AI is already influencing the global economy through investment and changes to work, but the evidence does not yet show a broad, durable productivity boom. Its future impact depends on whether firms turn spending and task-level efficiencies into lasting output gains—and whether workers and countries have the infrastructure, skills and institutions to share in them.
What is changing in the global economy now?
AI is affecting the economy through four connected channels: investment, productivity, employment and the distribution of income and economic power. They move at different speeds. Spending on AI infrastructure and systems can support demand today; productivity gains, if they persist, could raise output over time. Changes in work and the allocation of income depend on how firms deploy AI and who has access to the tools and skills needed to use it.
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The clearest current signal is investment, not proof of economy-wide productivity growth. The OECD’s September 2026 interim Economic Outlook says AI-related investment helped sustain production, trade and growth in the first half of 2026. The same report projects overall global GDP growth of 2.9% in 2026 and 3.0% in 2027. Those are the OECD’s projections for the whole economy, not estimates of how much AI contributed. The OECD warns that if returns on AI investment fall short of expectations, growth prospects could weaken and asset prices could be repriced.
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Could AI raise global growth?
It could, but the size and timing of the effect remain uncertain. The World Bank’s June 2026 Global Economic Prospects describes a possible pattern: investment may matter more in the near term, while productivity could become more important over the medium term. In an optimistic illustrative scenario, global growth in the 2030s exceeds the average growth rate of the 2000s. That scenario depends on AI producing transformative productivity improvements; it is not a central forecast or a measured outcome.
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Estimates vary because the result depends on several factors working together:
- How much work is affected: exposure varies by task, not simply by occupation or industry.
- How much faster or better those tasks become: exposure does not guarantee a useful productivity gain.
- How much economic value those tasks represent: time saved on a low-value task has a different effect from an improvement in a major production process.
- How widely and quickly firms adopt AI: access to tools alone does not ensure effective integration into work.
- Whether the improvement lasts: a one-time efficiency gain is different from a sustained increase in productivity.
The World Bank also notes that much of the evidence and modelling focuses on advanced economies, making the potential effect in emerging markets and developing economies harder to estimate. Its central point is that the growth effect depends on both persistent productivity improvements and countries’ ability to capture them.
Are productivity gains already showing up?
There are real task-level gains, but they are uneven and do not yet establish a global productivity acceleration. An International Labour Organization (ILO) review dated 1 June 2026 synthesizes experiments, firm data, platform studies and worker surveys across Australia, Denmark, Germany, Korea, Kuwait, the United Kingdom and the United States. It finds that reported gains are often unverified or uneven. In the evidence it reviewed, worker-reported time savings—on the scale of a few per cent of working hours—had not yet translated into higher measured output, earnings or employment.
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That distinction matters: saving time on an individual task does not automatically increase a firm’s output. The time may be absorbed by checking AI-generated work, shifting to other tasks, or changing how work is organized. Economy-wide productivity statistics also capture much more than the performance of a tool or a small group of workers.
Estimates of possible future productivity effects should be read as scenarios or research summaries, not as realized gains. An OECD cross-country report published in 2025 summarizes estimates from other studies that range from an almost zero contribution to annual U.S. labour-productivity growth over the next decade to a more optimistic one percentage point. For Europe, it cites early estimates of about 0.3–0.4 percentage points. These are estimates attributed to other researchers, not OECD measurements, guarantees or observed outcomes.
What does AI exposure mean for jobs?
Exposure means that some tasks in a job could be affected by generative AI; it does not mean the whole job will disappear. The joint ILO–World Bank analysis, reported by the ILO in 2026, covers 135 countries—around two-thirds of global employment. It finds higher generative-AI exposure in advanced economies, particularly in clerical and professional occupations. The study’s scope is an analysis of labour exposure, not direct measurement of AI’s effects in all 135 countries.
Whether exposure leads to automation, assistance or little change depends on the tasks people actually perform, the tools available, connectivity and workers’ skills. Two people with the same job title may do substantially different work. AI may take over some tasks, help a worker complete others, or change coordination and oversight without eliminating the role.
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The ILO’s June 2026 evidence review says large-scale displacement remains limited in the studies it synthesizes. It nevertheless identifies risks to younger workers’ employment opportunities and possible changes to work organization, coordination, autonomy and job quality. These are emerging risks, not a definitive global forecast of job losses.
Why the effects differ between countries
Countries cannot be ranked simply by how many jobs are exposed. The likely outcome depends on the relationship between exposure and the capacity to benefit: the tasks workers perform, their skills, the reliability of digital infrastructure, firms’ ability to adopt AI productively and the institutions that shape how gains and risks are shared.
| Context | What the evidence indicates | Why outcomes remain uncertain |
|---|---|---|
| Advanced economies | Higher generative-AI exposure, especially in clerical and professional occupations, according to the 2026 ILO–World Bank analysis. | Exposure alone does not show whether tasks will be automated or assisted, or how productivity and job quality will change. |
| Emerging and developing economies | Some workers may be less exposed because of differences in task composition and computer use at work; unreliable internet can also limit access to productivity benefits. | Lower aggregate exposure does not mean insulation from disruption. Some vulnerable clerical and administrative jobs may be exposed, while infrastructure gaps can prevent workers and firms from benefiting. |
| Workers in exposed clerical roles | Some of these jobs have provided relatively higher-quality work and pathways to decent employment, including for women and young people. | Replacing or reshaping such roles could affect access to work as well as the tasks done by people already employed. |
The same divide can run in both directions: some workers may face disruption before productivity benefits arrive, while others may be unable to adopt tools that could help them. Connectivity, skills and task mix help explain why an exposure measure is not a prediction of who wins or loses.
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Even if AI raises productivity, the gains need not be distributed evenly. They could appear as higher wages, lower prices, larger profits, more output or some combination. The outcome depends in part on bargaining power, the availability of alternatives for workers and firms, and how concentrated control is over models, computing and other essential inputs.
The International Monetary Fund (IMF) identifies income distribution, worker preparation, market concentration and unequal access to frontier models and computing as concerns. These issues connect the workplace effects of AI to economic power: when a small number of firms control important systems or infrastructure, they may be better positioned to capture returns, while smaller firms and lower-income countries may face barriers to adoption.
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That possibility is not proof that concentration or inequality will rise everywhere. The distributional effects depend on policy and on how access, competition, worker protections and investment evolve.
What will shape the outcome?
The World Bank, ILO and IMF point to enabling conditions rather than a single intervention that guarantees gains. Their policy priorities include reliable digital connectivity, relevant skills and lifelong learning, public investment, labour-market institutions and social protection. They also identify regulation and broader access to technology as ways to manage risks and widen participation.
- Build digital foundations: reliable connectivity and access to relevant systems affect whether workers and firms can use AI at all.
- Develop skills: education and lifelong learning can help workers adapt as tasks and job requirements change.
- Support adjustment: labour-market institutions and social protection can help people navigate job disruption and changes in job quality.
- Address concentration and systemic risks: the IMF also flags financial supervision, cybersecurity, energy supply and concentration in the AI ecosystem as issues for policymakers.
- Coordinate internationally: cooperation matters when countries differ in access to infrastructure, skills and frontier systems.
These are policy priorities identified by the institutions, not interventions with quantified returns established by the evidence summarized here.
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For readers assessing a new estimate or headline, four checks can help separate evidence from expectation:
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- Identify what is being measured. Investment, task-level time savings, measured output, employment and GDP are different indicators.
- Check whether the figure is observed or projected. The OECD’s 2025 productivity ranges summarize other researchers’ estimates; the World Bank’s 2030s scenario is conditional, not a forecast.
- Look at the population and setting. Evidence from particular countries, firms or occupations does not automatically describe the global workforce.
- Ask who can benefit. Connectivity, skills, task composition and institutions affect whether exposure becomes assistance, automation or a productivity gain.
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