Artificial intelligence can help people process information, assist with selected tasks, and support scientific discovery. Its benefits are clearest when a system is well matched to a task, uses suitable data, and operates with human oversight. Adoption figures and positive user reports are useful signals, but they do not prove that AI improves every job, service, or outcome.
How AI can create benefits
AI systems can recognize patterns, summarize or generate information, make predictions, and help people explore large datasets. In practice, these capabilities may save time on routine work, provide decision support, or help researchers investigate problems. The useful question is not whether AI is beneficial in general, but whether a particular system improves a particular task for the people affected by it.
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Evidence also comes in different forms. A survey records what organizations or workers report; a benchmark measures performance on a defined test; a device authorization permits a product to be marketed under applicable rules. None alone establishes that AI causes better results in every real-world setting.
Work and productivity: assistance, not an automatic upgrade
AI is used in areas such as service operations, supply-chain management, software engineering, marketing, and sales. Stanford HAI’s 2025 AI Index reports that 78% of surveyed organizations used AI in 2024, up from 55% in 2023. Generative AI use in at least one business function was reported by 71% of surveyed respondents in 2024, compared with 33% in 2023. These are adoption figures, not measures of productivity or proof of a causal effect.
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Among respondents whose organizations used AI, 49% in service operations reported cost savings, compared with 43% in supply-chain management and 41% in software engineering. Those percentages are the shares reporting some savings, not the amount saved by a typical company. Among respondents who reported savings, most estimated that the savings were below 10%; among those reporting revenue gains, the most common increase was below 5%.
Workers’ own assessments are positive but should be read as perceptions. In OECD surveys, four in five workers said AI improved their performance at work, and three in five said it increased their enjoyment of work. The OECD emphasizes that workplace AI can bring opportunities as well as risks, including impacts on job quality and worker rights (OECD, “Using AI in the workplace”, 15 March 2024). A tool that speeds up one task can still create new checking work, change job demands, or perform poorly when conditions differ from its intended use.
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Where workplace gains are most plausible
- Repetitive information tasks: drafting, classification, summarization, or searching can be assisted when a person can review the result.
- Decision support: pattern-finding can help staff prioritize cases or identify information to investigate, without transferring accountability to the model.
- Complex operations: predictions may inform planning in areas such as supply chains, but value depends on reliable data and whether teams can act on the forecast.
Health and science: promising uses with different levels of evidence
Health applications include screening and diagnosis support, clinical care, health research and drug development, public-health interventions, disease surveillance, outbreak response, and health-system management. The World Health Organization (WHO) says AI could also help rural and resource-poor settings bridge access gaps, while warning that potential benefits should not be overstated or used to displace essential investments in health systems. Its 2021 announcement summarized the balance: “Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm.” The statement was made by WHO Director-General Dr Tedros Adhanom Ghebreyesus (WHO, 28 June 2021).
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Stanford HAI’s 2025 AI Index reports that the number of FDA-authorized AI-enabled medical devices reached 223 by 2023, compared with six by 2015. The count shows growth in authorized devices; it does not establish that every device improves patient outcomes in routine care. Likewise, stronger performance on clinical-knowledge benchmarks or promising scientific discoveries can support further investigation without proving better care or treatment results.
What the evidence can—and cannot—show
- Authorization counts describe regulatory status, not comparative clinical effectiveness for every patient or setting.
- Benchmark scores show performance on specified tests, not necessarily reliability in clinical workflows.
- Research and discovery applications can help explore possibilities, but findings still need scientific validation and translation into practice.
WHO identifies risks including inappropriate collection or use of health data, bias encoded in systems, patient-safety failures, cybersecurity threats, and environmental concerns. Health systems need safeguards, clear accountability, and human judgment alongside the technology.
Education: useful support, uneven readiness
Students most commonly use generative AI for research, essay editing, and brainstorming, according to Stanford HAI’s 2026 AI Index education chapter. These uses may help learners get started, explore explanations, or revise writing, but generated material needs checking and should not replace learning or appropriate attribution.
The OECD identifies personalized tutoring, lower barriers to knowledge, and tools that help teachers create tailored materials as potential benefits—not guaranteed outcomes. Its 2024 assessment notes that adoption is slow and that earlier education technologies have not always delivered on their promise (OECD, “Assessing potential future artificial intelligence risks, benefits and policy imperatives”, 14 November 2024). Stanford HAI’s 2025 education chapter also highlights gaps in teacher preparedness (2025 AI Index education chapter). A tool being available to students or schools does not mean educators have the training, time, or policies to use it well.
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AI can help researchers analyze data, identify patterns, and test ideas; it may also improve forecasting and support work on complex challenges. The OECD’s 2024 report identifies ten priority potential benefits, including “accelerated scientific progress, productivity gains and better sense-making and forecasting.” These are prospective benefits, not settled results across every field.
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Any wider social gains depend on who can access the tools and who bears their costs or errors. The OECD also identifies risks including cyberattacks, manipulation, disinformation, fraud, concentration of power, incidents in critical systems, and exacerbated inequality or poverty. AI can contribute to problem-solving, but it is not a substitute for sound institutions, expertise, or investment in the underlying services.
How to judge whether an AI benefit is real
Before relying on a claimed benefit, assess the specific use rather than treating AI as one uniform technology:
- Evidence maturity: Is the claim a forecast, benchmark result, survey report, or demonstrated real-world outcome?
- Task fit: Does the system address a well-defined task, and are its inputs and operating conditions appropriate?
- Size and distribution: How large is the benefit, who receives it, and who may be excluded or disadvantaged?
- Cost of error: What happens when the output is wrong, and can a person detect and correct mistakes?
- Data and security: Are data handled lawfully and securely, with appropriate privacy protections?
- Oversight and accountability: Who reviews decisions and remains responsible for them?
- Access and wider costs: Are the system’s costs, infrastructure needs, and environmental impacts acceptable?
These checks help distinguish a useful assistant from a system whose risks, limitations, or operating costs outweigh the task it is meant to improve.
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