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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 errorsTo measure who benefits from an AI investment, track both the overall results and how gains, costs, and risks are distributed. Set a baseline before deployment, measure business or service outcomes alongside worker and user experience, and revisit the results after implementation. A productivity increase alone cannot show whether benefits are broadly shared.
Start by defining what “benefit” means
Specify the AI investment, the decision the evaluation should inform, and the period being assessed. Identify who could gain or bear costs: owners, employees, customers, suppliers, the public, or affected communities. A project may improve output while shifting workloads, reducing demand for some tasks, or creating costs elsewhere.
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There is no universal causal return-on-investment formula that answers these distribution questions for every AI system. The OECD notes that the capacity to benefit varies across countries, sectors, and firms, influenced by skills, infrastructure, industry mix, and integration into trade. OECD analysis of AI’s macroeconomic effects discusses these differences.
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Record the situation before the system is introduced, so later results can be compared against something concrete. Where feasible, use a pre-deployment period or comparison group. The appropriate design depends on the investment; the frameworks cited here support tailored evaluation, not one prescribed experimental method.
#1 Best Overall
Choose baseline measures that match the project’s purpose, such as:
- Output, quality, cost, and service levels.
- Which tasks people perform, and how time or responsibility is allocated.
- Relevant worker outcomes, including job quality, safety, and work experience.
- Access to the service or capability for customers and other affected users.
Measure results at more than one level
Pair aggregate measures—such as productivity, income, cost, or service performance—with measures that show who experiences the change. Disaggregate results by relevant worker or user characteristics when lawful and appropriate. The OECD identifies skills, experience, occupation, industry, and disability among factors associated with differing workplace outcomes; whether AI automates or augments tasks also matters. See the OECD’s 2024 report on AI, productivity, distribution, and growth.
Rank #2
For a comparison between two deployments, use the same time horizon and examine the same dimensions:
- Aggregate productivity, income, cost, and service outcomes.
- How gains are distributed among firms, workers, customers, and the public.
- Job quality, safety, displacement, and transition effects.
- The system’s context and the organization’s capacity to adopt it.
- Whether intended benefits materialized after implementation.
These comparison axes synthesize OECD and NIST guidance; they are not a universal weighted score.
Distinguish augmentation from automation
Track which tasks change, who gains time or new capabilities, and who may face reduced demand for their work or transition costs. Treating “AI exposure” as one uniform outcome can obscure important differences: a system that assists a worker with a task may have different effects from one that takes over the task.
Include job quality and safety alongside output. The OECD’s AI Principles call for responsible AI use at work, better worker safety and job quality, and benefits that are broadly and fairly shared. This is policy guidance, not a claim that any particular deployment has achieved those aims.
Assess the system in its context
Evaluation should fit the system and its purpose, rather than rely on a single generic checklist. The OECD’s Framework for the Classification of AI Systems organizes relevant context across People & Planet, Economic Context, Data & Input, AI Model, and Task & Output.
NIST’s TEVV-Athlon Framework for Evaluating AI Systems describes a four-stage approach for developing customized assessments. Together, these frameworks help structure what to examine; they do not supply a single return calculation that can be applied unchanged to every investment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor benefits and costs after rollout
Deployment is not the end of measurement. Revisit intended outcomes and track adoption, maintenance, risks, and the groups that actually experience the results. In its guidance for government, the OECD recommends planning, implementing, and monitoring AI investments to assess value for money, investment risks, timely deployment, and whether intended benefits are realized. See OECD guidance on governing with artificial intelligence.
Monitoring should also look for outcomes that were not part of the original success measures—for example, changes in workload, access, or safety. Record who benefits and who bears new costs instead of reporting only a project-wide total.
Separate reported experience from causal impact
Worker surveys can reveal how people experience a system, but favorable responses are not proof that AI caused a performance improvement or a positive return on investment. In its 2023 publication Using AI in the workplace, the OECD reported that four in five workers surveyed said AI improved their performance at work, and three in five said it increased their enjoyment of work. The publication result does not state the survey fieldwork year. These figures describe workers’ responses, not causal estimates or universal outcomes.
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In reporting results, label what was directly observed, what was estimated as a causal effect, and what came from people’s perceptions. State the time period, comparison used, and important limitations so readers can distinguish evidence of change from evidence about its cause.
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