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Pension funds should assess artificial intelligence (AI) as a portfolio-wide exposure and governance issue—not just as a bet on technology stocks. Trustees need to look through the whole scheme for direct and indirect AI exposure, test a severe but plausible AI-related downturn, and examine potential losses, liquidity demands, leverage and changing correlations. The UK Pensions Regulator (TPR) sets out this approach in its current guidance for trustees.
Why is AI investment risk a portfolio-wide issue?
An AI shock could affect much more than companies that develop AI software or chips. Pension portfolios may have indirect exposure through cloud providers, utilities, energy, data centres and other real estate, private-market assets, credit, or funds whose holdings overlap with AI-linked companies. A diversified-looking portfolio can therefore depend on the same businesses, financing conditions or infrastructure more than its fund labels suggest.
Concentration can build in different ways. A passive market-cap portfolio may assign a larger weight to a company as its market value rises. Several active managers may independently hold many of the same companies. Exposure can also arise through lenders, infrastructure owners or shared service providers rather than a direct equity stake.
TPR’s 22 October 2026 press release reported that global private AI investment rose 127.5% year on year in 2025. That figure describes global private investment, not the return on AI assets or the exposure of any particular pension scheme. It does, however, underline why trustees should examine private as well as public holdings.
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What risks should trustees assess?
A useful assessment connects portfolio exposures to the events that could impair them, then considers whether the scheme could absorb the resulting financial and operational effects. TPR, the Financial Stability Board (FSB), the European Securities and Markets Authority (ESMA), FINMA and the US Treasury address different parts of that task; their guidance and findings do not all have the same jurisdiction or legal status.
Exposure and concentration
Start by defining what counts as AI exposure. Include direct holdings in AI-related businesses and indirect links through suppliers, customers, infrastructure, lenders and funds. Ask managers to explain their definitions: one may classify a company by its main business, while another may count only a portion of revenue or holdings. Without consistent definitions, comparisons and portfolio totals can be misleading.
Map overlapping positions across mandates and default arrangements, then check for concentration in shared dependencies such as chips, cloud services, energy or data-centre capacity. TPR says trustees should look through the whole portfolio, including default arrangements. The aim is to understand common economic dependencies, not merely to add up assets carrying an “AI” label.
Valuation and business assumptions
Test whether asset prices depend on optimistic assumptions about earnings, productivity, adoption or the speed at which AI becomes commercially useful. A company can be important to the AI ecosystem without every expected growth assumption being reflected in sustainable earnings. Trustees should ask what happens if adoption takes longer, costs rise, or the revenue and productivity gains assumed by investors do not appear as expected.
Also consider disruption from regulation, supply bottlenecks, energy constraints or capital expenditure (capex) that exceeds what businesses can support. These are distinct paths to repricing: a portfolio can be vulnerable even if demand for AI remains strong, for example if infrastructure costs or financing needs undermine expected returns.
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Liquidity, leverage and funding
A market-value decline is not the only relevant loss. Trustees should examine whether a shock could create liquidity needs, collateral calls or pressure on leveraged positions, and whether assets could be sold when needed without worsening the outcome. For a defined-benefit scheme, consider implications for funding and the ability to meet benefit obligations. For a defined-contribution scheme, consider how market movements may affect member accounts and decisions.
Changing correlations and systemic links
Assets that appear diversified in ordinary markets may become more closely linked under stress. TPR calls for trustees to compare losses, liquidity demands and correlation changes in an AI-related stress with those in existing stress tests. The FSB’s 14 November 2024 assessment warns that AI may amplify market correlations, third-party concentration, cyber and model risks, data-quality and governance weaknesses, fraud and disinformation. It also points to possible effects on market structure, macroeconomic conditions and energy use.
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Manager, model and counterparty controls
Ask investment managers and relevant service providers how they use AI in investment decisions and what controls apply. FINMA identifies risks involving model robustness, correctness, explainability and bias; data security, quality and availability; IT and cyber security; third parties; and legal and reputational consequences. These categories can help trustees structure due diligence, while FINMA’s supervisory expectations are specific to its Swiss context.
Useful evidence includes model validation, data provenance, incident reporting, human oversight and the ability to stop or replace a system. For material dependencies, request information about service continuity and exit arrangements. A provider’s assurance should explain what was tested, by whom, and how problems are escalated—not simply state that a model is monitored.
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Regulatory, conduct and fiduciary responsibilities
AI use does not remove existing responsibilities. ESMA says firms using AI in investment services remain subject to MiFID II organisational, conduct and best-interest duties. It highlights risks including algorithmic bias, opaque decisions, overreliance, privacy and security. Those statements concern firms and services within the relevant European regulatory framework; they should not be treated as a complete description of rules in every jurisdiction.
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Operational resilience and member outcomes
Include cyber incidents, data leakage, model drift, vendor outages, fraud and AI-generated scams in operational risk discussions. Consider how an incident would be detected, who would be notified and what continuity or recovery arrangements would apply. An investment strategy can be financially diversified yet operationally reliant on a small number of technology providers.
For defined-contribution schemes, member communication is part of resilience. A downturn may prompt members to crystallise losses or stop participating. Trustees should have a clear, accurate communication approach ready for volatile conditions, without implying that members can avoid all investment risk by reacting to a market move.
How can a pension committee assess hidden AI exposure?
Use a consistent exposure map that reaches beyond named AI companies. The following comparison helps reveal where a portfolio’s apparent diversification may conceal common sources of risk.
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| Review dimension | What to examine | Question for managers or advisers |
|---|---|---|
| Direct versus indirect | AI businesses as well as suppliers, customers, lenders, infrastructure and service dependencies | What definition of AI exposure are you using, and which indirect links are included? |
| Public versus private | Listed securities, private equity, private credit and infrastructure assets | Can you provide look-through information for private assets and debt, and explain any limits? |
| Passive versus active | Index weights, shared holdings among active mandates and the effect of market-cap changes | Where do mandates overlap, and how could rising market values change concentration? |
| Valuation sensitivity | Dependence on adoption, productivity, earnings, capex, energy and regulatory assumptions | Which assumptions would cause the largest change in valuation if they proved wrong? |
| Liquidity and leverage | Potential asset sales, financing links, collateral demands and leverage effects | What cash or collateral could be needed in a stress, and how quickly? |
| Correlations under stress | Whether holdings or strategies become more closely linked in an AI-related downturn | How do your correlation assumptions change in the severe-but-plausible scenario? |
| Third-party dependencies | Common cloud, model, chip, energy or data-centre providers | What are the material dependencies, and what continuity and exit arrangements exist? |
| Model, data and cyber controls | Validation, data quality and security, explainability, human oversight and incident response | What has been tested, how are failures escalated, and who can intervene? |
| Conduct and member effects | Applicable regulatory duties, fairness, privacy and communications during market stress | How are legal compliance and member impacts assessed when AI is used? |
How should trustees stress-test AI investments?
TPR instructs trustees to test a severe but plausible downturn in AI-related assets and compare losses, liquidity demands and correlation changes with existing stress tests. The test should connect a credible cause to portfolio consequences; it should not depend on a single forecast of how AI will develop.
Build scenarios around plausible failure paths
Use a base case and adverse cases alongside the severe-but-plausible scenario. Relevant drivers include an AI-related repricing, a reversal in infrastructure investment, slower adoption, a regulatory shock, an energy constraint, a supply bottleneck or capex overshoot. These are scenario ingredients, not predictions that any one event will occur.
Trace the effects through the scheme
For each scenario, map direct and indirect holdings and identify where common exposures could transmit the shock. Ask managers to state their assumptions so trustees can see whether different mandates rely on the same market, liquidity or adoption expectations. Where look-through data is incomplete, record the resulting blind spot rather than treating an unknown exposure as zero.
Measure more than investment losses. TPR calls attention to liquidity demands and correlation changes; the committee should also examine leverage and collateral effects, funding implications for defined-benefit schemes, and potential impacts on defined-contribution member outcomes. The purpose is to find out whether the scheme can meet its obligations and manage the consequences, not just whether a portfolio’s paper value falls.
Challenge the result and set escalation limits
Ask whether the scenario is severe enough to test the scheme’s assumptions, but still plausible enough to inform decisions. Compare its results with existing stress tests, identify which assumptions drive the result and decide what would prompt escalation or a change in risk management. TPR’s guidance does not prescribe a universal loss threshold or testing frequency; trustees should set limits and review arrangements appropriate to their scheme and document their rationale.
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What should trustees request from managers and advisers?
Requests should produce evidence that can be reconciled across the scheme, not just a general statement that a manager considers AI risk. TPR’s whole-scheme approach makes look-through information especially important for funds, private assets and debt.
- Exposure data: holdings and relevant indirect exposures, the manager’s AI classification method, and information on private assets and debt.
- Concentration analysis: repeated holdings across mandates, index weights, financing links and common technology or infrastructure dependencies.
- Scenario assumptions: drivers, severity, affected assets, loss estimates, liquidity needs, leverage and collateral effects, and changes in correlation.
- Valuation rationale: assumptions about earnings, adoption, productivity, regulation, energy and infrastructure spending, including sensitivity to adverse changes.
- AI governance: where AI is used, how models are validated, how data quality and provenance are assessed, what bias and explainability checks exist, and where human oversight applies.
- Operational resilience: cyber and privacy controls, third-party dependencies, incident reporting, business continuity and exit plans.
- Accountability: who owns each risk, who receives incidents and test results, and how issues are escalated to the scheme’s governing body.
When managers cannot supply a requested item, ask them to identify the limitation and explain how they manage the resulting uncertainty. This lets trustees distinguish a genuine absence of exposure from an inability to see through a fund or dependency chain.
What governance process keeps the assessment useful?
AI exposure and provider dependencies can change as investments, market values and business practices change. A committee therefore needs clear ownership and review arrangements, not a one-off inventory. TPR’s guidance expects schemes to maintain high investment-governance standards and manage risk over the short, medium and long term.
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- Assign ownership. Decide which committee, trustee, adviser or manager supplies each part of the assessment. Delegated work should have a named reviewer and a route for unresolved issues.
- Set a common taxonomy. Agree what the scheme counts as direct and indirect AI exposure so manager reports can be compared and combined.
- Reconcile the portfolio. Look through mandates and default arrangements, identify repeat holdings and map links to financing and shared infrastructure.
- Approve scenarios and limits. Document the chosen stress cases, the outcomes to measure, the scheme’s risk appetite and the conditions that trigger escalation.
- Review provider controls. Record evidence on model, data, cyber, privacy and third-party safeguards, along with incident and recovery arrangements.
- Plan for communication and follow-up. Decide who informs trustees and members when material issues arise, and record actions and review dates.
This process should leave trustees able to explain what the scheme owns, what it depends on, what could fail together and how the governing body would respond. The objective is not to predict which AI companies will succeed; it is to understand and govern the scheme’s exposure to uncertainty.
Frequently Asked Questions
Do pension funds need to sell AI-related investments to manage the risk?
The guidance described here focuses on identifying exposure, testing scenarios and maintaining investment governance; it does not prescribe a blanket sale of AI-linked assets. Whether a holding remains suitable depends on the scheme’s objectives, risk appetite and analysis of the investment and its dependencies.
Does a pension fund have AI exposure if it owns no AI company shares?
It may: exposure can arise indirectly through funds, lenders, infrastructure, suppliers or shared technology and energy dependencies. A direct-holdings screen alone cannot establish that the rest of a portfolio is insulated.
Is AI used by an investment manager the same risk as investing in AI companies?
No. Holdings and market exposure concern what the scheme owns or depends on; a manager’s use of AI raises separate questions about decision-making, data, models, oversight and applicable conduct rules. Trustees may need to assess both.
Who is accountable if a pension provider or adviser uses AI?
Using a third party does not by itself transfer trustees’ accountability for scheme oversight. Trustees should seek assurance about the provider’s controls and understand how material issues are reported and escalated.
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