even when geopolitics dominates attention, other risks can still break either way. A clear example is AI. If expected returns disappoint and markets reprice, investment conditions could tighten at the same time as energy costs rise, compounding downside risk. The aim is to keep decisions anchored to the structural forces (AI diffusion, demographics, supply-chain realignment) while stress testing for shocks that can change the near-term path.
For professional services firms and our clients, that approach supports faster, calmer decisions. It helps protect cash flow and delivery capacity in the downside scenarios, while keeping firms ready to invest and able to capture opportunity if conditions stabilise sooner than expected.
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If expected returns disappoint and markets reprice, investment conditions could tighten at the same time as energy costs rise, compounding downside risk. The aim is to keep decisions anchored to the structural forces (AI diffusion, demographics, supply-chain realignment) while stress testing for shocks that can change the near-term path.
The practical implication is that strategic decisions need to be robust to multiple paths, from rapid productivity acceleration to slower diffusion, or a period of market adjustment if expectations run ahead of realised gains.