The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Assess AI exposure by identifying where AI enters a company’s business, testing whether specific uses can create measurable economic value, and examining the dependencies, risks, and controls behind them. Adoption alone does not establish a competitive advantage or a likely investment return; the case depends on company-specific evidence.
What counts as AI exposure?
AI exposure is broader than selling AI products. A company may develop AI, supply models or infrastructure, provide data or other inputs, integrate systems, or use AI in its operations, products, or services. It may also depend on business partners that develop or deploy systems it relies on. Map the relevant activities and relationships across the AI value chain before judging whether exposure is an opportunity, a risk, or both. The OECD’s responsible AI due-diligence guidance addresses enterprises across that value chain.
How to assess a company’s AI exposure
1. Map its role and relationships
Classify the company’s involvement: developer, model or infrastructure supplier, data or other input provider, integrator, or AI user. Note which business units and products are affected, and identify important partners involved in developing or deploying systems. A company can occupy several roles at once.
2. List the concrete use cases
For each material use, record the business function, intended user, system or provider, data involved, intended outcome, and deployment status. Separate systems in production from pilots and future plans. Ask management why AI is appropriate for the problem and what evidence supports the expected benefit; the OECD guidance specifically recommends seeking a clear, concise rationale for adoption.
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3. Test the economic case and dependencies
Ask what management expects AI to change: costs, revenue, service quality, capacity, or another business outcome. Find out how the company measures that effect, whether results have been realized, and whether the effect is material to the investment thesis. Examine associated expenses as well as benefits.
Then identify dependencies on model, cloud, compute, data, and integration providers. Consider whether the company has alternatives, how difficult it would be to switch, and whether contractual or operational constraints limit its options. The cited guidance does not offer a universal return metric, and adoption by itself does not prove improved returns.
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4. Examine risks, controls, and accountability
Match the review to each system and use case. Relevant questions include whether data is reliable and lawfully handled, whether privacy or bias concerns are addressed, and whether the system performs robustly and can be understood well enough for its intended use. Check for human oversight, clear responsibility, cybersecurity protections, and procedures to detect and respond to incidents.
The IMF technical note on AI in securities markets discusses data risks such as privacy and bias; performance concerns such as robustness, synthetic data, and explainability; cyber threats including data manipulation; and broader financial-stability risks. Its focus is securities markets, so the relevance and materiality of each issue depend on the company and its use cases. The note’s authors state that their views are not necessarily those of the IMF, its Executive Board, or IMF management.
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OECD due diligence is not limited to listing risks: it calls for identifying and assessing actual and potential adverse impacts, preventing or mitigating them, tracking results, communicating actions, and providing for or cooperating in remediation where appropriate. Its investor examples include the instruction to “Include risks of adverse impacts in portfolio risk assessments or investment analyses.”
5. Evaluate disclosures and follow up
Read filings and other official company disclosures for the systems in use, their business purpose, material supplier dependencies, risk ownership, controls, incidents, and measures of results. Compare claims about opportunity with the company’s discussion of costs and risks. Specific, consistent disclosures are more useful than broad statements about being “AI-powered.”
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In March 2025, SEC Commissioner Caroline Crenshaw asked: “What disclosures are being made around AI uses and risk, and are they consistent and sufficient?” Her remarks at an SEC roundtable frame useful investor questions; they are not a binding rule or a complete disclosure checklist. They also raise the possibility that AI use across financial-industry participants could have systemic market or volatility implications.
If public information leaves material gaps, ask management for its adoption rationale, risk assessments, planned mitigation, implementation measures, and results. OECD guidance notes that when business relationships do not provide enough information, an enterprise may use existing assessments while continuing to engage for disclosure. It also describes bilateral dialogue, requests for additional information or action, and escalation when other approaches fail.
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How to compare AI exposure across investments
For two or more companies, compare them on explicit dimensions rather than treating AI exposure as a single score. This framework synthesizes the cited guidance; it is not an official rating or scoring standard.
| Dimension | What to compare |
|---|---|
| Value-chain role and use case | Where AI enters the business, which activities it affects, and how central those activities are. |
| Evidence and materiality | Whether benefits are measured and realized, and whether they matter to the investment thesis. |
| Dependencies | Reliance on vendors, data, compute, and integration providers; available alternatives and switching constraints. |
| Risk and governance | Identified impacts, controls, accountability, monitoring, and remediation. |
| Disclosure quality | Specificity and consistency of disclosures, and whether management can answer follow-up questions. |
| Engagement capacity | Access to management and credible opportunities to seek additional information or improvement. |
If disclosure is sparse, record the uncertainty instead of assigning a confident score. General guidance can structure diligence, but it cannot establish a named company’s AI exposure, valuation, or future performance; those judgments require current company evidence and investment analysis.
What AI investment figures can—and cannot—show
The OECD’s 2026 guidance, citing OECD data, reports that global annual AI venture-capital value rose from about USD 6.4 billion in 2012 to USD 147 billion in 2024, accounting for 56% of the value of all VC investment by Q3 2025. This describes venture-capital investment, not public-market returns or the financial results of any specific company. It does not show that AI adoption creates value for a particular investment.
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