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To measure who benefits from AI investments, track more than productivity or financial return. Set a pre-deployment baseline, define the intended outcomes and beneficiaries, then monitor business or service results alongside worker experience, job quality, safety, and how gains and costs are distributed. There is no universal ROI formula that establishes whether an AI investment has benefited everyone fairly.
Start by deciding what “benefit” means
Name the investment and the decision the evaluation will inform: for example, whether to expand a deployment, change how it is used, or stop it. Specify the time horizon and the people or organizations whose outcomes matter. Depending on the system, beneficiaries and cost-bearers may include the investing firm, employees, customers, suppliers, the public, and affected communities.
Set out the intended outcomes before deployment. They might include higher output, lower costs, better service, improved access, or safer work. Also identify potential costs, such as maintenance, training, job disruption, or changes in work quality. This gives the evaluation a basis for checking whether the promised benefits occurred and who experienced them.
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Record current performance and conditions before introducing the system. Choose a comparison period or group where feasible, and document the measures relevant to the intended outcomes. Depending on the investment, a baseline may cover:
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- Output, quality, operating costs, and service levels.
- How tasks are allocated and how much time they require.
- Worker outcomes such as job quality, safety, and experience at work.
- Access to a service or its results for different user groups.
A before-and-after change does not by itself prove that AI caused the change. Other factors may have shifted at the same time. The evaluation should distinguish observed change from an estimated causal effect and explain the comparison used.
Measure outcomes at several levels
Pair organizational results with outcomes for people who use or work with the system. A productivity gain can coexist with poorer working conditions or uneven access; a single aggregate figure will not show that distribution.
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| Measurement level | What to examine | Why it matters |
|---|---|---|
| Organization or service | Productivity, income, costs, quality, and service outcomes | Shows whether the investment delivered its operational or financial aims. |
| Workers | Task changes, job quality, safety, work experience, and transition costs | Shows whether workers gained capability or time, faced new risks, or experienced reduced demand for their work. |
| Customers and users | Service quality, access, and who receives the benefits | Shows whether improved organizational performance also translates into better outcomes for users. |
| Firms, sectors, or communities | How gains and costs are distributed, including differences in capacity to adopt and integrate AI | Shows whether benefits concentrate among some organizations or groups rather than spreading broadly. |
Disaggregate results by relevant worker or user characteristics when lawful and appropriate. OECD analysis notes that worker outcomes can differ by skills, experience, occupation, industry, disability, and whether AI automates or augments tasks. Context also matters at a broader level: skills, infrastructure, industry mix, and integration into trade can affect a country, sector, or firm’s capacity to benefit from AI. See the OECD report on AI, productivity, distribution, and growth and its overview of AI’s macroeconomic effects.
Separate augmentation from automation
Track how the system changes tasks, not just job titles or headcount. With augmentation, AI may help people do tasks more quickly or effectively. With automation, it may reduce or remove demand for some tasks. A deployment can involve both pathways, affecting groups differently.
- Record which tasks changed and for whom.
- Check who gained time, capability, or access to new work.
- Identify workers who face reduced demand, changed responsibilities, or transition costs.
- Include safety and job quality, not only output or labor costs.
This distinction helps reveal whether a measured efficiency gain reflects useful assistance, a shift in who performs the work, or both.
Tailor the evaluation to the system and its purpose
There is no single set of measures that suits every AI investment. The OECD’s Framework for the Classification of AI systems organizes relevant context into five dimensions: People & Planet, Economic Context, Data & Input, AI Model, and Task & Output. Considering these dimensions helps evaluators describe what a system does, the setting in which it is used, and who may be affected.
NIST’s TEVV-Athlon Framework for Evaluating AI Systems describes a four-stage approach for creating customized assessments. Together, these resources support context-specific evaluation; they do not prescribe a universal weighted score or causal ROI formula.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsMonitor whether benefits materialize after deployment
An investment’s expected benefits are not established just because a system was purchased or launched. Follow implementation and check whether intended outcomes occur, whether adoption is sustained, and whether costs or risks emerge. OECD guidance for government calls for planning, implementing, and monitoring AI investments to assess value for money, investment risks, timely deployment, and realization of intended benefits. Although that guidance concerns public-sector use, its monitoring principle is relevant to evaluating an investment over time. See OECD guidance on governing with AI.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use worker surveys as evidence of experience, not proof of ROI
An OECD publication reports that four in five surveyed workers said AI improved their performance at work, and three in five said it increased their enjoyment of work. These are workers’ reported experiences, not causal estimates of investment returns or results that should be assumed to apply to every workforce. The publication does not state the underlying survey fieldwork year in the cited result. Use surveys alongside operational measures, and label perceptions as perceptions. See OECD, Using AI in the workplace.
Compare AI investments on the same basis
When comparing deployments, use the same time horizon and examine the same categories of outcome. Do not let a strong aggregate productivity result substitute for evidence about distribution, job quality, or realized benefits.
- Compare productivity, income, cost, or service outcomes.
- Examine how gains and costs fall among firms, workers, customers, and the public.
- Assess job quality, safety, displacement, and transition effects.
- Account for system context and differences in adoption capacity.
- Check whether intended benefits were realized after deployment.
These comparison axes synthesize OECD and NIST guidance; the frameworks do not prescribe a universal weighting or single score. A clear report should state what was measured, when, for whom, and what the evidence can—and cannot—establish. OECD’s AI Principles call for responsible AI use at work and benefits that are broadly and fairly shared, including attention to worker safety, job quality, productivity, and public services.
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