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To evaluate a company’s exposure to AI disruption, trace the path from what AI can do to what customers and the company will actually adopt—and then to the effect on revenue, costs, competition, and the company’s ability to capture value. Task exposure is an early signal, not a forecast of business losses, job cuts, or investment returns.
What does AI exposure mean at the company level?
A company is exposed when AI could change the work it performs, the products customers buy, or the way competitors deliver similar outcomes. But those changes are not the same thing as disruption. Four separate questions need answers:
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- Capability: Can AI perform or assist the relevant task?
- Adoption: Can it do so reliably, affordably, and within the company’s real workflow?
- Market effect: Will adoption change customer demand, prices, or competitive conditions?
- Value capture: Will this company retain or gain enough value to offset the costs of change?
Occupational exposure measures address only part of the first question. They do not directly measure a company’s revenue at risk, likelihood of failure, or future employment. A highly exposed activity may be difficult to automate profitably; conversely, a company may face pressure if AI lets customers bypass one of its offerings even when its own workforce is not especially exposed.
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How do you assess AI risk for a company?
1. Map how the company makes money
Start with the economic engine, not an AI score. Identify the main products and services, customer groups, pricing basis, recurring versus transactional revenue, and major costs. For each offering, state the customer outcome it delivers and how the company earns from that outcome.
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Then identify potential sources of defensibility: proprietary data, distribution, trust, regulation, integration, service, network effects, or switching costs. Treat each as a hypothesis to test. AI may strengthen a company’s advantage, but it may also make a product easier to reproduce or reduce customers’ need for an intermediary.
2. Connect tasks to products and customer workflows
List high-volume or high-cost activities inside the company, then map them to the workflows its products support. For each activity or workflow, ask whether AI could automate a task, assist a worker, improve speed or quality, enable a new product, or let customers do the job without the company’s current offering.
Do not equate generating an output with completing a customer’s job. Test what happens across the full workflow, including data access, human review, exceptions, accuracy requirements, integration, and accountability when an output is wrong.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →3. Use exposure indices as screening evidence
The International Labour Organization’s 2025 GenAI exposure index is useful context, not a company valuation tool. Its method combined task-level information, worker input, expert discussion, and model predictions across ISCO-08 task descriptions. Index development used a representative sample of 29,753 tasks in Poland’s occupational classification; input on perceived automation potential from 1,640 employed people; and 52,558 data points on automation potential for 2,861 tasks. The ILO reported that clerical work remained highly exposed and that exposure was rising in some digitized professional and technical roles.
The ILO’s global occupational estimates put one in four workers in an occupation with some GenAI exposure, while 3.3% of global employment was in the highest exposure category. In that highest category, the estimates were 4.7% of female employment and 2.4% of male employment; overall exposure was estimated at 11% of employment in low-income countries and 34% in high-income countries. These are occupational exposure figures, not company-specific estimates of revenue, job losses, or disruption probability.
There are limits to what such indices can establish. In a 17 April 2026 brief, the ILO explains that exposure measures rely on static descriptions of current tasks, omit economic feasibility and institutional barriers, include subjective assumptions, and do not model workflow change or adjustments in employment, wages, and demand. The ILO says exposure indicators are best understood as early signals of where work may change. Its brief cautions that they “cannot be interpreted as predictions of job displacement, productivity gains or reskilling needs.”
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4. Test whether adoption is feasible and valuable
Look for evidence beyond technical capability or a product announcement. Relevant indicators include customer usage, renewals, implementation time, realized cost savings, quality outcomes, regulatory acceptance, and willingness to pay. For internal uses, check whether deployments work in the production workflow, what human review remains necessary, and whether measured savings persist after support and integration costs.
Separate observed results from management statements, third-party estimates, and analyst inference. A pilot, announced partnership, or available AI feature is not by itself evidence of broad adoption or attractive economics.
Will AI disrupt the company’s business model?
Translate the workflow analysis into explicit business channels. For each one, record the mechanism, expected timing, supporting evidence, and uncertainty. Avoid combining all effects into a single headline judgment before examining them separately.
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| Impact channel | What to examine |
|---|---|
| Demand or price pressure | Could customers need less of a vulnerable offering, negotiate lower prices, or switch to an AI-enabled alternative? |
| Substitution or bypass | Could a new AI product replace the company’s role, or allow a customer to complete the workflow without an intermediary? |
| Lower delivery costs | Can AI reduce labor or other costs in practice, after integration, review, support, and ongoing operating expenses? |
| More customer value or market growth | Could faster, better, or newly possible outcomes raise willingness to pay or expand the addressable market? |
| Investment and operating expense | What spending is needed for infrastructure, models, data, energy, components, implementation, and support, and when might revenue follow? |
| Pricing power and margins | Will the company retain the benefit of improved productivity, or will competition and customer bargaining pass it through as lower prices? |
Microsoft’s fiscal 2026 Form 10-K illustrates why both sides matter, but it is not a universal benchmark. The filing discusses competition from free applications and open-source products that may mimic features, and warns that competition can pressure sales volumes and prices. It also describes significant AI infrastructure and operational investment ahead of fully developed revenue streams, alongside uncertainty about adoption, demand, capacity utilization, training and inference costs, component and energy costs, and pricing pressure. Treat these as disclosure categories to investigate at any company—not as proof that the same economics apply elsewhere.
Which companies are most exposed to AI?
There is no reliable universal ranking based on occupational exposure alone. A more useful comparison asks how much of a company’s value chain is vulnerable, whether AI can complete the customer outcome, how quickly customers can switch, and whether the company can defend or capture the resulting value.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches| Comparison axis | Evidence to compare consistently |
|---|---|
| Task and workflow exposure | Which high-volume or high-cost tasks and customer workflows could AI affect, and what steps still require people or specialized systems? |
| Substitutability of the customer outcome | Can customers meet the same need with an AI product, an in-house workflow, or a different provider? |
| Adoption and willingness to pay | Are customers using the capability, renewing, and paying for it? How long and costly is implementation? |
| Price and margin pressure | Is AI lowering delivery costs, weakening pricing power, or both? |
| Investment and operating costs | What continuing infrastructure, model, energy, labor, and support costs are required? |
| Defensibility | Do data, distribution, trust, integration, switching costs, or other advantages remain meaningful as AI capabilities spread? |
| Implementation capacity | Can the company integrate AI into real workflows and maintain quality, security, and service? |
| Governance and regulatory constraints | What rules, accountability requirements, or stakeholder impacts could shape deployment or limit use? |
Compare firms over the same reporting periods and label each input by its source: observed operating result, management-stated expectation, third-party estimate, or analyst inference. This makes it easier to distinguish a company with high technical exposure but credible adoption barriers from one with lower task exposure but a vulnerable customer proposition.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can the company respond and capture value?
Assess whether the company can turn a capability into a durable economic result. Examine whether it can access the relevant data and distribution, integrate AI into existing products and workflows, retain customer trust, comply with applicable rules, and earn more from the improvement than it spends on compute, labor, capital, and support. Also ask whether the same tools lower barriers for new entrants or make the company’s own product easier to replace.
Responsible implementation belongs in the analysis, not in a separate box disconnected from business performance. The OECD’s six-step due-diligence framework covers embedding responsible business conduct in management systems; identifying and assessing impacts; preventing and mitigating impacts; tracking implementation; communicating actions; and cooperating in remediation where appropriate. These steps can help an analyst check whether a company has processes to identify and manage effects on workers, customers, and others as it deploys AI.
How should you turn the assessment into a scorecard?
There is no source-supported, validated cross-industry company AI exposure score. If a consistent internal scorecard is useful, keep the dimensions separate rather than blending exposure, readiness, and resilience into one number. The table below is an analyst framework, not a predictive model.
| Dimension | Question | Evidence strength to record |
|---|---|---|
| Exposure | Which tasks, workflows, offerings, or customer outcomes could AI change? | Task-index evidence, workflow mapping, and specific product analysis |
| Adoptability | Can the use case meet reliability, integration, cost, and regulatory requirements? | Production usage and measured outcomes are stronger than pilots or announcements |
| Economic impact | How could demand, price, cost, investment, and margins change? | Observed revenue and cost effects are distinct from forecasts and assumptions |
| Value capture and response | Can the company defend its position and retain enough benefit to justify the investment? | Customer retention, willingness to pay, defensibility, and implementation record |
| Governance | Does the company identify, mitigate, track, communicate, and address impacts? | Documented processes and results, not policy language alone |
For each dimension, record the evidence, its date, and whether it is observed, management-stated, third-party estimated, or inferred. If a data point is unavailable, mark it as unknown rather than silently treating it as zero risk. Revisit the assessment when capabilities, customer behavior, disclosures, or regulation change.
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